The inferior longitudinal fasciculus (ILF) is a long-range, associative white matter pathway that connects the occipital and temporal-occipital areas of the brain to the anterior temporal areas. In view of the ILF’s anatomic connections, it has been suggested that this pathway has a major role in a relatively large array of brain functions. Until recently, however, the literature data on these potential functions were scarce. Here, we review the key findings of recent anatomic, neuromodulation, and neuropsychological studies. We also summarize reports on how this tract is disrupted in a wide range of brain disorders, including psychopathologic, neurodevelopmental, and neurologic diseases. Our review reveals that the ILF is a multilayered, bidirectional tract involved in processing and modulating visual cues and thus in visually guided decisions and behaviors. Accordingly, sudden disruption of the ILF by neurologic insult is mainly associated with neuropsychological impairments of visual cognition . Furthermore, disruption of the ILF may constitute the pathophysiologic basis for visual hallucinations and socio-emotional impairments in schizophrenia, as well as emotional difficulties in autism spectrum disorder. Degeneration of the ILF in neurodegenerative diseases affecting the temporal lobe may explain (at least in part) the gradual onset of semantic and lexical access difficulties. Although some of the functions mediated by the ILF appear to be relatively lateralized, observations from neurosurgery suggest that disruption of the tract’s anterior portion can be dynamically compensated for by the contralateral portion. This might explain why bilateral disruption of the ILF in either acute or progressive disease is highly detrimental in neuropsychological terms. Although the first anatomic description of the ILF dates back to the beginning of the 19 century , scienti century  and iden century . Until rIn the present review, we first summarize data on the topological organization of the ILF; these range from historical reports to the latest DTI and dissection studies that suggest a multilayered organization. We next focus on studies of both brain-damaged and neurologically healthy participants in which the ILF’s role in an array of visual-based functions has been highlighted. Alteration of the ILF’s connectivity leads to visual agnosia and prosopagnosia, semantic and lexical retrieval difficulties, alexia, and impairments in visual emotion recognition. We also review reports concerning the impact of disruption of the ILF in psychopathologic and other neurologic disorders; these data indicate how the tract may be involved in the pathogenesis of a range of clinical and behavioral symptoms. Lastly, we look at how the ILF  can be dynamically compensated for in certain pathophysiologic situations.Literature on the ILF published up to 2017 was accessed by searching open-access Internet and proprietary databases . The search term was “inferior longitudinal fasciculus.” Records were screened manually, and only experimental investigations and reviews were selected. Historical reports not referenced in the databases were also selected for review.Anatomie des centres nerveux.” By this time, scientists had started to use dichromate to harden anatomic specimens, and the invention of the microtome had simplified the preparation of serial sections. The development of these and other histopathologic techniques (notably methods for staining myelin sheaths) and application of Wallerian degeneration had enabled the performance of remarkably accurate analyses long before the advent of MRI and diffusion tractography. Nonetheless, the Dejerines’ meticulously detailed description of the ILF was mostly restricted to the core of the tract, i.e., the compact portion of fibers surrounding the inferior and inferior-lateral aspects of the lateral ventricle. According to the Dejerines , the ILF formed “a kind of sharply bent “gutter” or groove, open at the top and the inside, which receives fibers from the occipital-temporal lobe in its concave projection. […] Inside the occipital lobe, the inferior wall of the gutter turns in on itself at the top and on the inside. At the base of the cuneus, the two edges of the “gutter” are so close to each other that they join together and transform the bundle into a kind of empty cone, the slender apex of which is located around 2.5 cm from the occipital pole. It follows that on coronal sections, the ILF is shaped like a relatively irregular, angular ring inside the occipital lobe, and like a sharply bent “gutter” inside the temporal lobe.”In view of the fibers’ cortical terminations and orientation, the Dejerines stated that the ILF “sends a great number of fibers to the three temporal convolutions , the third occipital convolution (O3), and the fusiform and lingual lobules.” The Dejerines further affirmed that “the fibers of the ILF originate inside the cortex of the occipital pole and lobe (…) and rapidly come together to form a thin annular fascicle behind the occipital horn (…), whereas fibers originating from the inferior-lateral half of the occipital lobe are oriented from back to front along the inferior-lateral edge of the occipital and temporal horns, fibers originating from the superior-lateral part of this lobe are oriented very obliquely downward and forward along the lateral wall of the ventricular atrium, and are all the more oblique when they come from higher regions.”stratum sagittale externum by Sachs) was composed solely of association fibers. According to the Dejerines, the projection and association fibers in the superior part of the ILF are so intertwined that dissection cannot resolve the problem; the only way to distinguish between the two is to apply the Wallerian degeneration method. The Dejerines also stated that “the inferior portion of the ILF is solely an association pathway.” Nonetheless, the same question was raised a century later by Figure 1B). In their book, the Dejerines disagreed with the descriptions made by Figure 1C) demonstrated that “in addition to the occipital-temporal projection system [as proposed by It was not until Figure 1D). The most precise study was performed by Subsequent studies of the ILF  based onThe ILF is in direct contact with five other association tracts: the uncinate fasciculus, the inferior fronto-occipital fasciculus (IFOF), the long and posterior/vertical segments of the arcuate fasciculus and the vertical occipital fasciculus of Wernicke. It has also close relationships with the optic radiations and with the fibers forming the tapetum of the corpus callosum.Both the uncinate fasciculus and the ILF have cortical projections in anterior temporal structures. They provide an indirect anatomical connection between the posterior temporal and occipital areas, and the frontal lobe. According to some authors, both anatomical connectivities may form an indirect ventral stream for language and semantic processing .The IFOF is also in close relationship with the ILF. Inside the temporal and occipital lobes, the ILF runs more superficial and ventral than the IFOF. Both white matter tracts have cortical terminations in posterior occipito-temporal areas, especially in the fusiform, lingual and lateral occipital cortices.The long and the posterior segments of the arcuate fasciculus, the vertical occipital fasciculus and the ILF have all four cortical terminations in the posterior part of the inferior temporal gyrus, making this brain area important for direct interactions between the ventral and dorsal streams.stratum sagittale where pass the IFOF and the middle longitudinal fasciculus cranially, and the optic radiations and the tapetum medially.Finally, at the deep aspects of the ILF, inside the temporal lobe, are found the fibers forming the Diffusion tensor imaging studies of white matter maturation over the lifespan generally suggest that the ILF matures earlier than other association white matter fibers  . In humaIn view of the ILF’s cortical endpoints in visual occipital and inferior temporal associative areas, this tract is topographically well located to subserve the so-called ventral visual stream  thought to be critical for object recognition . In suppConverging evidence from both positron emission tomography and functional MRI (fMRI) studies suggests that certain cortical regions in the ventral occipital-temporal cortex are part of a “core” face-selective network, i.e., they are more strongly activated by faces than by other classes of stimuli like objects or places  in the microstructural organization of both the ILF and the IFOF on both body sides . ImportaIn summary, the result studies of healthy individuals and brain-damaged patients suggest strongly that a functional right ILF is crucial for efficient face recognition. However, this hypothesis does not rule out the possible involvement of other white matter connectivities  in face recognition. Indeed, functional imaging has identified a number of face-selective cortical areas. For example, white matter connections between the right posterior superior temporal sulcus and the temporal pole (both of which belong to the “extended” face network) may convey critical signals for face recognition–perhaps through the fibers in the middle