Segmentation of cardiac anatomical structures in cardiac magnetic resonance images (CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases. To increase robustness and performance of segmentation methods this study combines automatic segmentation and assessment of segmentation uncertainty in CMRI to detect image regions containing local segmentation failures. Three existing state-of-the-art convolutional neural networks (CNN) were trained to automatically segment cardiac anatomical structures and obtain two measures of predictive uncertainty: entropy and a measure derived by MC-dropout. Thereafter, using the uncertainties another CNN was trained to detect local segmentation failures that potentially need correction by an expert. Finally, manual correction of the detected regions was simulated in the complete set of scans of 100 patients and manually performed in a random subset of scans of 50 patients. Using publicly available CMR scans from the MICCAI 2017 ACDC challenge, the impact of CNN architecture and loss function for segmentation, and the uncertainty measure was investigated. Performance was evaluated using the Dice coefficient, 3D Hausdorff distance and clinical metrics between manual and (corrected) automatic segmentation. The experiments reveal that combining automatic segmentation with manual correction of detected segmentation failures results in improved segmentation and to 10-fold reduction of expert time compared to manual expert segmentation. Cardiac magnetic resonance imaging (CMRI) is currently considered the reference standard for quantification of ventricular volumes, mass and function2. Short-axis CMR imaging, covering the entire left and right ventricle (LV resp. RV) is routinely used to determine quantitative parameters of both ventricle’s function. This requires manual or semi-automatic segmentation of corresponding cardiac tissue structures for end-diastole (ED) and end-systole (ES).To perform diagnosis and prognosis of cardiovascular disease (CVD) medical experts depend on the reliable quantification of cardiac functionExisting semi-automated or automated segmentation methods for CMRIs regularly require  manual intervention caused by lack of robustness. Manual or semi-automatic segmentation across a complete cardiac cycle, comprising 20 to 40 phases per patient, enables computation of parameters quantifying cardiac motion with potential diagnostic implications but due to the required workload, this is practically infeasible. Consequently, segmentation is often performed at end-diastole and end-systole precluding comprehensive analysis over complete cardiac cycle.4, deep learning segmentation methods have shown to outperform traditional approaches such as those exploiting level set, graph-cuts, deformable models, cardiac atlases and statistical models6. However, recent comparison of a number of automatic methods showed that even the best performing methods generated anatomically implausible segmentations in more than 80% of the CMRIs7. Such errors do not occur when experts perform segmentation. To achieve acceptance in clinical practice these shortcomings of the automatic approaches need to be alleviated by further development. This can be achieved by generating more accurate segmentation result or by development of approaches that automatically detect segmentation failures.Recently8. To increase segmentation performance, several methods have been proposed12. Tan et al.9 used a convolutional neural network (CNN) to regress anatomical landmarks from long-axis views . They exploited the landmarks to determine most basal and apical slices in short-axis views and thereby constraining the automatic segmentation of CMRIs. This resulted in increased robustness and performance. Other approaches leverage spatial10 or temporal12 information to increase segmentation consistency and performance in particular in the difficult basal and apical slices.In manual and automatic segmentation of short-axis CMRI, largest segmentation inaccuracies are typically located in the most basal and apical slices due to low tissue contrast ratios13 developed an anatomically constrained neural network (NN) that infers shape constraints using an auto-encoder during segmentation training. Duan et al.14 developed a deep learning segmentation approach for CMRIs that used atlas propagation to explicitly impose a shape refinement. This was especially beneficial in the presence of image acquisition artifacts. Recently, Painchaud et al.15 developed a post-processing approach to detect and transform anatomically implausible cardiac segmentations into valid ones by defining cardiac anatomical metrics. Applying their approach to various state-of-the-art segmentation methods the authors showed that the proposed method provides strong anatomical guarantees without hampering segmentation accuracy.An alternative approach to preventing implausible segmentation results is by incorporating knowledge about the highly constrained shape of the heart. Oktay et al.A different research trend focuses on detecting segmentation failures, i.e. on automated quality control for image segmentation. These methods can be divided in those that predict segmentation quality using image at hand or corresponding automatic segmentation result, and those that assess and exploit predictive uncertainties to detect segmentation failure.17. Robinson et al.17 using the approach of Reverse Classification Accuracy (RCA)18 predicted CMRI segmentation metrics to detect failed segmentations. They achieved good agreement between predicted metrics and visual quality control scores. Alba et al.16 used statistical, pattern and fractal descriptors in a random forest classifier to directly detect segmentation contour failures without intermediate regression of segmentation accuracy metrics.Recently, two methods were proposed to detect segmentation failures in large-scale cardiac MR imaging studies to remove these from subsequent analysis19 extracted features from the segmentation results of the left ventricle in CT scans. Using the obtained features the authors trained a classifier that is able to discriminate between consistent and inconsistent segmentations. To distinguish between acceptable and non-acceptable segmentations Kohlberger et al.20 proposed to directly predict multi-organ segmentation accuracy in CT scans using a set of features extracted from the image and corresponding segmentation.Methods for automatic quality control were also developed for other applications in medical image analysis. Frounchi et al.21 computed an overall score for target segmentation structure from voxel-wise predictive uncertainties. The method was tested for detection of Multiple Sclerosis in brain MRI. The authors showed that rejecting segmentations with high uncertainty scores led to increased detection accuracy indicating that correct segmentations contain lower uncertainties than incorrect ones. Similarly, to assess segmentation quality of brain MRIs Jungo et al.22 aggregated voxel-wise uncertainties into a score per target structure and showed that the computed uncertainty score enabled identification of erroneous segmentations.A number of methods aggregate voxel-wise uncertainties into an overall score to identify insufficiently accurate segmentations. For example, Nair et al.24. For example, Roy et al.23 aggregated voxel-wise uncertainties into four scores per segmented structure in brain MRI. The authors showed that computed scores can be used to predict the Intersection over Union and hence, to determine segmentation accuracy. Similar idea was presented by DeVries et al.24 that predicted segmentation accuracy per patient using an auxiliary neural network that leverages the dermoscopic image, automatic segmentation result and obtained uncertainties. The researchers showed that a predicted segmentation accuracy is useful for quality control.Unlike approaches evaluating segmentation directly, several methods use predictive uncertainties to predict segmentation metrics and thereby evaluate segmentation performance25. For the first measure the multi-class entropy per voxel (entropy maps) was computed using the output distribution. For the second measure Bayesian uncertainty maps were acquired using Monte Carlo dropout (MC-dropout)26. In25 we showed that the obtained uncertainties almost entirely cover the regions of incorrect segmentation i.e. that uncertainties are calibrated. In the current work we extend our preliminary research in two ways. First, we assess impact of CNN architecture on the segmentation performance and calibration of uncertainty maps by evaluating three existing state-of-the-art CNNs. Second, we employ an auxiliary CNN (detection network) that processes a cardiac MRI and corresponding spatial uncertainty map (Entropy or Bayesian) to automatically detect segmentation failures. We differentiate errors that may be within the range of inter-observer variability and hence do not necessarily require correction (tolerated errors) from the errors that an expert would not make and hence require correction (segmentation failures). Given that overlap measures do not capture fine details of the segmentation results and preclude us to differentiate two types of segmentation errors, in this work, we define segmentation failure using a metric of boundary distance. In25 we found that degree of calibration of uncertainty maps is dependent on the loss function used to train the CNN. Nevertheless, in the current work we show that uncalibrated uncertainty maps are useful to detect local segmentation failures. In contrast to previous methods that detect segmentation failure per-patient or per-structure24, we propose to detect segmentation failures per image region. We expect that inspection and correction of segmentation failures using image regions rather than individual voxels or images would simplify correction process. To show the potential of our approach and demonstrate that combining automatic segmentation with manual correction of the detected segmentation failures per region results in higher segmentation performance we performed two additional experiments. In the first experiment, correction of detected segmentation failures was simulated in the complete data set. In the second experiment, correction was performed by an expert in a subset of images. Using publicly available set of CMR scans from MICCAI 2017 ACDC challenge7, the performance was evaluated before and after simulating the correction of detected segmentation failures as well as after manual expert correction.We build on our preliminary work where automatic segmentation of CMR images using a dilated CNN was combined with assessment of two measures of segmentation uncertainties7 was used. The dataset consists of cardiac cine MR images (CMRIs) from 100 patients uniformly distributed over normal cardiac function and four disease groups: dilated cardiomyopathy, hypertrophic cardiomyopathy, heart failure with infarction, and right ventricular abnormality. Detailed acquisition protocol is described by Bernard et al.7. Briefly, short-axis CMRIs were acquired with two MRI scanners of different magnetic strengths (1.5 and 3.0 T). Images were made during breath hold using a conventional steady-state free precession (SSFP) sequence. CMRIs have an in-plane resolution ranging from 1.37 to 1.68 mm To investigate uncertainty of the segmentation, anatomical structures in CMR images are segmented using a CNN. To investigate whether the approach generalizes to different segmentation networks, three state-of-the-art CNNs were evaluated. For each segmentation model two measures of predictive uncertainty were obtained