longitudinal fasciculus.Regions in the ventral occipital-temporal cortex have been repeatedly identified as being particularly important in reading processing; this is especially true for the so-called visual word form area (vWFA) located in the lateral occipitotemporal sulcus , which enables the recognition of word forms . The facIn line with the hypothesis whereby the ILF subserves the semantic/orthographic reading pathway, other data suggest that the tract is involved in reading comprehension. For example, inter-individual variations in the microstructural properties of both the left and right ILFs in people with normal intelligence are correlated with word reading fluency and reading comprehension . This agThe ILF has many connections to the ATL; it has been suggested that the latter’s ventrolateral aspect ATL acts as a transmodal semantic hub . In viewTwo very recent studies of healthy individuals have provided a better understanding of the types of semantic processes that may be distributed through the ILF. In the first study, inter-individual variability in the microstructural properties of both the ILF and the UF was found to be predictive of successful word-learning; the significant negative correlation between performance and RD suggested that connectivities of the ILF and the UF are involved in the acquisition of novel word-to-meaning mappings, and thus in semantic learning . In the Other experimental evidence suggests that the left ILF is involved in other language processes–lexical retrieval, in particular. In a large-scale neuropsychological study of 110 patients having undergone a surgical resection for diffuse low-grade glioma (DLGG), residual tumor infiltration in the left ILF was found to be a strong predictor of permanent impairments in lexical retrieval . This reBy virtue of the ILF’s connections with both the visual cortex and the amygdala, this tract is conceptually well suited to the integration of visual and emotional processes within the cortical affective network. In fact, the first evidence for this involvement was reported in the 1980s and 1990s; three patients with occipital-temporal lesions extending to the ILF were described as suffering from a syndrome called visual hypoemotionality, defined as a “modality-specific inability to become aroused by visual cues” and which was accompanied by feelings of “unreality or detachment” . At the n = 104 patients), the extent to which the IFOF and/or the ILF were disrupted (with regard to lesion volume) was found to be strongly predictive of inability to identity facial emotions (n = 42) suffering from moderate-to-severe traumatic brain injury , other appears to be bilateral . Further research must now better delineate the ILF’s exact state of involvement in each hemisphere.It is also noteworthy that only a few studies have focused on the ILF’s functions. Much of the experimental evidence has been derived by studying the correlation between DTI scalars and behavior in healthy subjects. Accordingly, causal and converging evidence is still needed to confirm and generalize most of the results described in this section.Several lines of evidence suggest that white matter abnormalities are central in the pathogenesis of schizophrenia, and may result from impaired myelination and/or oligodendroglial function . AlthougAlthough structural disruption of the ILF is apparently less frequent than structural disruption of other white matter tracts, some studies suggest that ILF is only damaged in patients showing specific patterns of clinical symptoms . This isIn a recent study, the mean DTI metrics  in the left ILF (but not the right ILF) were significantly lower in adolescents with schizophrenia than in healthy controls . ImportaAlong the same lines, patients with 22q11.2 deletion syndrome  also display abnormal white matter connectivity. Importantly, individuals with 22q11.2 deletion syndrome have an elevated risk of developing schizophrenia, with an incidence of approximately 30%. Overall, individuals with 22q11.2 deletion syndrome appear to have the same patterns of white matter alterations as patients with schizophrenia . InteresDisruption of the structural connectome is one of the most frequently reported features of ASD. Indeed, scientific research has repeatedly identified anomalies in the microstructural organization of associative and inter-hemispheric white matter tracts in this patient population. Indeed, fiber tract disruption is now considered to be an integral part of the neural phenotype in ASD. However, the hypothesis whereby patients with ASD present with widespread, severe alterations of white matter pathways has been challenged. For example, recent work demonstrated that most of the white matter changes observed in ASD might be artifacts caused by the patient’s head motion–a variable that had not been carefully controlled for in most of the literature studies . HoweverIn the above section, we reviewed evidence showing that disruption of the ILF is associated with specific patterns of neuropsychological or clinical symptoms in certain patient populations. In particular, ILF dysfunction may be involved in the pathogenesis of psychotic symptoms in both schizophrenia and 22q11.2 deletion syndrome, and may have a role in the occurrence of the social cognition impairments that characterize both of these disorders. However, the link between ILF dysfunction, behavioral outcomes, and social cognition impairments remains unclear and must be specifically addressed in future studies; ideally, a comprehensive battery of neuropsychological tests should be used to assess the different aspects of social cognition.In accordance with the ILF’s putative functions, our review of the literature suggested that disruption of this tract in ASD can be associated with impaired visual perception and/or poor language skills. Nonetheless, the correlational evidence is relatively dispersed and must be confirmed in subsequent research. Our review raises a number of other interesting ideas, however. For example, it would especially useful to assess to what extent the impairments in face identification and emotion recognition observed in ASD are attributable to anomalies in the ILF–especially in the right hemisphere.Lastly, the evidence reviewed here suggests that epilepsy-related dysfunction of the ILF may be a pathophysiologic correlate of a number of cognitive impairments in patients with TLE. However, the data are scarce, and little is presently known about the neuropsychological consequences of disruption of the ILF in this form of epilepsy. A more comprehensive assessment of this putative link might tell us more about the ILF’s functions in normal  circumstances.As DLGG frequently affects structures in the ATL and thus the underlying connectivity , the antIn this respect, a recent case study of a patient suffering from multifocal DLGG has provided a better understanding of potential plasticity of the ILF . The patAlong the same lines, a recent study showed that axonal stimulation of the anterior-to-middle part of the left ILF systematically induced lexical retrieval difficulties during a picture-naming task  but only when the temporal pole was not damaged by the tumor  . As wellAlthough disruption of ILF may be compensated for under certain pathophysiologic circumstances, it may also serve as the basis for functional recovery in the event of partial damage. In a kurtosis-based tractography study, it was shown that intensive language rehabilitation (based on confrontation naming) reinforced the microstructural organization of the left ILF. Furthermore, this language rehabilitation was associated with a significant reduction in the naming error rate , suggesting that therapy-induced structural plasticity of the left ILF might be the physiologic basis of semantic recovery in stroke patients .Lastly, a recent study in neurologically intact participants showed that learning Morse code improves the microstructural organization of the left ILF and suggested that the acquisition of a new language skill is underpinned by the neuroplastic potential of this ventral tract . In the Figure 2 for a schematic illustration), which fits well with the tract’s many known or supposed cortical connections. Although some functions of the ILF appear to be relatively lateralized , a few reports suggest that the anterior part can be dynamically compensated for by the contralateral part. This would explain why bilateral disruption of the ILF appears to be an important feature in the emergence of severe neuropsychological impairments, as is notably the case in neurodegenerative diseases  that typically affect the two hemispheres . Abnormalities in the ILF appear also to be an important determinant of cognitive and behavioral disturbances in certain psychopathologic or neurodevelopmental disorders, such as ASD or schizophrenia.Although the investigation of the ILF is still in its infancy, the available literature data nevertheless suggests that this white matter tract mainly supports visual functions; indeed, disruption of the ILF is associated with an array of neuropsychological syndromes that involve the visual modality. The findings reviewed here also suggest that the ILF has a multilayered functional organization  a variety of methodological approaches to generate converging evidence. Furthermore, many of the studies reviewed here suffered from methodological limitations–some of which are specific to DTI methods. These DTI-related limitations  and the quasi-exclusive use of FA as measure of tract integrity) have recently been comprehensively reviewed by Table 1 for a tentative summary of the ILF’s layers possible functions). These hypotheses could be tested by using DTI to track each of the four layers of the ILF and by testing specific anatomic-functional correlations in both healthy and brain-damaged patients. Secondly, it was mentioned above that some functions potentially subserved by the ILF appear to be at least partially lateralized. However, to the best of our knowledge, the relationship between the ILF’s degree of anatomic lateralization and the tract’s specific functions has never been examined. This type of relationship has been demonstrated for other connective tracts and functions, such as branch II of the superior longitudinal fasciculus and spatial cognition (The current review identified a certain number of subjects for further investigation. Firstly, in most of the studies reviewed here, the ILF was considered to be a single-layer white matter tract. However, the results of most recent anatomic dissection and DTI studies e.g.,  suggest ognition . One canognition .In conclusion, the current review is the first to have provided general principles governing the anatomic and functional organization of the ILF. Although this knowledge is still incomplete and must be greatly extended, it may nevertheless form a solid basis for future investigations of the tract’s role in neurocognitive networks.GH, IZ, and HD drafted, revised, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


High numbers of consumable medical materials  are used during the daily routine of intensive care units (ICUs) worldwide. Although medical consumables largely contribute to total ICU hospital expenditure, many hospitals do not track the individual use of materials. Current tracking solutions meeting the specific requirements of the medical environment, like barcodes or radio frequency identification, require specialized material preparation and high infrastructure investment. This impedes the accurate prediction of consumption, leads to high storage maintenance costs caused by large inventories, and hinders scientific work due to inaccurate documentation. Thus, new cost-effective and contactless methods for object detection are urgently needed.The goal of this work was to develop and evaluate a contactless visual recognition system for tracking medical consumable materials in ICUs using a deep learning approach on a distributed client-server architecture.We developed Consumabot, a novel client-server optical recognition system for medical consumables, based on the convolutional neural network model MobileNet implemented in Tensorflow. The software was designed to run on single-board computer platforms as a detection unit. The system was trained to recognize 20 different materials in the ICU, while 100 sample images of each consumable material were provided. We assessed the top-1 recognition rates in the context of different real-world ICU settings: materials presented to the system without visual obstruction, 50% covered materials, and scenarios of multiple items. We further performed an analysis of variance with repeated measures to quantify the effect of adverse real-world circumstances.P<.001; mean recognition accuracy 0.71; SD 0.13) or multiple objects of the target group were present , compared to a nonobstructed view. The approach met the criteria of absence of explicit labeling  while maintaining a high standard for quality and hygiene with minimal consumption of resources .Consumabot reached a >99% reliability of recognition after about 60 steps of training and 150 steps of validation. A desirable low cross entropy of <0.03 was reached for the training set after about 100 iteration steps and after 170 steps for the validation set. The system showed a high top-1 mean recognition accuracy in a real-world scenario of 0.85 (SD 0.11) for objects presented to the system without visual obstruction. Recognition accuracy was lower, but still acceptable, in scenarios where the objects were 50% covered (Using a convolutional neural network architecture, Consumabot consistently achieved good results in the classification of consumables and thus is a feasible way to recognize and register medical consumables directly to a hospital’s electronic health record. The system shows limitations when the materials are partially covered, therefore identifying characteristics of the consumables are not presented to the system. Further development of the assessment in different medical circumstances is needed. A large amount of medical materials are consumed in the daily routine of intensive care units (ICUs). It is estimated that 85% of their healthcare costs are captured by three cost blocks, namely staff, clinical support services, and consumable medical materials ,2. The lDue to the complexity of an ICU treatment, the financing of ICUs is often based on a flat-rate reimbursement scheme , resultiIn some hospitals, this problem is solved by scanning a material-specific barcode when a disposable material is used at the patient site, which enables patient-specific billing. However, this means each article needs to be marked with an individual code. The use of wireless radio-frequency identification (RFID) has been assessed in the circumstances of patient care, however this requires the installation of specialized reader infrastructure and related management systems . HoweverThe aim of this work is to develop and evaluate Consumabot, a novel client-server recognition system for medical consumable materials based on a convolutional neural network, as an approach to solve the above-mentioned challenges in the sector of intensive care medicine. We first described the technical background (hardware and machine learning), taking the specific limitations and challenges of the ICU sector into account. In a proof-of-concept study, we then evaluated the performance of the system in the adverse circumstances of a real ICU environment, assessing the feasibility of the application in a real-world hospital setting. We performed a thorough analysis on the technical, medical, and economic circumstances of ICUs and defined the specific requirements of the system. This included the identification of the need for a visual and contact-free recognition of the consumables, and for detection in proximity to the patient bed to facilitate assignment to the individual patient. Based on these considerations, the distributed concept of Consumabot was developed as follows: multiple low-cost detection units are located close to the patient bed, then these units are wirelessly connected to a local training server with high computational power for model training. This server has a direct connection to the hospital database and the electronic health record (EHR) backend .For the bedside detection units (clients), the commercially available single board computer Raspberry Pi was chosen as the hardware platform. Raspberry Pi is an inexpensive and widespread, small, single-board computer, initially developed to promote teaching of basic computer science in schools and in developing countries. The system architecture was developed at the University of Cambridge and is now being promoted by the charitable foundation the Raspberry Pi Foundation . With loIn this work, we specifically used the Raspberry Pi 3 Model B for the detection units (clients), equipped with a quad-core processor ARM Cortex A53, a network and wireless network card, and one gigabyte of random accessible memory. The official camera module of the Raspberry Pi Foundation version 2.1, with 8-megapixel resolution b, as welConsequently, a more computationally capable training server was set up. This server was equipped with an Intel Xeon Gold 6140 processor with four dedicated processor cores, had 40 gigabytes of storage space on a single-state hard disk and had 320 gigabytes of storage space on a conventional magnetic hard disk for the resulting training data. Both the recognition module and the server used the open source operating system Linux in the Debian distribution .NumPy, used in data preprocessing, scikit-learn, used for data mining, or Keras as a high-level neural network interface. For modelling the machine learning backend, we adapted the model of a convolutional neural network (CNN) [TensorFlow, a software framework that simplifies the programming of data stream–centered procedures [The software backbone of Consumabot was developed in Python, a programming language often used in the field of machine learning. One major advantage of Python is the availability of a wide range of machine learning tools like rk (CNN) . The conrk (CNN) -16. Howeocedures , and sevocedures . Since tocedures . In tranIn the first step of the training the bottlenecks were generated, and they are the layer of the network that is located directly below the output layer . Since estore in database button on the screen, the material was then registered in the database of the training server. This