per voxel. Thereafter, to detect and correct local segmentation failures an auxiliary CNN (detection network) that analyzes a cardiac MRI was used. Finally, this leads to the uncertainty map allowing detection of image regions that contain segmentation failures. Fig. Monte Carlo dropout (MC-dropout) introduced by Gal and Ghahramani26 is implemented in every network. The following three segmentation networks were evaluated: Bayesian Dilated CNN, Bayesian Dilated Residual Network, Bayesian U-net.To perform segmentation of LV, RV, and LVM in cardiac MR images i.e. 2D CMR scans, three state-of-the-art CNNs are trained. Each of the three networks takes a CMR image as input and has four output channels providing probabilities for the three cardiac structures  and background. Softmax probabilities are calculated over the four tissue classes. Patient volumes at ED and ES are processed separately. During inference the 2D automatic segmentation masks are stacked into a 3D volume per patient and cardiac phase. After segmentation, the largest 3D connected component for each class is retained and volumes are resampled to their original voxel resolution. Segmentation networks differ substantially regarding architecture, number of parameters and receptive field size. To assess predictive uncertainties from the segmentation models 2. All trainable layers except the final layer use rectified linear activation functions (ReLU). To enhance generalization performance, the model uses batch normalization in layers 2 to 9. In order to convert the original DN27 into a Bayesian DN dropout is added as the last operation in all but the final layer and 10 percent of a layer’s hidden units are randomly switched off.The Bayesian DN architecture comprises a sequence of ten convolutional layers. Layers 1 to 8 serve as feature extraction layers with small convolution kernels of size 328 for image segmentation. More specifically, the DRN-D-2228 is used which consists of a feature extraction module with output stride eight followed by a classifier implemented as fully convolutional layer with 128 into a Bayesian DRN.The Bayesian DRN is based on the original DRN from Yu et al.29 is used. The network is fully convolutional and consists of a contracting, bottleneck and expanding path. The contracting and expanding path each consist of four blocks i.e. resolution levels which are connected by skip connections. The first block of the contracting path contains two convolutional layers using a kernel size of 3The standard architecture of the U-netH and W denote the height and width of the original CMRI, respectively.To detect failures in segmentation masks generated by CNNs in testing, spatial uncertainty maps of the obtained segmentations are generated. For each voxel in the image two measures of uncertainty are calculated. First, a computationally cheap and straightforward measure of uncertainty is the entropy of softmax probabilities over the four tissue classes which are generated by the segmentation networks. Using these, normalized entropy maps T per voxel are obtained. As an overall measure of uncertainty the mean standard deviation of softmax probabilities per voxel over all tissue classes C is computedx, y) in 2D slice I, C is equal to the number of classes, T is the number of samples and x, y) in image I for class c. The predictive mean per class Second, by applying MC-dropout in testing, softmax probabilities with a number of samples Risk-coverage curves introduced by Geifman et al.30 visualize whether incorrectly segmented voxels are covered by higher uncertainties than those that are correctly segmented. Risk-coverage curves convey the effect of avoiding segmentation of voxels above a specific uncertainty value on the reduction of segmentation errors (i.e. risk reduction) while at the same time quantifying the voxels that were omitted from the classification task (i.e. coverage).Ideally, incorrectly segmented voxels as defined by the reference labels should be covered by higher uncertainties than correctly segmented voxels. In such a case the spatial uncertainty maps are perfectly calibrated. To generate risk-coverage curves first, each patient volume is cropped based on a minimal enclosing parallelepiped bounding box that is placed around the reference segmentations to reduce the number of background voxels. Note that this is only performed to simplify the analysis of the risk-coverage curves. Second, voxels of the cropped patient volume are ranked based on their uncertainty value in descending order. Third, to obtain uncertainty threshold values per patient volume the ranked voxels are partitioned into 100 percentiles based on their uncertainty value. Finally, per patient volume each uncertainty threshold is evaluated by computing a coverage and a risk measure. Coverage is the percentage of voxels in a patient volume at ED or ES that is automatically segmented. Voxels in a patient volume above the threshold are discarded from automatic segmentation and would be referred to an expert. The number of incorrectly segmented voxels per patient volume is used as a measure of risk. Using bilinear interpolation risk measures are computed per patient volume between  percent.To detect segmentation failures uncertainty maps are used but direct application of uncertainties is infeasible because many correctly segmented voxels, such as those close to anatomical structure boundaries, have high uncertainty. Hence, an additional patch-based CNN (detection network) is used that takes a cardiac MR image together with the corresponding spatial uncertainty map as input. For each patch of 8 31 consisting of a feature extraction module with output stride eight followed by a classifier indicating the presence of segmentation failure. The first level of the feature extraction module consists of two convolutional layers. The first layer uses 16 kernels of 7 The detection network is a shallow Residual Network (S-ResNet)7 for each of the clinical metrics three performance indices were computed using the measurements based on manual and (corrected) automatic segmentation: Pearson correlation coefficient; mean difference (bias and standard deviation); and mean absolute error (MAE).Automatic segmentation performance, as well as performance after simulating the correction of detected segmentation failures and after manual expert correction was evaluated. For this, the 3D Dice-coefficient (DC) and 3D Hausdorff distance (HD) between manual and (corrected) automatic segmentation were computed. Furthermore, the following clinical metrics were computed for manual and (corrected) automatic segmentation: left ventricle end-diastolic volume (EDV); left ventricle ejection fraction (EF); right ventricle EDV; right ventricle ejection fraction; and left ventricle myocardial mass. Following Bernard et al.To evaluate detection performance of the automatic method precision-recall curves of identification of slices that require correction were computed. A slice is considered positive in case it consists of at least one image region with a segmentation failure. To achieve accurate segmentation in clinic, identification of slices that contain segmentation failures might ease manual correction of automatic segmentations in daily practice. To further evaluate detection performance detection rate of segmentation failures was assessed on a voxel level. More specific, sensitivity against the number of false positive regions was evaluated because manual correction is presumed to be performed at this level.Finally, after simulation and manual correction of the automatically detected segmentation failures, segmentation was re-evaluated and significance of the difference between the DCs, HDs and clinical metrics was tested with a Mann-Whitney U test.To use stratified four-fold cross-validation the dataset was split into training (75%) and test (25%) set. The splitting was done on a patient level, so there was no overlap in patient data between training and test sets. Furthermore, patients were randomly chosen from each of the five patient groups w.r.t. disease. Each patient has one volume for ED and ES time points, respectively.33 (SD); cross-entropy (CE); and Brier loss34. However, for the evaluation of the combined segmentation and detection approach for each model architecture the two best performing loss functions were chosen: soft-Dice for all models; cross-entropy for DRN and U-net and Brier loss for DN. For completeness, we provide the equations for all three used loss functions.N denotes the number of voxels in an image, c and c.N denotes the number of voxels in an image and p denotes the probability for a specific voxel c.DRN and U-net were trained with a patch size of 128 25.Choosing Brier loss to train the DN model instead of CE was motivated by our preliminary work which showed that segmentation performance of DN model was best when trained with Brier loss35, where after every 10,000 iterations the learning rate was reset to its original value of 0.02.All models were trained for 100,000 iterations. DRN and U-net were trained with a learning rate of 0.001 which decayed with a factor of 0.1 after every 25,000 steps. Training DN used the snapshot ensemble technique36. Furthermore, models were regularized with weight decay to increase generalization performance.All three segmentation networks were trained using mini-batch stochastic gradient descent using a batch size of 16. Network parameters were optimized using the Adam optimizer7. Finally, segmentation errors located in slices above the base or below the apex are always included in the set of segmentation failures.To train the detection model a subset of the errors performed by the segmentation model is used. Segmentation errors that presumably are within the range of inter-observer variability and therefore do not inevitably require correction (tolerated errors) are excluded from the set of errors that need to be detected and corrected (segmentation failures). To distinguish between tolerated errors and the set of segmentation failures M denotes the number of patches in a slice I.Using the set The detection network is trained by minimizing a weighted binary cross-entropy loss:Each fold was trained using spatial uncertainty maps and automatic segmentation masks generated while training the segmentation networks. Hence, there was no overlap in patient data between training and test set across segmentation and detection tasks. In total 12 detection models were trained and evaluated resulting from the different combination of 3 model architectures , 2 loss functions  and 2 uncertainty maps .The patches used to train the network were selected randomly  model architecture and loss function on detection performance is slightly stronger when e-maps were used as input for the detection task compared to b-maps. Detection rates are consistently lower when segmentation failures originate from segmentation models trained with soft-Dice loss compared to models trained with CE or Brier loss. Overall, detection rates are higher when b-maps were exploited for the detection task compared to e-maps.To evaluate detection performance w.r.t. slices containing segmentation failures precision-recall curves for each combination of model architecture and loss function using e-maps Fig. b, left oFigure The results listed in Tables Results in terms of clinical metrics shown in Table In general, the effect of correction of detected segmentation failures is more pronounced in cases where the segmentation-only approach achieved relatively low accuracy (e.g. DN-SD for RV at ES). Furthermore, performance gains are largest for RV and LV at ES and for ejection