resulted in an additional confirmation step of human classification, as only images confirmed to have been classified correctly were included in further training. To facilitate online learning, at regular intervals the stored images of correctly recognized materials were transferred as training data to the training server and the model of the neural network was trained. The resulting model (the retrained graph) was distributed among the recognition units, which enabled an improved recognition of the desired consumables. Finally, the database of the control server was used to further process the data for either analysis or optimization.The internal structure of the system must take the particularities of the ICU environment into account. The detection module's camera took a picture every second and stored it on a memory card. This image from the camera was presented to the trained model of the recognition unit, which then predicted the probabilities of the recognized objects. If anything other than an empty surface was detected then the recognized object with the greatest probability was selected and presented to the user. After pressing the After finishing the training, we installed the hardware on an ICU within the University Hospital Aachen. Testing the system in real ICU conditions is obligatory due to the specific lighting and environmental conditions. In a test series, the 20 objects specified in Each material was presented 10 times to the system. An object was classified as correct if it correctly appeared on the screen as the most probable classification (top-1 accuracy).A full video of the hardware setup and training process can be found in We performed a repeated measures, one-way analysis of variance (ANOVA) with Geisser-Greenhouse correction and a Tukey’s multiple comparisons test with individual variances computed for each comparison. This was performed to assess the effect of adverse conditions in scenario two and three in comparison to the nonobstructed view in scenario one. All calculations were performed using GraphPad Prism 8.1.2 .Disposable bag valve maskAmpouleAuraOnce laryngeal maskBerotec inhalatorHand disinfection bottleDocumentation sheetBoxed DressingsPackaged Gauze bandageUnpackaged Gauze bandagesGelafundin infusion solutionIntravenous access orangeTube set for infusion solutionsIntravenous access greyBraun sterile syringeGreen Molinea protective padWhite Protective padOxygen maskOxygen tubing for maskInfusion solution SterofundinEmpty scenario (reference)We trained the system in the context of a real ICU, taking special lighting conditions and other circumstances into account. We randomly chose a total of twenty common medical consumables from diverse categories with various sizes and formats to train the system on-site . An emptThe top layer of the CNN received a 1001-dimensional vector as input for each image, and we trained a softmax layer on top of this vector representation . Assumin) is obtained and compared to the appearance of the top classification as the target label (having the highest probability). The top-1 score is then computed as the times a predicted label matched the target label, divided by the number of data points evaluated. Selecting the correct model also needs to take the computational requirements into account, as the computational power of the recognition module is limited. Comparing MobileNet, AlexNet, GoogleNet and VGG16, we decided to apply a MobileNet, a class of efficient models for mobile and embedded vision apps, as a compromise between low requirements for computational power and high accuracy in image classification [For choosing the appropriate network, we took the recognition accuracy of different convolutional neural networks into account. For this purpose, we used the top-1 score . Brieflyfication .Like all common machine learning techniques, the used model also requires a cost function that has to be minimized. We used cross-entropy as a cost function :We applied this function to quantify the difference between the two probability distributions of the training and the validation set. As shown in The fully trained model  is provided in We then performed the on-site study at an ICU at the University Hospital Aachen, taking the particular lighting conditions and adverse circumstances due to clinical workflow into account. Each of the 20 consumable materials were presented ten times to the detection unit and were classified as correct if they appeared correctly on the screen with the highest associated probability . The datIn nonobstructed visual conditions, the model showed a good recognition performance, with a mean recognition accuracy of 0.85 (SD 0.11). Materials with large surface areas and many distinguishable visual features  had particularly good detection rates. For materials only distinguishable by color  Consumabot showed lower recognition accuracies for the grey IV access (mean 0.8) and the orange IV access (mean 0.6) , Table 1In a scenario where the surface area of the material was 50% covered, the system showed a lower, although still acceptable, mean recognition accuracy of 0.71 (SD 0.13). This was particularly true for materials with a small surface or with less distinguishable features , where the recognition accuracy dropped by 0.3 between when it was uncovered (mean 0.9) to when it was covered (mean 0.6).Assessing the performance of the system in a scenario with multiple elements present in the scene resulted in a mean recognition accuracy of 0.78 (SD 0.11). For small elements compared to the secondary material present in a scene, this mostly resulted in a drop in recognition accuracy .We also performed an ANOVA with repeated measures to quantify the effect of the different scenarios, representing adverse real-world circumstances. Results of the ANOVA and other statistical analyses are given in P<.001; R2=0.46), and post hoc analyses with Tukey's multiple comparisons test showed a significant difference between the noncovered cohort and the partially covered cohort . Further significant differences between the noncovered scenario and the scenario with multiple consumables were also observed , however, the differences between the noncovered group and multiple consumables were not statistically significant (P=.06).Independence of the observations among the groups, no sphericity, and a normal distribution were assumed for the analysis. The results of the performed ANOVA showed significant differences between the groups  since they did not show fully satisfactory recognition rates. This occurred if the distinguishing features were not clearly visible, partially covered, or multiple consumables were present in the scene. To solve this problem, a user training course with a note that identifying features must be clearly presented is recommended. Further, the model performance is likely to increase with added training data during daily use in the ICU due to the implemented feedback mechanism of Consumabot. Further development of the software and assessment of its performance with larger sets of medical consumables is desirable. The presentation of an object unknown to the detection unit results in a prediction with low confidence. In our prototype we display this confidence factor see  to the uOverall, though, the results were satisfactory enough to promote the further use and development of Consumabot in practice and research. The system fulfilled the requirements for recognizing materials without explicit labelling while maintaining the standard for quality and hygiene of ICUs. The system will make retrospective data analysis  considerably easier and enable time-critical research with direct correlation between action and reaction. The prototype’s capabilities could potentially be enhanced by the integration of visual, multi-object detection algorithms, thus enabling it to detect a multitude of objects in parallel. Further, the need for tactile manual confirmation could be reduced by the integration of a microphone array to enable voice commands. The full source code of the detection unit, the pretrained model, and the training script have been released under the open source license Apache Version 2.0, January 2004 , and det