fraction of both ventricles.The best overall performance is achieved by the DRN model trained with cross-entropy loss while exploiting entropy maps in the detection task. Moreover, the proposed two step approach attained slightly better results using Bayesian maps compared to entropy maps.manual correction of detected segmentation failures by an expert. The results show that this correction led to improved segmentation performance in terms of DC, HD and clinical metrics. Improvements in terms of HD are in 50 percent of the cases statistically significant  samples on the segmentation performance validation experiments were performed for all three segmentation architectures  using The combined segmentation and detection approach detects segmentation failures on region level. To investigate the effect of patch-size on detection performance three different patch-sizes were evaluated: 4 To investigate the impact of the tolerance threshold separating segmentation failures and tolerable segmentation errors, we calculated the ratio of the number of segmentation failures and all errors i.e. the sum of tolerable errors and segmentation failures. Fig. We have described a method that combines automatic segmentation and assessment of uncertainty in cardiac MRI with detection of image regions containing segmentation failures. The results show that combining automatic segmentation with manual correction of detected segmentation failures results in higher segmentation performance. In contrast to previous methods that detected segmentation failures per patient or per structure, we showed that it is feasible to detect segmentation failures per image region. In most of the experimental settings, simulated manual correction of detected segmentation failures for LV, RV and LVM at ED and ES led to statistically significant improvements. These results represent the upper bound on the maximum achievable performance for the manual expert correction task. Furthermore, results show that manual expert correction of detected segmentation failures led to consistently improved segmentations. However, these results are not on par with the simulated expert correction scenario. This is not surprising because inter-observer variability is high for the presented task and annotation protocols may differ between clinical environments. Moreover, qualitative results of the manual expert correction reveal that manual correction of the detected segmentation failures can prevent anatomically implausible segmentations see Fig. . TherefoThe proposed combined segmentation and detection approach was evaluated using three state-of-the-art deep learning segmentation architectures. The results suggest that our approach is generic and applicable to different model architectures. Nevertheless, we observe noticeable differences between the different combination of model architectures, loss functions and uncertainty measures. In the segmentation-only task the DRN clearly outperforms the other two models in the evaluation of the boundary of the segmented structure. Moreover, qualitative analysis of the automatic segmentation masks suggests that DRN generates less often anatomically implausible and fragmented segmentations than the other models. We assume that clinical experts would prefer such segmentations although they are not always perfect. Furthermore, even though DRN and U-net achieve similar performance in regard to DC we assume that less fragmented segmentation masks would increase trustworthiness of the methods.25. Nevertheless, the results of the combined segmentation and detection approach showed that a lower degree of uncertainty calibration only slightly deteriorated the detection performance of segmentation failures for the larger segmentation models (DRN and U-net) when exploiting uncertainty information from e-maps. Hendrycks and Gimpel37 showed that softmax probabilities generated by deep learning networks have poor direct correspondence to confidence. However, in agreement with Geifman et al.30 we presume that probabilities and hence corresponding entropies obtained from softmax function are ranked consistently i.e. entropy can potentially be used as a relative uncertainty measure in deep learning. In addition, we detect segmentation failures per image region and therefore, our approach does not require perfectly calibrated uncertainty maps. Furthermore, results of the combined segmentation and detection approach revealed that detection performance of segmentation failures using b-maps is almost independent of the loss function used to train the segmentation model. In line with Jungo et al.38 we assume that by enabling MC-dropout in testing and computing the mean softmax probabilities per class leads to better calibrated probabilities and b-maps. This assumption is in agreement with Srivastava et al.39 where a CNN with dropout used at testing is interpreted as an ensemble of models.In agreement with our preliminary work we found that uncertainty maps obtained from a segmentation model trained with soft-Dice loss have a lower degree of uncertainty calibration compared to when trained with one of the other two loss functions (cross-entropy and Brier)7 and standard clinical measures are not sensitive to small segmentation errors. Nevertheless, errors of the current state-of-the-art automatic segmentation methods may lead to anatomically implausible segmentations7 that may cause distrust in clinical application. Besides increase in trustworthiness of current state-of-the-art segmentation methods for cardiac MRIs, improved segmentations are a prerequisite for advanced functional analysis of the heart e.g. motion analysis40 and very detailed morphology analysis such as myocardial trabeculae in adults41.Quantitative evaluation in terms of Dice coefficient and Hausdorff distance reveals that proposed combined segmentation and detection approach leads to significant performance increase. However, the results also demonstrate that the correction of the detected failures allowed by the combined approach does not lead to statistically significant improvement in clinical metrics. This is not surprising because state-of-the-art automatic segmentation methods are not expected to lead to large volumetric errors7 reported inter-observer variability ranging from 4 to For the ACDC dataset used in this manuscript, Bernard et al.The presented patch-based detection approach combined with (simulated) manual correction can in principle lead to stitching artefacts in the resulting segmentation masks. A voxel-based detection approach could potentially solve this. However, voxel-based detection methods are more challenging to train due to the very small number of voxels in an image belonging to the set of segmentation failures.42 or variational dropout43. In addition, previous work by Kendall and Gal44, Tanno et al.45 has shown that the quality of uncertainty estimates can be improved if model (epistemic) and data  uncertainty are assessed simultaneously with separate measures. The current study focused on the assessment of model uncertainty by means of MC-dropout and entropy which is a combination of epistemic and aleatoric uncertainty. Hence, future work could investigate whether additional estimation of aleatoric uncertainty improves the detection of segmentation failures.Evaluation of the proposed approach for 12 possible combinations of segmentation models (three), loss functions (two) and uncertainty maps (two) resulted in an extensive number of experiments. Nevertheless, future work could extend evaluation to other segmentation models, loss functions or combination of losses. Furthermore, our approach could be evaluated using additional uncertainty estimation techniques e.g. by means of ensembling of networksFurthermore, to develop an end-to-end approach future work could incorporate the detection of segmentation failures into the segmentation network. Besides, adding the automatic segmentations to the input of the detection network could increase the detection performance.Finally, the proposed approach is not specific to cardiac MRI segmentation. Although data and task specific training would be needed the approach could potentially be applied to other image modalities and segmentation tasks.A method combining automatic segmentation and assessment of segmentation uncertainty in cardiac MR with detection of image regions containing local segmentation failures has been presented. The combined approach, together with simulated and manual correction of detected segmentation failures, increases performance compared to segmentation-only. The proposed method has the potential to increase trustworthiness of current state-of-the-art segmentation methods for cardiac MRIs.


Despite advances in acute management and prevention of cerebrovascular disease, stroke and vascular cognitive impairment together remain the world’s leading cause of death and neurological disability. Hypertension and its consequences are associated with over 50% of ischaemic and 70% of haemorrhagic strokes but despite good control of blood pressure there remains a 10% risk of recurrent cerebrovascular events and there is no proven strategy to prevent vascular cognitive impairment.Hypertension evolves over the life-span, from predominant sympathetically-driven hypertension with elevated mean blood pressure (BP) in early and mid-life to a late-life phenotype of increasing systolic and falling diastolic pressures, associated with increased arterial stiffness and aortic pulsatility. This pattern may partially explain both the increasing incidence of stroke in younger adults as well as late-onset, chronic cerebrovascular injury associated with concurrent systolic hypertension and historic midlife diastolic hypertension. With increasing arterial stiffness and autonomic dysfunction, blood pressure variability increases, independently predicting the risk of ischaemic and intracerebral haemorrhage, and is potentially modifiable beyond control of mean blood pressure.However, the interaction between hypertension and control of cerebral blood flow remains poorly understood. Cerebral small vessel disease is associated with increased pulsatility in large cerebral vessels and reduced reactivity to carbon dioxide, both of which are being targeted in early-phase clinical trials. Cerebral arterial pulsatility is mainly dependent upon increased transmission of aortic pulsatility via stiff vessels to the brain whilst cerebrovascular reactivity reflects endothelial dysfunction. In contrast, although cerebral autoregulation is critical to adapt cerebral tone to blood pressure fluctuations to maintain cerebral blood flow, its role as a modifiable risk factor for cerebrovascular disease is uncertain.New insights into hypertension-associated cerebrovascular pathophysiology may provide key targets to prevent chronic cerebrovascular disease, acute events and vascular cognitive impairment. These aetiologies depend upon intermediate disease phenotypes which can be treated, such as carotid endarterectomy for atherosclerosis16 or anticoagulation for AF.17 However, the intracranial physiological effects of hypertension are less well understood even though they may explain some of the residual risk of recurrent stroke despite effective BP lowering. In the PROGRESS study, a 12mmHg reduction in blood pressure with combination treatment reduced recurrent stroke by up to 43%,18 whilst any treatment reduced intracerebral haemorrhage by 50%, with similar efficacy in all secondary prevention trials,19 but there was still a 10% risk of recurrent events. In the multi-centre TIARegistry, there was a 14.3% risk of recurrent major vascular events (MACE) at 5 years with a 9.5% risk of recurrent stroke,20 despite high-quality secondary prevention.Hypertension is associated with all ischaemic stroke subtypes, whether cardioembolic stroke related to atrial fibrillation (AF),9 These represent further intermediate phenotypes linking hypertension with cerebrovascular outcomes, and are responsible for 30% of ischaemic strokes, 80% of intracerebral haemorrhage22 and 45% of dementia, as well as late-onset refractory depression and significant global functional impairment at older ages.24 SVD is evident on brain imaging in >50% of people over 65 years of age and nearly all people over 80 years of age25. However, no specific treatments exist to target its progression.The principal manifestation of chronic cerebrovascular injury is cerebral small vessel disease. Originally defined by pathological changes including vessel lipohyalinosis, fibrinoid necrosis and vessel rarefaction, cSVD is now primarily defined by imaging characteristics on MRI  for bothAny original data that support the conclusions of this study are available from the authors upon reasonable request.26 Randomised controlled trials then confirmed that a reduction in mean blood pressure reduced the risk of recurrent events by the predicted degree,7 irrespective of baseline cerebrovascular risk18 and at minimally increased baseline blood pressure levels.28 However, hypertension research for primary prevention has lacked a nuanced focus on stroke, with limited stroke phenotyping in large randomized controlled trials (RCTs), whilst stroke research has lacked a nuanced understanding of hypertensive physiology and its effect on the cerebral circulation.Derivation of the statistical measure ‘usual’ blood pressure in the early 1990s allowed precise estimation of the blood pressure related vascular risk by adjusting for variation between clinic visits, to remove the resulting bias that caused underestimation of the effect of average blood pressure (‘regression dilution bias’).10 As preventative strategies including antihypertensive treatment and reduced salt intake have become more effective the incidence of stroke has reduced in older age groups.29 However, due to the aging population there is a marked overall increase in the prevalence of stroke and vascular cognitive impairment. Furthermore, in younger patients under 50 years, particularly women, age-standardised stroke incidence is increasing, associated with increasing rates of obesity and hypertension.31At all ages, elevated mean blood pressure is associated with an increased risk of stroke.32In late life, stroke, SVD and vascular cognitive impairment reflect the cumulative cerebrovascular effect of hypertension over the whole life-span . Althoug25 driven by aging and hypertension and with a strong, genetic contribution.33 White matter hyperintensities (WMH) and concurrent systolic blood pressure are strongly associated at all ages,34 but there is a stronger relationship with midlife blood pressures,36 whilst the strength of the relationship increases the earlier in life the blood pressure is measured.37 Early microstructural brain imaging changes on diffusion tensor imaging in midlife participants in the UK Biobank study are cross-sectionally associated predominantly with mean arterial pressure, independent of age, whilst the association with pulse pressure attenuates significantly after adjustment for age. However, this interaction is synergistic such that increased pulse pressure accentuates the age-related deterioration in cerebral microstructure.36Similarly, SVD is uncommon before 55 years of age but then increases at an increasing rate,38 and acute ischaemic stroke due to small vessel occlusion,39 implying a potentially causative interaction between increasing blood pressure levels over a timescale of a year or less, and both rupture or occlusion of small cerebral vessels. This relationship is particularly strong for stroke subtypes most closely related to hypertension, including deep parenchymal vs lobar haemorrhage and lacunar versus non-lacunar stroke, and provides a strong rationale for early, consistent blood pressure control, particularly after intracerebral haemorrhage, as is being tested with telemetric home monitoring in PROHIBIT-ICH (NCT03863665).In contrast to the long-term association with chronic SVD, recent blood pressure elevations are more strongly associated with both acute intracerebral haemorrhage40 However, there were age-dependent differences between genetically determined drivers of mean versus pulsatile blood pressure. Both were associated with late-life large artery athero-thrombo-embolism, but elevated mean pressure was dominant across ages for other aetiological subtypes.41 Similar analyses have confirmed likely causative relationships between hypertension and the risk of incident all-cause dementia.42Mendelian randomisation has also demonstrated an age-dependent evolution of the relationship between blood pressure and cerebrovascular outcomes, with a likely causative association between high blood pressure and all stroke subtypes, except for lobar intracerebral haemorrhage.43 but vascular risk factors remain dominant after childhood.44 In particular, overactivity of the sympathetic nervous system is a key driver of hypertension, with increased muscle sympathetic nerve activity (MSNA) associated with obesity and blood pressure.45 This implies a potential treatment target with early, unblinded trials of renal sympathetic denervation (RDN) suggesting large reductions in systolic blood pressure (up to 40mmHg). Unfortunately, the properly blinded, sham-controlled Symplicity HTN-346 study failed to replicate this. However, more recent trials ,47 with newer devices and 4-quadrant denervation, have suggested that a more rigorous interventional approach can achieve significant blood pressure reductions. However, the achieved BP reductions are equivalent only to the addition of one new drug, whilst the application of RDN to patients with stroke is yet to be tested.Early-life risk factors for late life cerebrovascular disease include low birth weight and pre-eclampsia,48Baroreceptor activation therapy is an alternative, whereby an implanted stimulator increases the parasympathetic:sympathetic balance, with a resulting reduction in blood pressure. Lagging behind RDN, uncontrolled studies have been prone to bias but a sustained blood pressure reduction was achieved in the sole RCT in resistant hypertension (RHEOS PIVOTAL).51 and its progression is hypertension-dependent, with a 20mmHg elevation of SBP associated with a 1.14m/s progression in PWV per decade.52 Furthermore, there are strong associations between aortic PWV and specific cerebrovascular phenotypes including acute lacunar stroke,53 deep intracerebral haemorrhage (by contrast with lobar),54 cerebral SVD and cognitive impairment 56, with a similar odds ratio of 1.25-1.3 per SD increase in PWV associated with an increase in severity of white matter hyperintensities on the Fazekas scale.Late-life hypertensive phenotypes are characterized by increasing arterial stiffness, with a reduction in elastin and increase in collagen in the aorta reducing vascular compliance and increasing speed of travel of the aortic pressure wave from the heart to the carotid and femoral arteries . This marker of ‘vascular aging’ has a hazard ratio of 1.54 per standard deviation increase for stroke and 1.42 for all vascular events,57 Following reflection, the ‘reverse’ travelling wave then returns towards the heart to meet the forward travelling wave before it has dissipated. With increasing arterial stiffness, there is thus greater ‘augmentation’ of the peak of the waveform, increasing the aortic systolic pressure. Aortic BP is even more strongly associated with the risk of recurrent events than brachial pressure,58 since low resistance vascular beds  are more directly exposed to aortic pressures. Thus, a greater reduction in aortic pressure with an amlodipine-based regimen compared to an atenolol-based regimen potentially explained differences in vascular outcomes in the ASCOT-BPLA trial.59Increased arterial stiffness is also associated with increased wave reflection, whereby the pressure-wave generated with each cardiac impulse ‘reflects’ from points of increased impedance, either at the junction of large and medium vessels or between medium arteries and arterioles.4 However, the statistical adjustment used to calculate ‘usual’ blood pressure depends upon adjusting for the variation in BP between assessments. The greater the variability in blood pressure (BPV) the greater the adjustment and the stronger the estimated relationship between mean blood pressure and subsequent risk. However, blood pressure is highly variable between clinic visits,60 and across five cohorts, the top decile of visit-to-visit BP variability was associated with 2.2-3.8 times the risk of recurrent stroke compared to the bottom decile.61 Variability was reduced in patients randomized to a calcium-channel blocker based regimen compared to an atenolol-based regimen, explaining the difference between treatment regimens that was not explained by mean blood pressure62 and consistent with effects of calcium channel blockers or thiazide-like diuretics in all large randomized controlled trials BPV and the risk of stroke.63Mean blood pressure level over the lifespan is the strongest predictor of cerebrovascular disease.64 on both visit-to-visit and home day-to-day variability,65 and worse outcome after both ischaemic66 and haemorrhagic stroke67 with increasing BPV. The association between BPV and cognitive impairment is greater than that for mean BP, although associations are highly heterogeneous between studies and may reflect reverse causation.68 Variability is predictive from visit-to-visit, day-to-day69 or beat-to-beat,70 is associated with arterial stiffness, baroreceptor dysfunction and increased reactivity to stress71 and progresses over the age of 5572 in parallel with progression of arterial stiffness and aortic pulsatility  by antagonism of the angiotensin-aldosterone system. Other potential targets include PPARy receptors, with reduced remodeling of large cerebral vessels in rat models under the influence of rosiglitazone or pioglitazone,77 with could explain the reduction in cerebrovascular events with pioglitazone in the IRIS trial.78Chronic hypertension is associated with characteristic changes in small vessels throughout the body. Shear stress promotes intracranial atherosclerosis and intracranial stenoses, whilst in smaller vessels it induces smooth muscle hypertrophy, reorganization (or disorganization) of smooth muscle cells, a reduction in the wall:lumen ratio, reduced vascular compliance and, over time, rarefaction of vessels. This increases intracranial cerebrovascular resistance, balancing increased perfusion pressures, but is associated with impaired resting perfusion is evident in the white matter of patients with severe cSVD.79 Furthermore, break down of the blood brain barrier (BBB) in cerebral small vessel disease is seen both in white matter hyperintensities and in areas of normal appearing white matter in patients with either lacunar stroke or established white matter hyperintensities elsewhere in the brain.80 BBB dysfunction may be induced directly by chronic hypertension as a potential mechanism for development of SVD or cognitive decline, either through haemodynamic effects or through the induction of oxidative stress and low-grade inflammation81 or through Amyloid beta-mediated pathways.82Endothelium derived nitric oxide (NO) is a key driver of vasodilatation, with reduced functional hyperaemia