Quantum key distribution (QKD) generates symmetric keys between two authenticated parties with the guarantee of information-theoretically security. A vital step in QKD to obtain fully-matched key between two parties is information reconciliation. The blind reconciliation protocol provides a useful tool that corrects the mismatch in a wide range of qubit error rate (QBER) but without a prior error estimation. However, there is a contradiction between the reconciliation efficiency and the processing time in this protocol. In this work, we propose a blind reconciliation protocol with variable step sizes to relieve this contradiction. The analysis and simulation results show that the improved protocol inherits all the advantages of the original blind reconciliation protocol and can obtain better reconciliation efficiency with less operation time. The improved blind reconciliation protocol enhances the final secret key rate and accelerates the processing speed of a QKD system. QKD is proved to be information-theoretically secure owing to the solid foundation of quantum physics without computational assumptions5. The technology of QKD has been developed quickly in the past three decades and has achieved remarkable milestones. For example, QKD products has been commercialized with growing market6; QKD networks has been constructed in several countries around the world9; a QKD satellite realizes secure communication in global scale11. Thus, QKD is one of the most mature fields in quantum information.Quantum key distribution (QKD)12. At the first phase, Alice and Bob share the raw key by transmitting quantum states prepared by Alice through a quantum channel and measuring them by Bob. Then, sifting as the first step of classical post-processing helps Alice and Bob to maintain the cases that they are using the matched bases. The raw key is obtained after this step. However, the raw key shared between Alice and Bob may contain errors due to the channel noise and adversary’s attacks. To eliminate the mismatch of the raw key between Alice and Bob, the system runs information reconciliation to guarantee that the same string of key shared at both sides. The reconciled key maybe partially correlated with an adversary, since the adversary can interact with the quantum states during the raw key exchange via quantum channel and listen to the public information during the information reconciliation via the classical channel. Thus, privacy amplification is applied to remove the leaked information, thereby obtaining the final secret key share between Alice and Bob. The sifting, information reconciliation, and privacy amplification are called as the post-processing phase.In the implementation of QKD, a system operates two main phases to establish the shared key – the phase of quantum raw key exchange and the phase of classical post-processing13. Regarding information reconciliation, a commonly used method is Cascade protocol, which identifies the position of errors via dichotomizing search in each block14. Although Cascade protocol is simple to be implemented with relative high reconciliation efficiency, this protocol requires many rounds of communication between Alice and Bob. Thus, this information reconciliation protocol is time-consuming and occupies significant amount of communication resources in a QKD system. To reduce the rounds of communication, Winnow protocol is proposed, which uses Hamming code instead of a simple parity check to identify and correct errors15. However, Hamming code only can correct 1-bit error in each block, and its efficiency is much lower than the Shannon limit.In the post-processing, it is obvious that information reconciliation is a vital step to eliminate the mismatch, which is also called error bits, in the raw key to guarantee that Alice and Bob share the same key17 is proposed to be used for the information reconciliation in a QKD system to reduce the rounds of communication and achieve high reconciliation efficiency19. Since LDPC code is able to correct multiple-bit errors, its reconciliation efficiency is close to the Shannon limit. Furthermore, only one round of communication is necessary to transmit the syndrome. Hence, the party (e.g. Alice or Bob) who receives the syndrome can use it to correct the errors. In practice, quantum channel is varying over time, and thus quantum bit error rate (QBER) is also changing20. In order to adapt to the different QBER, two techniques, puncturing22 and shortening23, are applied to the LDPC code to correct errors in one round of communication. This is called rate-adaptive information reconciliation24.Low-density parity-check (LDPC) codeblindly without information of QBER28. Because of no information about the QBER, the blind reconciliation protocol allows multiple rounds of communication to try to correct the errors by assuming different error rate in each round. The specific procedure of the blind reconciliation protocol is as follows. In the first round of communication, Alice assumes the minimal error rate and only transmits the syndrome to Bob, and all the auxiliary bits are punctured. If the reconciliation fails, Alice reveals a small amount of punctured bits, turning them to be shortened bits, which helps Bob to correct errors. With more rounds the protocol operates, more shortened bits are known by Bob. Until Bob successfully corrects all errors or the maximum of rounds reaches, the protocol ends. This protocol does not need the error estimation as prior information and can achieve high average efficiency26.Based on the rate-adaptive information reconciliation, blind information reconciliation protocol is suitable to the situation that the QBER is unknown, in which the reconciliation procedure is processed However, there is a trade off between the reconciliation efficiency and the time consumption of this protocol. On the one hand, the more rounds of interactivity allowed between Alice and Bob, the higher reconciliation efficiency can achieve. On the other hand, more rounds of interactivity take more time to complete the protocol. Practically, in a QKD system, the time consumption of the post-processing significantly affects the secret key rate. If the post-processing consumes much more time than the raw key exchange phase, the secret key rate will be limited by the speed of post-processing and thus cannot obtain high key rate.To relieve the contradiction between the reconciliation efficiency and the time consumption, we propose a blind information reconciliation protocol with variable step sizes. Different from the original protocol, the proposed protocol gradually increases the amount of the shortened bits revealed in each round, rather than reveals constant amount of the shortened bits in each round. In this way, the proposed protocol can achieve better reconciliation efficiency while incurring similar or even less time consumption to complete the error correction. More specifically, the simulation results show that the proposed protocol can achieve improvement of the reconciliation efficiency while consuming less iteration time. Besides, by using a decoy-state BB84 QKD system, we demonstrate that the proposed protocol can improve the secret key rate. This is useful in practice when the QBER is not known in advance. In short, this protocol inherits all the advantages of the original blind information reconciliation protocol and can provide better reconciliation efficiency with less operation time than the original one.The paper is organized as follows. In Sec. 2, we introduce the protocol of blind information reconciliation with variable step sizes. The corresponding simulation results that compared to the original blind information reconciliation protocol are shown in Sec. 3. We further analysis the effect of the improved reconciliation efficiency owing to the variable step sizes on the secret key rate of QKD in Sec. 4. The conclusion is drawn in Sec. 5.Blind reconciliation can cover a wide range of QBER by using only one LDPC code. This is due to the technique of rate-adaptive reconciliation. Thus, in this section, we first describe the working principle of rate-adaptive information reconciliation. Based on this, we introduce the protocol of blind reconciliation with variable step sizes that we propose.R only can correct the error in a certain range17. Therefore, multiple LDPC codes are needed to cover a wide range of error rate. However, this solution may be impractical in the hardware implementation of a QKD system, because it occupies memory space to store copies of the LDPC codes. Thus, an ideal solution would be using a single LDPC code whose code rate can be adjusted in order to adapt to a wide range of error rate. This solution is called rate-adaptive information reconciliation, in which the adaptable code rate is realized by the techniques of puncturing and shortening24.For a given LDPC code p symbols from the code words, and thus the LDPC code becomes 22. The code rate is increased to be X with length 31. The dark count rate In order to show the effect of the improved efficiency provided by the modified blind reconciliation protocol on a QKD system, we simulate the final secret key rate of a decoy-state BB84 QKD system with the typical reconciliation efficiency obtained from the blind reconciliation protocol with shortened bits The simulation results are shown in Fig. i disclose to Bob in the ith iteration is related to i instead of a fixed number as in the original protocol. This indicates the idea of variable step sizes.Information reconciliation is an important step in the post-processing of QKD. The information reconciliation protocol based on LDPC codes is becoming popular because of its strong capability of error correction. As required by practice, blind reconciliation protocol can correct the error without estimating the QBER but still obtain good reconciliation efficiency. However, the blind reconciliation protocol runs multiple rounds of communication between Alice and Bob to correct all errors, which consumes large amount of communication resources. Thus, reconciliation efficiency is a key parameter in QKD, and the speed of reconciliation is the bottleneck of the system’s repetition rate. In this work, we propose an improved blind reconciliation protocol with variable step sizes that reveals more shortened bits