in older hypertensive patients, reduced vascular reactivity in cerebral small vessel disease and increased circulating markers of endothelial cell dysfunction in patients with cSVD and hypertension.39 Recurrent stroke was reduced in patients with lacunar events in the SPS3 trial, whilst haemorrhagic stroke was reduced by 63%. Moreover, intensive blood pressure lowering was associated with a reduction in progression of white matter hyperintensities , with a dose-response relationship between blood pressure reduction and rate of progression. However, there was also increased cerebral atrophy, and in the majority of these trials baseline white matter hyperintensities were not severe, limiting generalizability to patients with advanced cerebral SVD. However, reassuringly, there was no evidence of impairment of MRI-measured cerebral perfusion with intensive treatment in the more severely affected participants in the PRESERVE trial.83Cerebral small vessel disease is associated with 30% of ischaemic stroke, 80% of intracerebral haemorrhage and 45% of dementia.85 The pattern of distribution of microbleeds is indicative of the underlying pathology, with a strictly lobar distribution associated with cerebral amyloid angiopathy, reflecting deposition of β-amyloid in small cortical and leptomeningeal vessels. In contrast, deep microbleeds in the basal ganglia and brainstem are associated with hypertension, left ventricular hypertrophy, arterial stiffening and pulse pressure with a dose-response relationship between hypertension and severity of microbleeds.86 Although hypertension may trigger symptomatic lobar haemorrhage in CAA, there is a weaker overall association with hypertension, suggesting hypertension may aggravate CAA but not cause it. Studies to intensively control blood pressure after haemorrhage, either by intensive monitoring (PROHIBIT-ICH) or early triple, combination therapy (TRIDENT), may help to determine the effects of improved hypertension control.Cerebral microbleeds are the most prevalent haemorrhagic radiological manifestation of cerebral SVD. These areas of ‘blooming’ artefact most often reflect deposition of paramagnetic material, usually microscopic areas of subclinical blood extravasation (haemorrhage), though a range of lesions from haemosiderin deposition to intact erythrocytes to vessel wall calcification have been demonstrated.87 This distribution of SVD markers associated with hypertension reflects brain regions most exposed to intraarterial pressures and pulsatility,88 and vulnerability to these factors is at least partially genetically determined, with a heritability of basal ganglia PVS of up to 0.6 and significant shared heritability with white matter hyperintensities and shared genetic associations in large scale genome-wide association studies.89 This indicates a common hypertension-associated genetic predisposition across different manifestations of cSVD, and may also contribute to observed associations between PVS and both ischaemic90 and intracerebral haemorrhage.91Similar to cerebral microbleeds, there is a stronger association between hypertension and dilated perivascular spaces in the basal ganglia than with PVS in the centrum semiovale.92 or by high temporal-resolution multiband imaging.93 Cerebral pulsatility is strongly associated with severity of white matter hyperintensities,94 acute lacunar infarcts, cerebral microbleeds95 and dilated perivascular spaces.96 It increases with age and is tightly associated with aortic stiffness and aortic pulsatility,97 reflecting increased transmission of the aortic waveform to the brain, modified by dampening of the waveform at points of tortuosity and impedance and by the distal resistance of smaller cerebral vessels. It is highly reproducible, progresses over the age of 65 and independently predicts the risk of recurrent cerebrovascular events. However, methods to modify it independent of mean blood pressure are not clear. In the ECLIPSE study a phosphodiesterase 3 inhibitor (cilostazol)98 reduced cerebral pulsatility, potentially explaining beneficial effects of cilostazol in clinical trials beyond its known antiplatelet activity.11Cerebral vessels modify the blood pressure to maintain perfusion of the brain throughout the cardiac cycle , from beat-to-beat and hour-to-hour  and in response to endogenous and exogenous stimuli (via reactivity). Cerebral artery pulsatility is measured on transcranial ultrasound (Gosling’s pulsatility index), 4d-phase contrast MRI sequences,99 exiting via newly identified meningeal lymphatic vessels.100 This pathyway has been linked to drainage of amyloid β and other solutes. Despite limitations in experimental techniques101, 2-photon imaging tracer studies and ligation studies of the carotid102 indicate that arterial pulsatility provides motive force for perivascular drainage in healthy animals. However, it is unclear whether hypertension-associated increases in pulsatility and vascular stiffness affect perivascular drainage in humans, given a lack of in vivo glymphatic imaging techniques that do not require intrathecal injection of contrast.103Arterial pulsatility may also be key to drainage of interstitial fluid from the brain along perivascular spaces, sometimes called ‘glymphatics’. This recently identified pathway challenged the axiom that CSF reabsorption only happened via the arachnoid granulations, with intracisternal injection of radiolabeled tracers appearing in the CSF filled perivascular spaces around arteries, and then around veins,The endothelium controls the cerebrovascular response to stimuli, regulating smooth muscle tone via balanced vasodilator and vasoconstrictor pathways. The archetypal, eNOS vasodilator pathway induces smooth muscle cGMP upregulation and cell relaxation to increase local CBF, which antagonises vasoconstrictor pathways such as endothelin-mediated vasocontriction.104 CVR is an attractive therapeutic target, being simple to measure, modifiable by established medications, and with a biologically plausible relationship to disease. It is associated with severity of white matter hyperintensities, dilated perivascular spaces105 and microbleeds, and progresses in parallel with progression of SVD. Recent studies have also demonstrated impaired CVR in the basal ganglia in patients with deep intracerebral haemorrhages.106Cerebrovascular reactivity (CVR) is most commonly measured in vivo by altering carbon dioxide levels,  and measuring the vascular response .However, studies of the relationship between hypertension and CVR, independent of established cerebrovascular injury remain sparse with inconsistencies in methods of measurement of CVR, limited data in different cerebrovascular manifestations and uncertainty regarding the temporal order of the components of the phenotypes. This critical question may be answered by large collaborative studies such the MarkVCID and SVDs@TARGET consortia, and by interventional studies targeting improvements in CVR: TREAT-SVDs (NCT03082014), OxHARP (NCT03855332), LACI-2 (NCT03451591).Cerebral autoregulation maintains cerebral blood flow despite changes in blood pressure, with sustained BP elevation inducing vasoconstriction and increased resistance to blood flow. Hypertension is associated with a rise in the ‘set-point’ of this curve to compensate for elevations in blood pressure, although the dynamic range achieved may still be less than the upper limit of blood pressure variability.107 dCA is measured either by induced blood pressure changes  or by resting-state relationships between spontaneous fluctuations in blood pressure and cerebral blood flow in time-domain (‘Mx’), or frequency-domain transfer function analysis to estimate the magnitude (gain), delay (phase) and strength of the relationship (coherence), or by modeling the response to an acute step change . The underlying changes in vascular tone depend upon myogenic, metabolic and autonomic/neurogenic responses to maintain stable CBF.With technical advances to measure beat-to-beat blood pressure and cerebral blood flow, dynamic cerebral autoregulation (dCA) reflects rapid adaptation to changes in blood pressure.108 is associated with an increased risk of early neurological deterioration109 and haemorrhage110 and can predict a poor response to reperfusion therapies despite recanalisation,111 but studies have been small with inconsistent findings, partly due to difficulties in consistent measurement of dCA, although international collaborations (INFOMATAS) seek to better define its role. 112Despite the critical biological importance of cerebral autoregulation, evidence for its role in the aetiology of stroke is limited. Autoregulation is commonly impaired in the ipsilateral hemisphere in severe ischaemic stroke,In comparison, the population-based effect of hypertension and age on dCA are even less well documented. Limited studies have not demonstrated a consistent decline in autoregulation with age, although with conflicting results. Similarly, there is limited evidence of the effects of chronic hypertension on dCA and its relevance to development of cerebrovascular pathology, despite significant potential for new interventional targets.113Hypertension-related secondary prevention of cerebrovascular disease will be the focus of subsequent articles in this series, including intracerebral haemorrhage, recurrent ischaemic events and cognitive impairment. These intermediate pathophysiological targets between hypertension and clinical events provide new potential treatment targets. For example, calcium channel blockers and thiazide-like diuretics appear to reduce BP variability, but effects at different baseline blood pressure levels and by stroke aetiology are needed and there remains a need to define which antihypertensive medications are optimal in the secondary prevention of stroke.114 CSPS115) with cilostazol reducing ischaemic stroke to an extent greater than expected from its antiplatelet effects, with no increased risk of haemorrhage. Finally, XILO-FIST has recently reported a null effect of allopurinol on progression of white matter hyperintensities, despite a small reduction in blood pressure.116 However, no trial has yet identified a reliable intervention that has lead on to definitive trials showing efficacy.A number of trials are currently testing repurposed interventions to target specific aspects of these phenotypes. TREAT-SVDs is comparing the effects of amlodipine versus atenolol and losartan on cerebrovascular reactivity to inhaled carbon dioxide on MRI, and is expected to report in 2022/2023. OxHARP is testing phosphodiesterase inhibitors (sildenafil and cilostazol) on cerebral pulsatility and reactivity to carbon dioxide, using both transcranial ultrasound and MRI, with dCA as a secondary outcome. The completed but unreported PASTIS study (NCT02450253)assessed the effect of a single dose of tadalafil on white matter perfusion in the early phase after a lacunar stroke. LACI-1 tested implied improved cerebrovascular reactivity on MRI with either cilostazol or isosorbide mononitrate in lacunar stroke, leading to LACI 2 to test the same agents to prevent recurrent lacunar strokes and progression of white matter hyperintensities. This follows suggested benefits in larger clinical trials (PICASSO,The relationship between hypertension and stroke, and its reduction by appropriate blood pressure control, has significantly reduced its incidence. However, our developing understanding of the complex physiological effects of hypertension on the cerebral circulation, particularly in the context of cerebral SVD and its consequences, promises new treatment paradigms for the future.


Knowledge of the risk factors for implant osseointegration is essential for clinical decision-making and optimizing treatment success. This clinical report presents a rare case of implant failure in a patient who received intravitreal injections of a vascular endothelial growth factor (VEGF) inhibitor for the treatment of age-related macular degeneration. Following CARE guidelines, the report presents a case rehabilitated with a mandibular 2-implant overdenture using the immediate-loading protocol and standard procedures. The implants failed within six weeks of immediate loading although primary stability (≥50 Ncm) was achieved during surgery and clinical follow-ups did not show any deviance from standard implant care or patient-related complications. Further investigation suggested that the intake of a VEGF inhibitor may be the cause of failure. This clinical report highlights the importance of systemic risk factors in implant success and their consideration during planning for implant-assisted treatment. Osseointegration is defined as the “process whereby clinically asymptomatic rigid fixation of alloplastic materials is achieved and maintained in bone during functional loading” . A prediVascular endothelial growth factor (VEGF) is a growth factor involved in many human physiologic processes such as angiogenesis . VEGF isFollowing CARE guidelines for case reports , this clThe connection of right- and left-side implants and prostheses was established via unsplinted abutments . The midline implant was unloaded for within-patient comparison on peri-implant bone crest height and implant stability. The opposing maxillary dentition for all patients was rehabilitated with a conventional removable complete denture. Of the 18 participants, one participant lost the left implant because of parafunctional habit. Another patient who experienced implant loss , and who is the subject of this case report, lost both loaded implants. Implant loss occurred within 6 weeks of immediate loading. The medical and dental history and panoramic and cephalometric preoperative radiographs  , 11. AMDDespite distinctive clinical relevance, knowledge about the potential effect of inhibiting VEGF-dependent angiogenesis in dental implant osseointegration is limited. Mair et al. studied the impact of TNP-470, an inhibitor of angiogenesis, on the peri-implant bone formation and osseointegration . Their rOn the contrary, the potential impact of immediate loading on implant failure should not be ignored, especially since both immediately loaded implants failed while the midline control implant healed uneventfully. It was initially recommended, in the conventional oral implant-loading protocol for rehabilitation with a mandibular overdenture, that loading of the implant should occur after a minimum of 3 months of healing after implant placement in the mandible, to let the implant osseointegrate with the surrounding bone . In receImplants may fail for different reasons that are not always easy to determine. The intake of Ranibizumab, a commonly used VEGF inhibitor, may introduce a risk for implant success due to its negative effects on bone formation and osseointegration. Data from large clinical cohorts are needed for further investigation on this topic.


Antibiotic-loaded polymethyl methacrylate (PMMA) has been widely applied in the treatment of knee periprosthetic joint infections. However, problems with antibiotic-loaded PMMA-based spacers, such as structural fracture and implant dislocation, remain unresolved. A novel polyethylene-based spacer, designed with an ultra-congruent articulating surface and multiple fenestrations, was introduced in the current study. Validation tests for biomechanical safety, wear performance, and efficacy of antibiotic cement were reported. During cycle fatigue testing, no tibial spacer failures were observed, and less wear debris generation was reported compared to commercial PMMA-based spacers. The volumetric wear of the novel spacer was within the safety threshold for osteolysis-free volumetric wear. An effective infection control was demonstrated despite the application of lesser antibiotic cement in the 30-day antibiotic elution test. The tube dilution test confirmed adequate inhibitory capabilities against pathogens with the loaded antibiotic option utilized in the current study. The novel polyethylene-based knee spacer may offer sufficient biomechanical safety and serve as an adequate carrier of antibiotic-loaded cement for infection control. Further clinical trials shall be conducted for more comprehensive validation of the novel spacer for practical application. Ultra-high molecular polyethylene (UHMWPE) and polymethyl methacrylate (PMMA)-based bone cement are polymers commonly utilized in joint arthroplasty surgeries. Bone cement has generally been utilized for gap filling or reinforcement of an osteoporotic structure, while UHMWPE has been widely applied in soft bearing components for artificial joint articulation. Total knee arthroplasty (TKA) is currently accepted as the most efficient and successful surgical strategy for the treatment of intracapsular disease of the knee joint, greatly enhancing patients’ quality of life ,2. HowevAlthough articulating knee spacers have demonstrated advantages in joint mobility and clinically successful rates in infection control, issues relating to biomechanical safety contributed by cement material characteristics should be carefully noted. Articulating knee spacer-related complications include squeaking during articulation , intra-aA novel polyethylene-based knee spacer, for the purpose of infection control, was developed to enhance the biomechanical safety and surgical convenience of articulating knee spacers. As an antibiotic-loaded cement carrier, the scaffold-like structures within the polyethylene component facilitate cement deposition with antibiotic-releasing properties, while a standardized metallic canal rod is prepared for intramedullary infection control. The current pre-clinical validation study has been designed to confirm the safety and efficacy of the novel device in infection control by conducting the required in vitro biomechanical fatigue, wear, and antibiotic elution testing before approval in clinical applications.The novel spacer set consists of a femoral spacer, a tibial spacer and a canal rod . The spaThe canal rod is made of a titanium alloy consisting of a 4 mm shaft, a proximal instrument adapter to facilitate the removal of the rod from the canal, and a distal umbrella-shaped hook to ensure distal support of the wrapped cement to facilitate the clean removal of the structure from the canal when infection control has been successfully accomplished .Size #1 femoral and tibial spacers were selected based on the worst-case principle (the least space spared for antibiotic-loaded cement volume among the 5 sizes) and the available dimensions of the comparative commercial spacer. Antibiotic-loaded bone cement  was applied to validate the usage of commercial cement in infection control, while regular (non-antibiotic) bone cement  was utilized as a cement baseline for antibiotic elution testing. The femoral and tibial spacers were positioned inside customized silicone molds to ensure smooth articulating surfaces during cement preparation . The cem5 cycles”, to mimic the maximum 180-day implantation period for general infection control [Fatigue testing was set in accordance with the guidance in ASTM F1800 A. Both s control . The tes5 cycles [The wear test was conducted according to the guidance in ISO 14243-1. The loading magnitudes were reduced to 50% to account for partial weight bearing conditions and the regular 180-day usage of the knee spacer . Hence, 5 cycles , as desc® knee simulator, Endolab Mechanical Engineering GmbH, Riedering, Germany, 5 cycles were conducted with loading frequency set at 1 Hz for all 6 specimen sets. All specimens were then dried in a vacuum chamber (37 °C) to facilitate the quantification of the material loss due to spacer or cement wear. The results were compared to published data on commercial knee spacers to confirm the wear safety volume [All specimens were pre-soaked in the test fluid . The specimens were then mounted onto the knee simulator  (ATCC 25923), methicillin-resistant Staphylococcus aureus (MRSA) (ATCC 43300), coagulase-negative Staphylococci (CoNS) (ATCC 14990), Pseudomonas aeruginosa (P. aeruginosa) (ATCC 27853), and Escherichia coli (E. coli) (ATCC 25922) were selected as the pathogens. The samples were added to a tube with 105 bacterial colony forming units (CFUs)/mL in 96-well cultured dishes and incubated at 37 °C for 24 h. Visual identification of bacterial growth was used as qualitative evidence of bacterial inhibition. Clear test fluids were an indication of sufficient eluted antibiotic concentrations. A positive control (without antibiotics) and standard samples with varying antibiotic concentrations were prepared for visual comparison.The retrieved 2 mL elution samples were measured for antibiotic activities with the tube dilution method. Bacteria strains, including methicillin-sensitive 5 fatigue load cycles. The maximal displacement of material remained stable without sudden raise/drop of values recorded by the material testing system. However, one test specimen showed minor cracks on the cement region  revealed high elution behaviors at the beginning  which gradually decreased with time . The oveP. aeruginosa throughout a 30-day testing period with observations of clear tube samples. Antibiotic activities lasted for 24 days in E. coli samples, and 3 days in MRSA samples. However, the effective bacterial inhibition of CoNS samples only lasted for one day.Polymer materials have been widely applied in orthopedic surgery for the purpose of implantation or supplementation. The novel polyethylene-based knee spacer is the first design concept using scaffold-like UHMWPE structures filled with antibiotic–loaded PMMA-based bone cement, to facilitate smoother articulation and control infection of the knee joint. The current study conducted several in vitro pre-clinical validation tests to