than that in the last round, increasing the information of the code word to help Bob correct his errors. The major modification of the improved reconciliation protocol is to set the number of shortened bits Δi) according to the error correction conditions of previous rounds in a more sophisticated way may be needed. This optimized protocol can be our future work. Moreover, the blind information reconciliation protocol with variable sizes may combine with the methodology of symmetric blind information reconciliation proposed in ref. 28 that improves the reconciliation efficiency by disclosing the positions of additional shortened bits decidedly indicated by unsuccessful belief propagation decoding algorithm. The reconciliation efficiency may be further improved by leveraging these two ways of the information leakage reduction. The investigation of this methodological combination can also be an interesting future work.The variable step sizes relieve the conflict of the reconciliation efficiency and the processing time. As shown by the analysis and simulation results in above sections, in the range of relative low error rate for a LDPC code, the improved blind reconciliation protocol discloses less shortened bits in the first several iterations, thus reaching better reconciliation efficiency than the original blind reconciliation protocol. In the range of relative high error rate, the proposed blind reconciliation protocol with variable step sizes reveals the shortened bits within less iterations than the original one to help Bob reconcile his bit strings, which takes less operation time and accelerates the speed of post-processing. In the application of the blind information reconciliation where the QBER is not known in advance, the advantages of our proposed protocol are obvious. We also show that the modified blind reconciliation protocol can provide a better reconciliation efficiency in QKD system, which enhances the final secret key rate of a decoy-state BB84 QKD system. To further achieve a even better reconciliation efficiency for the improved protocol, a protocol that can choose the size of step (Δ


Probe-based confocal laser endomicroscopy (pCLE) enables in vivo, in situ tissue characterisation without changes in the surgical setting and simplifies the oncological surgical workflow. The potential of this technique in identifying residual cancer tissue and improving resection rates of brain tumours has been recently verified in pilot studies. The interpretation of endomicroscopic information is challenging, particularly for surgeons who do not themselves routinely review histopathology. Also, the diagnosis can be examiner-dependent, leading to considerable inter-observer variability. Therefore, automatic tissue characterisation with pCLE would support the surgeon in establishing diagnosis as well as guide robot-assisted intervention procedures.The aim of this work is to propose a deep learning-based framework for brain tissue characterisation for context aware diagnosis support in neurosurgical oncology. An efficient representation of the context information of pCLE data is presented by exploring state-of-the-art CNN models with different tuning configurations. A novel video classification framework based on the combination of convolutional layers with long-range temporal recursion has been proposed to estimate the probability of each tumour class. The video classification accuracy is compared for different network architectures and data representation and video segmentation methods.We demonstrate the application of the proposed deep learning framework to classify Glioblastoma and Meningioma brain tumours based on endomicroscopic data. Results show significant improvement of our proposed image classification framework over state-of-the-art feature-based methods. The use of video data further improves the classification performance, achieving accuracy equal to 99.49%.This work demonstrates that deep learning can provide an efficient representation of pCLE data and accurately classify Glioblastoma and Meningioma tumours. The performance evaluation analysis shows the potential clinical value of the technique. Biophotonics techniques such as probe-based confocal laser endomicroscopy (pCLE) have enabled direct visualisation of tissue at a microscopic level, with recent pilot studies suggesting it has a clear role in identifying residual cancer tissue and improving resection rates of brain tumours . HoweverState-of-the-art computer-aided diagnosis (CAD) systems designed for the analysis of pCLE data have mainly focused on the classification of colonic cancers. A content-based image and video retrieval framework based on the Bag-of-visual Words (BoW) approach has been proposed in  for the Recently, approaches to content-based image retrieval of pCLE data have focused on learning discriminative visual features to improve the retrieval accuracy. In , a MultiWan et al.  extendedThe above CAD systems are composed of three main steps, namely visual feature extraction, feature encoding, and supervised classification. The performance of these systems relies on the design of handcrafted morphological or textural descriptors, capable of extracting discriminative image features which could facilitate the classification task. However, the selection of efficient features is problem-dependent and it is not easy to find a generic feature extraction method which can perform equally well in different applications. Hence, the above CAD systems can have significant performance variations depending on the data and the surgical context.Recently, significant progress has been made in image feature extraction and representation, mainly due to the revival of deep learning models such as convolutional neural networks (CNN). These architectures enable the learning of data-driven and highly representative image features from a large set of training data. This alleviates the need to design explicit handcrafted image features and also allows the feature extraction, encoding and classification tasks to be done within the optimisation of the same deep architecture. Hence, the network performance can be easily tuned, in a systematic fashion.The success of deep learning models in general computer vision tasks has motivated their application to medical image analysis . More spAn efficient representation of the context information of pCLE images by exploring state-of-the-art CNN models and proposing different tuning configurations. The best configurations have been selected and used to classify image and video data into the above tumour types.A novel video classification framework based on the combination of convolutional layers with long-range temporal recursion to estimate the probability of each tumour class.A set of network architectures and video segmentation methods which has been combined with different pCLE data representations to increase the video classification accuracy.The proposed framework has been validated on ex vivo pCLE data and has been compared to state-of-the-art brain tumour classification approaches. The performance evaluation analysis shows the potential clinical value of the proposed framework.The aim of this paper is to propose a brain tissue characterisation framework for context aware decision support in neurosurgical oncology. This will be part of a platform which can extract, interpret and use context information to provide the surgeon with a set of clinical actions that can be made in the current situation and guide intraoperative resection. The context in our case refers to the discriminative information which is extracted from the pCLE data and is used to characterise the state of the tissue. In particular, we focus on the classification of brain tumours into Glioblastoma and Meningioma which are the most common malignant and benign tumours, respectively, in neurosurgery. Our classification is based on the analysis of endomicroscopic data, and we propose two deep learning-based frameworks for the classification of image and video pCLE data. To the best of our knowledge, this is the first work on brain tissue characterisation based on deep learning. The novel aspects of our work include:In this work, we propose two deep learning-based frameworks for brain tissue characterisation based on the classification of image and video pCLE data into two tumour types, namely Glioblastoma and Meningioma.L is the number of layers, Our image-based tissue characterisation approach employs convolutional neural networks for context representation and classification of pCLE images. A CNN for classification can be thought of as the composition of a number of convolutional functions In this work we employ three state-of-the-art CNN architectures namely, the AlexNet , the VGGUpon initialization, a CNN is simply a function which through numerous operations transforms highly dimensional data into several numerical values which at this point are random. It is through training that the model function feature extractor, we randomly initialise the weights and biases of several of the 8 layer groups and then train those variables as usual. More specifically, we tune the first n layers are initialised randomly, with the weights sampled from Gaussian distributions and the biases set