validate the safety and efficiency of the spacer set. The advantageous material characteristics of UHMWPE supported the fatigue safety of the tibial spacer and ideal anti-wear performance for spacer longevity in simulated knee motion while ensuring sufficient antibiotic elution performance.Safety is always the key issue in orthopedic implants. Commercial preformed knee PMMA cement spacers or hand-made cement spacers have been widely used in two-stage exchange arthroplasty. However, spacer dislocation and fracture are common complications. Haddad et al.  reported3. According to the wear testing results in the current study, the dry weight loss of the polyethylene-based spacers was less than that of the commercial cement spacers  [3), was only 55.92 mm3, lower than the reported osteolysis-free volumetric wear threshold. The ideal surface condition of UHMWPE spacers , which may have contributed to the volumetric wear results, demonstrated better anti-wear performance than commercial knee spacer with full-cement coverage.Component wear at the articulating surface remains an issue as there are no existing surface treatment and there remains uncontrollable complexity within the joint capsular. In addition to structural damage due to surface wear, polymer and cement wear debris morphology ,38,39,4026.0 mg) . The volE. coli. The major reason may be the bone cement used in the current study, which was loaded with specific gentamicin concentrations that may not be appropriate antibiotic options or dosages against these pathogens. This reveals an important consideration that not all commercial preformed antibiotic-loaded cement spacers or their associated antibiotic formulations may fulfill ideal antibiotic options for infection control. Therefore, flexible antibiotic options  and 71.6% of the tibial  spacers. A reduced cement volume usage can provide better cost-effectiveness. However, major concerns exist with insufficient antibiotic elution concentration associated with lesser antibiotic-loaded cement usage. However, the current study results for the novel scaffold-like UHMWPE spacer have provided sufficient confidence that lesser cement volume usage may still provide clinically effective infection control capabilities with minimum gentamicin concentrations in the antibiotic-loaded cement group (1.9 μg/mL) remaining effective (>1 μg/mL)  at the e-Concern of Bacterial BiofilmThere were several limitations with the current study:The formation of bacterial biofilm is an important concern when foreign orthopedic devices are implanted , especia-The Antibiotic-Loaded Cement AppliedThe current study has only considered a single antibiotic-loaded cement option for tibial spacer fatigue, wear, and antibiotic-elution testing. The application of the current spacer with other commercial antibiotic cements or surgeon-defined antibiotic formulations or dosages will have to be further validated for mechanical safety and biological efficacy concerns. In addition, the Palacos R + G (0.5 g gentamycin/40 g powder) is generally applied for prophylaxis use . Due to -Modified Test ConditionThe spacer is defined as a device used for temporary infection control and limb length (or soft tissue tension) maintenance. Generally, the recommended period for usage is 180 days under partial weight-bearing conditions. The load conditions in the current biomechanical test were modified with reference to an estimated partial weight-bearing condition . These t-Wear Rate CalculationDue to technical limitations, the weight or volumetric ratio of the UHMWPE and cement wear debris could not be identified. However, from wear pattern observations of the test specimens and SEM images for particle examination, most of the wear particle were determined to have originated from the UHMWPE material. Volumetric wear performance was then calculated by applying the appropriate density for UHMWPE.-Surface Condition and Material of Molding DeviceIt was observed that the cement articulation was not ideally smooth. The major cause of this rough surface was the simple silicone mold was utilized for forming the articulating surface . The sil-Unverified Canal RodThe canal rod in the current knee spacer set has only been introduced for its function and features but was not involved in the pre-clinical validation testing. Major safety and efficacy concerns exist with the utilization of the worst-case conditions, without the utilization of canal rod (wrapped with antibiotic cement). No fatigue testing was applied to the canal rods as they do not contribute to mechanical support. Antibiotic elution testing and wear testing were also not applied as the canal rods did not interfere with the antibiotic-loaded cement spacer and its articulation surface.The scientific validation conducted in the current study with a novel scaffold-like polyethylene-based knee spacer design will provide surgeons with an improved knee joint infection control solution that has sufficient mechanical properties, anti-wear performance, and antibiotic elution. As a cement carrier, this novel device may offer surgeons more flexible antibiotic options and dosage adjustments for a more precise and effective infection control. Further clinical trials should be conducted to enhance clinical confidence regarding the use of this device in practical applications. TW M465151U  Porous artificial knee joint CN203341865U Porous type artificial knee joint (patent of Mainland China)


The De Winter electrocardiogram (ECG) pattern is considered as a ST elevated myocardial infarction (STEMI)-equivalent pattern. Due to its rare nature, it is unclear whether this ECG pattern suggests the presence of some other condition.We reported a 47-year-old man with new-onset chest discomfort several hours after the second-stage percutaneous coronary intervention (PCI).An emergency coronary angiogram (CAG) did not show any abnormality. However, the dynamic changes in the ECG and myocardial biomarkers indicated perioperative myocardial infarction.The patient was monitored in the cardiac care unite (CCU), and was administered an intravenous infusion of diltiazem and subcutaneous injection of low molecular weight heparin.After a few hours, his symptoms were alleviated. The patient was discharged after 6 days of hospitalization without any complications.The De Winter ECG pattern can be observed in patients without significantly coronary arteries occlusion. The newly onset De Winter ECG pattern after PCI procedure may indicate perioperative myocardial infarction caused by impaired microvascular perfusion. Thus far, nearly all the reported cases with this ECG pattern have exhibited proximal left anterior descending (LAD) artery occlusion.,3 Therefore, the De Winter ECG pattern is considered as a ST elevated myocardial infarction (STEMI)-equivalent pattern. Due to its rare nature, it is unclear whether this ECG pattern suggests the presence of some other condition. In the present report, we describe the case of a STEMI patient who underwent second-stage percutaneous coronary intervention (PCI) and subsequently developed a perioperative myocardial infarction that presented a De Winter ECG pattern, despite the coronary angiogram (CAG) not showing occlusion in any arteries.Recent studies have found that certain patients with an acute thrombotic occlusion of a large epicardial coronary artery do not show ST-elevation on the standard 12-lead electrocardiogram (ECG).2A 47-year-old man with a history of hypertension was admitted to the emergency department for typical acute chest pain nearly 18 hours after symptom onset. In the emergency department, an ECG Fig. . HoweverWith regard to myocardial enzyme levels, his CK-MB and troponin T values were 23.56 ng/mL and 3007 ng/L, respectively. We believed that in-stent thrombosis and main side branch occlusion could not be excluded. Hence, emergency CAG was performed, but no significant abnormality was noted  died within the first week.In 2008, De Winter first described a special ECG pattern wherein the upward-sloping ST-segment depression in leads V1 to V6 was accompanied by tall, positive symmetrical T waves. suggested that the area of transmural ischemia was large, and hence, no injury currents were generated towards the precordial leads but were only directed upwards to the standard lead aVR. Theoretically, an anatomical variant of the Purkinje fibers with endocardial conduction delay could also result in this special ECG pattern. Some researchers believe that the absence of ST-segment elevation may be related to the lack of activation of sarcolemmal adenosine triphosphate (ATP)-sensitive potassium (KATP) channels by ischemic ATP depletion, as has been found in KATP-knockout animal models of acute ischemia.The electrophysiological explanation of why these patients did not show ST-segment elevation in the presence of an occluded proximal LAD artery remains elusive. Verouden et al or RCA. Moreover, Xu et al described a case with the De Winter ECG pattern wherein emergency CAG confirmed the occlusion of the obtuse marginal artery. A systemic review indicated that this ECG pattern may be observed in cases with either LAD or LCX occlusion.As cases with the De Winter ECG pattern are rare, it is currently unclear whether it is also observed in other clinical conditions besides myocardial infarction due to LAD occlusion. This specific ECG pattern has recently been described in acute myocardial infarction patients with a culprit lesion in the first diagonal branchTo our knowledge, this is the first case exhibiting the De Winter ECG pattern following PCI. Emergency CAG showed TIMI-3 blood flow in both the left and right coronary arteries, and in-stent thrombosis or main side branch occlusion were excluded. Nevertheless, the dynamic changes in the ECG and cardiac biomarkers indicated the presence of perioperative myocardial infarction. We believe that acute impairment of microvascular reperfusion might have caused this condition.Conceptualization: Hua Wang, Shi Chen.Investigation: Shi Chen.Project administration: Shi Chen.Supervision: Hua Wang.Validation: Hua Wang.Writing – original draft: Shi Chen, Liwei Huang.


Future work will include a model-based algorithm to compute secure grasp, virtual reality immersion, and testing with clinical populations. Sensory feedback from wearables can be effective to learn better movement through enhanced information and engagement. Facilitating greater user cognition during movement practice is critical to accelerate gains in motor function during rehabilitation following brain or spinal cord trauma. This preliminary study presents an approach using an instrumented glove to leverage sense of agency, or perception of control, to provide training feedback for functional grasp. Seventeen able-bodied subjects underwent training and testing with a custom-built sensor glove prototype from our laboratory. The glove utilizes onboard force and flex sensors to provide inputs to an artificial neural network that predicts achievement of “secure” grasp. Onboard visual and audio feedback was provided during training with progressively shorter time delay to induce greater agency by intentional binding, or perceived compression in time between an action (grasp) and sensory consequence (feedback). After training, subjects demonstrated a significant reduction ( Wearable sensors are becoming increasingly prevalent to monitor movement activities and to provide the user feedback, from which current and future behavior can be modified . InformaInjury to the spinal cord or brain often requires physical therapy to regain functional abilities and perform activities of daily living (ADLs) ,5. Restophysically assist, but rather provides informative feedback about secure grasp to cognitively engage the user during training. Secure grasp can be analytically identified by deterministic factors of grasp force quality . During the functional task, trained ANN output was rounded. ANN output values greater than 0.5 were rounded to ‘1’ to indicate secure grasp, and output values below 0.5 were rounded down to ‘0’ to indicate insecure grasp.Each subject participated in three separate sessions for performing a functional grasp-move-place task with the glove C. Each sImprovement in performance of the functional task was measured as reduction in the following three metrics from baseline to post-training retention: (1) task completion time, (2) motion pathlength of object, and (3) placement error. Motion pathlength was computed according to accumulation of 3D position displacements of object over time samples (100 Hz) when velocity was non-zero. The completion time was measured as the time duration when object velocity was non-zero. Placement error was computed as the projection of the center of the object onto the table surface from the center of target. Procedures for marker-based digitization  was usedFor these performance metrics, two training outcomes measures were specifically evaluated. First, an intra-training rate was evaluated as the fitted linear regression slope to the performance metric data plotted across thirty sequential trials during the training trial-block. Since the desired behavior is a reduction in each performance metric, a negative slope served as a ‘positive’ intra-training rate. Ultimately, we observed three distinct subject-cases whereby a given subject may exhibit a distinct combination of intra-training rates across the three training feedback modes. The second outcomes measure of interest was post-training effect whereby the difference in performance metric from pre-training to post-training trial blocks are evaluated. Again, a negative difference, i.e., reduction in performance metric value, is the desirable outcome.For evaluating ANN training parameters, an ANOVA and Tukey post hoc comparisons with Bonferroni correction were performed for cross-entropy and percentage error across data sets for training, validation, and testing. A two-sample t-test was used to compare mean squared error computed directly from continuous ANN output versus when ANN output was rounded to 0 or 1. One-way repeated-measures ANOVA and Tukey post hoc comparisons with Bonferroni correction were performed independently for each metric across the three feedback modes serving as training groups and across trial-block times . Another one-way ANOVA was done on the outcome measure of intra-training rate and post-training effect for each performance metric for comparison between training feedback groups. A one-sample t-test was also done on both outcome measures of each performance metric and feedback group to verify significant difference from zero.The results for ANN training parameter values are shown in Across all subjects, the mean true-positive rate for ANN prediction of secure grasp on the testing data ranged from 87% to 91% for the three grip postures , Table 2The completion time, pathlength, and placement error data before and after transformation by intra-subject normalization from pre-training data are shown in The mean pathlength, completion time, and placement error prior to training were 25.0 ± 1.75 cm, 1.48 ± 0. 35 s, 5.6 ± 0.68 mm, respectively, over all subject pre-training trial-blocks. Repeated measures ANOVA  demonstrp < 0.05) were observed across feedback groups for both completion time and pathlength for both intra-training rate and post-training effect. While IF only demonstrated significant improvement over NF for intra-training completion time, IBF demonstrated significant improvement over NF for intra-training completion time, intra-training pathlength, post-training completion time, and post-training pathlength. No significant differences were observed related to placement error.While the ANOVA results on outcome measures indicate significant differences among feedback groups, This study observed improvement in grasp performance metrics after sensory feedback training with an instrumented glove designed to induce greater cognitive agency through intentional binding. The glove hardware components are not wholly unique to previous sensor gloves. Sensor integration with a computational framework to assess secure grasp and alert the user at cognitively inspired timing intervals is the novel feature. As such, this study demonstrates an important bridging concept between the psychology of agency and delivery of sensor-based sensory feedback for improving movement performance. Specifically, post-training functional performance may be enhanced when robust computational detection of secure grasp from sensor signals is used to inform the user at specific timing intervals. The instrumented glove system was developed to alert the user when secure grasp onto an object was achieved. Training a feedforward artificial neural network on empirical data to predict all-or-nothing accomplishment of secure grasp was sufficient to examine the effects of feedback timing on performance. ANN training parameters were stable  across training/testing/validation data sets and behaved functionally well (high true-positive prediction rate) across various grasp types. We confirmed the ANN  was needed to effectively identify empirical patterns of secure grasp. An analytical computation of secure grasp relying on explicit sensor signals would have been relatively inferior in prediction of secure grasp during training.p < 0.05) improvements in performance were observed for metrics of pathlength and completion time with intentional binding feedback, whereby feedback was provided at progressively shorter time intervals during training. Significant differences were not observed for placement error, suggesting little margin of placement variability for healthy subjects. A placement accuracy objective was still necessary to ensure subjects properly performed the task. Previous studies have demonstrated that feedback to reinforce performance and activate sensory modalities  can enhance movement learning [With ANN-based feedback, significant (learning .Results from our study suggest that well-placed timing of sensory feedback can further accelerate functional gains. During training with IBF, we employed progressive reduction in the time-delay between voluntary grasp action and the sensory-based alert to promote greater agency by altering perception between grasp action and expectation of sensory consequences during training. Provision of feedback in this manner produced significant improvements in performance criteria of task completion time and motion pathlength compared to the control case of NF. While IF also demonstrated improved performance compared to NF, the instances were fewer and non-unique relative to IBF. Training with IBF not only produced greater significant improvements in performance measures from NF, but these improvements were all significantly non-zero. This distinction was important since NF produced instances of significantly worse post-training performance. Performance decline with NF may be attributable to cognitive fatigue from lack of engagement with repetition of a simple task whereby provision of any feedback could generate positive training and post-training effects. We postulate that larger improvements with this type of feedback training may be observed where ranges in performance are expectedly larger. This may be the case with clinical populations, complex tasks, or more enhanced feedback as with VR . We furtTraining with gradual reduction in feedback delay was inspired by the concept of intentional binding. We sought to actively compress the perceived time-interval between grasp action and the expected sensory consequence  as a vehThe notable limitation in this study was that we did not explicitly measure agency and directly relate it to performance. Our previous work has made the positive connection between agency and performance  but reliIn the future, enhanced forms of rehabilitation should employ instrumented wearables that are easy to don-and-doff and approaches that cognitively engage participants. Our pilot study suggests that cognitive factors may be directly leveraged for better movement training through sensory feedback. Wearables that are lightweight and cosmetic could be discreetly utilized to provide cognitively inspired feedback to train better movement throughout the day. Furthermore, instrumented wearables, such as the presented glove, could facilitate usage at home and throughout the day to bridge and supplement traditional physical therapy sessions. Physical therapy itself may benefit from employing instrumented wearables and other methods that focally consider cognitive factors such as agency. We plan to make this glove compatible with VR such that it could be used in stand-alone mode while doing ADLs or incorporated with VR for intensive physical therapy. Both instrumented wearables and VR applications offer unique advantages of logging data for long-term assessment of progress, enhanced movement tracking with high-fidelity sensors or computation, and entertaining interfaces to inform and engage users. Sophisticated algorithms may be employed to further advance these features of rehabilitation systems. These algorithms can analytically identify movement features such as grasp or intelligently present feedback cues through devices and VR to personalize and optimize rehabilitation outcomes.


The challenges of the pathological diagnosis of this case are discussed. The diagnosis of primary ovarian melanoma should not be based on one immunohistochemical marker only. A single biopsy of omental implants in peritoneal carcinomatosis during ex-lap surgery should be avoided.Ovarian granulosa cell tumors are rare gynecological cancers with favorable clinical evolution and survival outcomes. We report a new case of this presentation in a patient that was initially diagnosed as a bilateral primary melanoma of the ovary. The patient is a 51-year-old woman with a history of abdominal swelling and deterioration of her general conditions. Physical examination revealed abdominal distension and diffuse dullness with initially highly elevated cancer antigen 125. Contrast-enhanced thoracoabdominal-pelvic computed tomography showed a left-sided ovarian mass and abundant ascites and pleurisy. Ex-lap surgery found two large bilateral ovarian masses associated with peritoneal carcinomatosis and highly abundant ascites. The histopathological examination of the omental biopsy revealed an undifferentiated tumor proliferation of cells with highly positive Human Melanoma Black 45 marker in favor of an achromic malignant melanoma according to the pathologist. Because of her advanced disease, the patient received a combination of six cycles of neoadjuvant dacarbazine, cisplatin, and paclitaxel and showed partial response based on the response evaluation criteria in solid tumors, followed by total abdominal hysterectomy and bilateral salpingo-oophorectomy with cytoreductive surgery. Unexpectedly, the histopathological analysis of the surgical specimens was in favor of an advanced adult granulosa cell tumor with positive inhibin B. Our patient is alive at her 13 Malignant melanoma of the gynecologic tract is rare with a particular aggressive behavior and poor survival outcomes . PrimaryOur patient was a 51-year-old white woman from the Rif region of Morocco (Amazigh ethnicity) that presented abdominal swelling and deterioration of her general conditions three months before her visit to a general practitioner. The patient was previously diagnosed with hypertension and is currently under angiotensin-converting enzyme inhibition without any family history of hereditary cancer or other diseases. Her physical examination revealed abdominal distension and diffuse dullness with initially highly elevated cancer antigen 125 (CA-125) at 461.7 U/mL. Diagnostic imaging based on contrast-enhanced thoracoabdominal-pelvic computed tomography (TAP-CT)  but without liver and spleen dissemination were found upon exploration and an omental biopsy was performed. The postoperative course was uneventful. Microscopically Figure , the secImmunohistochemistry (IHC) based on 13 antibodies covering the most known possible tissue origins of tumor infiltration was used for differential diagnosis on surgical biopsy , cisplatin , and paclitaxel  every three weeks. After six months of NACT, there was a decrease in the size of the known left ovarian tumor (52 × 49 mm against 91 × 77 mm) in favor of a partial tumor response  and other minor types including POM . Given tSurgery is the cornerstone for treating ovarian melanomas . Total hIn this paper, we reported a new case of adult granulosa cell tumor of the ovary that was first diagnosed as bilateral POM. Importantly, we recommend to all surgical oncologists to avoid surgical biopsies for diagnostic purposes on peritoneal implants. The primary tumors should be targeted for biopsy. Moreover, the pathologists should use more than one marker to suggest melanoma in their pathological review. Of note, the association of surgery and chemotherapy combining cisplatin, paclitaxel, and dacarbazine demonstrated clinical response in adult granulosa cell tumor of the ovary.