to a constant value. Another approach is the fine-tuning, where we initialise the weights and biases of some trainable layers from pre-trained models and then fine-tune those values using a smaller learning rate. Similar to the above tuning approach, we copy to the first n layers, we transfer the pre-trained values and update them at a learning rate equal to the 1/10th of the pre-training rate [n is here referred as the tuning depth. Since the Inception network has a different architecture, we retrain only the final fully connected layer which corresponds to tuning depth In the first approach, called fc8 has been modified to fit our binary classification task. So, the last layer is always randomly initialised in both approaches. Training the CNN from scratch is a special case of the feature extractor method where we optimise all the layers. The case of fine-tuning all the layers is covered when we use the fine-tuning approach with tuning depth For the last layer of both architectures, we cannot use the values from pre-trained models since For the classification of video pCLE data, we use a deep learning architecture which combines convolutional layers and long-range temporal recursion. Specifically, we extract visual features from a set of consecutive endomicroscopic images using a CNN network and then feed the ordered feature sequences to a recurrent neural network (RNN) to perform classification. At the end of the network, a fully connected layer with softmax is included to generate a prediction about the tumour class. An outline of the proposed architecture is shown in Fig. Standard LSTM with 2 LSTM layers, the Wide LSTM with only one but wider layer and the Deep LSTM of 3 layers as shown in Fig. To extract visual features from the pCLE images, we employed the same CNN models that were used for image classification. For temporal recursion, we use the long short-term memory (LSTM) network . We explBefore-Final-fc (BFF) and After-Final-fc) (AFF). In the former configuration, we pass to the LSTM network the input of the final fully connected layer. This is a feature vector of length 2048, 4096, 4096 for the Inception network, the AlexNet and the VGGNet, respectively. In the latter configuration, the input of the LSTM network is the output of the last fully connected with softmax layer of the CNN network which is a vector of size equal to the number of classes in the classification task and represents the predictions of the classes.In this work, we test two different configurations to combine the CNN and the LSTM networks, namely the The above architecture is different to the long-term recurrent convolutional networks proposed in  as our vIn this section, we first describe the preprocessing steps that we apply on our endomicroscopic data. Then we evaluate the performance of the proposed image and video classification methods.The validation of the proposed classification framework is based on ex vivo pCLE data of Meningioma and Glioblastoma brain tumours, collected during brain tumour resection procedures at the Merheim Hospital in Germany. The examined tissue samples were scanned in the surgical theatre next to the patient, immediately after removing the tumour. As contrast agent, acriflavine hydrochloride AF from Sigma Pharmaceuticals, Victoria, Australia, was used. The data were collected using the Cellvizio system  and were classified into the above two types by expert histopathologists. Each pCLE video represents one tumour type and corresponds to a different patient. Our dataset is a subsetFor the video classification approach, to expand the dataset for both training and validation, we segment the videos into short video clips. The proposed video classification framework is tested using overlapping and non-overlapping video clips. In the non-overlapping case, no frame appears in two video clips at the same time as shown in Fig. To evaluate the performance of the different models and configurations proposed in this work, we split the dataset into 3 parts, namely the training set (66%), the validation set 17%) and the test set (17%). The training set is used to fit the parameters of each model while the validation set to compare their performance and decide which model to select. The performance of the selected models is then assessed on the test set. To ensure independence of each set, we perform patient-level (i.e. video-level) splitting and no frame from one set ends up on the other sets. To eliminate data splitting bias we employ sixfold cross-validation, where the test and validation sets are selected such that they are different for each run, while the rest of the dataset is used for training. The size of the test set for each fold during image and video classification is shown in Table 7% and thfeature extraction method, while for the fine-tuning it is 10 times smaller, i.e. 0.001. Our network minimises the cross-entropy loss function during the training optimisation process. For the Inception network, we set the number of training iterations to 4000. The last layer is retrained using the gradient descent optimiser with learning rate 0.01, batch size 100 and cross-entropy as the loss function. We use the TensorFlow framework to train our models.The hyperparameters we apply when training our models are those used for AlexNet in . The AleAlexextractAlextuneAlextuneVGGtuneVGGtuneInceptiontuneTo constrain the set of classification models for validation, we first assess the performance of all possible combinations of tuning methods and tuning depths proposed in “Image classification” section without cross-validation, to select the best classifiers. We refer to tuning depths feature extractor with tuning from conv4 to fc8, achieving 97.97% classification accuracy on the test set. For the VGGNet, the fine-tuning with tuning from fc6 to fc8 is the best with 98.51% classification accuracy on the test set. The Inception network has the lowest accuracy, equal to 96.61%. It is also clear from Fig. VGGtuneThe performance of the selected models on the validation and test datasets is shown in Table n, the features tend to be more low-level. The above results indicate that model tuning of medium depth can generate discriminative features and provide an efficient representation of our pCLE data. This enables the selected classifiers to recognise the delicate difference between the two tumour types and generate accurate predictions. In our classification task, deep tuning results in lower classification accuracy possibly due to the relatively small size of our training dataset. In the above analysis, we observe that the AlexNet works better with the feature extractor tuning method while the VGGNet achieves more accurate results using the fine-tuning method. This shows that loading pre-trained weights helps the convergence of deeper and complex networks. Since pCLE data are very different from the dataset used for pre-training, random initialisation can generate efficient data representation when used with simple networks such as the AlexNet.In general, as we increase the depth of tuning AlexextractVGGtuneInceptiontuneTo extract visual features for video classification, we selected the CNN models that performed the best in the image classification task, namely the m method  for optim method . The traStandard LSTM, Wide LSTM, Deep LSTM) result in 36 models which are evaluated on the validation dataset and compared in Fig. AFFoverlapping seems to perform the best and for the VGGNet model with deep LSTM achieves 99.49% accuracy on the test dataset. The superior performance of our video classification framework verifies that temporal information assists the classification of pCLE data.For evaluation, we use fixed-length video clips, 48 frames long and report the accuracy of classifying video clips rather than entire videos. The different CNN-LSTM combination approaches (BFF and AFF), video segmentation methods (overlapping and non-overlapping video clips) and LSTM configurations  runs at about 1 s.In this work, we have proposed a deep learning-based brain tissue characterisation framework which can classify brain tumours into Glioblastoma and Meningioma based on the analysis of pCLE image and video data. For image classification, an efficient representation of the context information of pCLE images has been proposed by exploring different CNN models and tuning configurations. Our video classification is based on a novel combination of convolutional layers with long-range temporal recursion to estimate the probability of each tumour class. The proposed deep learning-based image classification approach with 98.51% classification accuracy outperforms state-of-the-art feature-based brain tumour classification methods. The use of video data further improves the classification performance with accuracy equal to 99.49%. These results verify the potential of deep learning-based methods in classifying pCLE data, with significant gain in classification accuracy. The focus of our future work is to build up a decision support platform which can characterise different states of brain tissue. For this purpose, more brain tissue classes will be examined with pCLE data collected during intraoperative tissue scanning using a contrast agent which has been approved for use in humans.


Optimal shoulder images must adhere to specific radiographic criteria before they are sent to the radiologist for reporting. Repeat x-rays of the shoulder may increase radiation exposure to the patient.The aims of this study were to determine whether images adhered to the required radiographic criteria for routine shoulder imaging and to identify possible reasons for non-adherence.The study was conducted at an imaging department at a tertiary academic hospital in Bloemfontein, South Africa.A criteria checklist compiled from literature was used to evaluate 578 routine shoulder images including anteroposterior (AP) with external rotation and lateral-Y (LAT-Y) projections. The checklist determined whether the shoulder images adhered to the criteria with regard to the anatomy included, positioning and technical factors, such as inclusion of the correct anatomical lead marker. Data were analysed using SAS Version 9.2 statistical software.More than 80% of the AP external rotation images included unnecessary anatomical structures owing to incorrect centring. In four out of seven criteria pertaining to positioning for AP external rotation imaging, at least 70% of images were performed incorrectly. Four-sided collimation was not present in more than 50% of both AP external rotation and LAT-Y images because of incorrect centring, while more than 30% of shoulder images presented with anatomical digital markers.The application of criteria required for shoulder imaging must be addressed at the participating imaging department to improve overall patient care. An in-service training session is recommended to enhance the radiographic technique with regard to routine shoulder projections. The quality of radiographic images plays an integral role in the diagnosis and subsequent care of patients Hobbs . TherefoWith x-ray projections of the shoulder, the most sensitive regional organs that may receive scatter ionising radiation are the thyroid gland and the breast. Radiographers are responsible for patients with regard to radiation protection during radiographic imaging , reduces the dose to the patient  before exposure may have medico-legal implications  benchmark from literature the radiographic criteria for routine anteroposterior (AP) projection  and lateral-Y (LAT-Y) projection of the shoulder and (2) compile the radiographic criteria checklist based on the literature review to identify causes contributing to images failing to meet the requirements.The study was approached from the pragmatic paradigm focussing on problem-solving. This paradigm was chosen because of the problem observed, and specific methodologies were used to provide solutions to address the problem at hand Hall . A descrThe checklist used in this study consisted of radiographic criteria used to formulate an ‘opinion or judgment about a particular practice’; in other words, to retrospectively critique the routine shoulder images acquired by the radiographers at the participating imaging department. By selecting ‘yes’ or ‘no’, the researcher could determine if the shoulder projections adhered to the requirements as stipulated in the checklist  visibility of the correct anatomical lead marker and (b) collimation. When the correct centring point was used and collimation applied prior to exposure, clear four-sided collimation borders were visible on the AP external rotation and LAT-Y images  were not evaluated because radiographers commonly use post-processing tools to ‘fix’ the images, such as altering collimation before sending the images to the PACS.The shoulder images that were evaluated included 578 AP external rotation and 578 LAT-Y projections. Every time the researcher collected data at the participating imaging department, a note was made of the display monitor used to search for routine shoulder projections, the patient’s name and the date of the examination. The notes made were for the researcher’s own use to assist her to keep track of data collection equipment used, data collection start and end points.The data from the hard copy checklist were captured electronically in two Microsoft Excel spreadsheets for the AP external rotation projection and the LAT-Y projection, respectively. Additional comments were documented and all comments showing similarities to reason why images did not adhere to the criteria were used to formulate sub-sections  to facilData analysis was done using the SAS Version 9.2 statistical analysis software . Descriptive statistics, namely frequencies and percentages, were calculated for categorical data. Means and standard deviations or medians and percentiles were calculated for numerical data.The researcher obtained ethical approval (ECUFS 100/2015) from the Research Ethics Committee of the Faculty of Health Sciences, University of the Free State, the Department of Health in the Free State, the head of Clinical Services and the director of the participating imaging department. No patients were directly involved in the study; therefore, informed consent was not required. No names of the patients and the participating imaging department, where the data had been collected, were mentioned or used in the study, which ensured anonymity and confidentiality. All information collected was managed in a strictly ethical and confidential manner.The results are presented in accordance with the anatomical structures that must be included for the AP  and LAT-Y projection, positioning criteria and technical considerations that must be considered during positioning and the most evident reasons for non-adherence of radiographic criteria requirements. For each criterion used, the analysis will be first for the AP  projection and then the LAT-Y projection of the shoulder.The AP external rotation projection has to adhere to specific criteria in relation to positioning . Figure Specific criteria for positioning are required for LAT-Y projections . Figure Four-sided collimation is visible when the primary beam is limited and correct centring has been applied to include only the anatomical structures of interest. The lead researcher added comments in the open-ended sections of the checklist when routine shoulder images did not adhere to the radiographic criteria requirements. Various reasons for non-adherence were identified. A notable finding was that most of the LAT-Y shoulder projections showed inclusion of the correct anatomical structures, with exception of the proximal humerus. As shown in The results depicted in Overall, it appears as if the radiographers did not rotate the patient’s arm externally to demonstrate the GT in profile, and the LT between the GT and humeral head. Furthermore, 89% of AP external rotation projections demonstrated incorrect centring. The radiographers either centred too inferiorly or too medially to the correct centring point. Consequently, structures such as the inferior angle and middle of the scapula were at the centre of the collimation field instead of the GH joint and coracoid process . Hence, The LAT-Y shoulder projections adhered to more than 70% of the documented positioning criteria, except for humerus and scapula superimposition and centring . Less thThe relationship between the humeral head and the glenoid cavity  will not be visible on a LAT-Y shoulder projection if the patient presents with a dislocation  when considering radiation exposure during imaging. Radiographers must demonstrate optimal collimation practices at all times that contribute to the ALARA principle and provide optimal x-ray images , in 4% of AP external rotation shoulder projections, collimation was applied only superior and inferior to the ROI . HoweverThe results on the use of anatomical lead markers showed that the radiographers employed anatomical lead markers for more than 60% of routine shoulder projections . It was The non-adherence to the radiographic criteria of the shoulder images could be because of the level of training of the radiographers. The images were obtained by student , community service, supplementary and qualified radiographers (refer to ‘Sample selection’ section) that have different levels of training. Moreover, the experience of the radiographers in imaging of the shoulder among the aforementioned radiographers also differs. Some were exposed more readily than others to imaging of the shoulder and could learn to adjust their radiographic technique in obtaining optimal images of the shoulder in different cases.Student, qualified, community service and supplementary radiographers are compelled to provide quality care to all patients and have the best interest of the patient at heart, as outlined by the South African Health Professions Act No. 56 of 1974 . Two participants (radiologist and orthopaedic surgeon) as part of the pilot testing phase indicated that the whole scapula should be included for the AP external rotation projection, as the participating imaging department is exposed to trauma cases and does not want to miss underlying pathologies. However, the checklist was not amended accordingly by the researcher; thus, it was a limitation in the study.Nevertheless, based on the findings of the study, the lead researcher has a responsibility to recommend an in-service training session at the participating imaging department for the improvement of radiographic technique with regard to routine shoulder projections at the participating imaging department. The researchers strongly recommend that a pre- and post-intervention research study with an in-service training session as intervention on the topic at hand be executed in another study, to determine if such a session would improve radiographers’ practice regarding routine shoulder imaging. The radiographic criteria checklist can also be used by other imaging departments to determine if the images obtained adhere to the specific requirements.