Diagnostic support tools based on artificial intelligence (AI) have exhibited high performance in various medical fields. However, their clinical application remains challenging because of the lack of explanatory power in AI decisions (black box problem), making it difficult to build trust with medical professionals. Nevertheless, visualizing the internal representation of deep neural networks will increase explanatory power and improve the confidence of medical professionals in AI decisions. We propose a novel deep learning-based explainable representation “graph chart diagram” to support fetal cardiac ultrasound screening, which has low detection rates of congenital heart diseases due to the difficulty in mastering the technique. Screening performance improves using this representation from 0.966 to 0.975 for experts, 0.829 to 0.890 for fellows, and 0.616 to 0.748 for residents in the arithmetic mean of area under the curve of a receiver operating characteristic curve. This is the first demonstration wherein examiners used deep learning-based explainable representation to improve the performance of fetal cardiac ultrasound screening, highlighting the potential of explainable AI to augment examiner capabilities. With the rapid development of tools to support medical diagnosis using artificial intelligence (AI), expectations from AI have been increasing continuously ,2,3,4,5.In the present study, we employ the third approach, i.e., interpretable models. One reason for its choice is that the performance of conventional AI is already high, and we can accept slight performance degradation. The second reason is that our purpose of developing AI diagnostic imaging support technology is not to improve the performance of the technology alone, rather to enhance the performance of medical professionals using this technology. A more sophisticated explainable representation has the potential to enhance the performance of medical professionals. Therefore, we propose a novel interpretable model targeting videos of fetal cardiac ultrasound screening, one of the crucial obstetric examinations; however, its detection rate of congenital heart diseases (CHDs) remains low ,26,27. THowever, studies on the comparison or collaboration between AI and humans are vital to obtain insight into the clinical implementation of AI, and many studies have been conducted in this regard ,31,32,33In this study, we attempted to verify whether the deep learning-based explanatory representation “graph chart diagram” could enhance the detection of CHD anomalies for 27 examiners . Quantitative evaluation using the arithmetic mean of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve showed that the screening performance was improved by utilizing the graph chart diagram in all groups: expert, fellow, and resident groups. This is the first report to demonstrate improved screening performance for CHD using explainable AI, and it presents a new direction for the introduction of explainable AI into medical testing and diagnosis.® E8 or E10 ultrasound machine  at the four Showa University Hospitals  in an opt-out manner. The probe was an abdominal 2–6 MHz transducer, and a cardiac preset was used.The total dataset used in this study consists of 160 cases and 344 videos (18–34 weeks gestation). We used 13 CHD cases and 26 videos as the abnormal data, which has been confirmed by postnatal testing. We used only normal data from 134 normal cases (292 videos) to train DNNs explained in Komatsu et al.  showed tWe can address this problem of barcode-like timelines using graph chart diagrams. A graph chart diagram represents the substructure detection status converted from a barcode-like timeline into a trajectory of points in two dimensions using an auto-encoder with two neurons in the intermediate layer . The trajectory on the graph chart diagram is expected to have a constant shape regardless of the probe movement if it is not an abnormal video. The examiner can determine whether the patient has CHD by assessing the deviation of the shape from the normal one, without specifying the diagnostic plane. The computational time to create and display a graph chart diagram is real time. For more details, A simple auto-encoder can form graph chart diagrams by learning from the normal data; however, we propose view-proxy loss and a cascade graph encoder to improve graph chart diagrams for practical applications. The view-proxy loss prevents learning instability due to the “entanglement” of graph chart diagrams. The cascade graph encoder improves explainability by creating sub-graph chart diagrams for some group substructures and then creating a main-graph chart diagram based on them.oxy loss b. By minThe cascade graph encoder  construcWe need further processing to quantitatively evaluate the degree of abnormalities as a scalar number because the graph chart diagram is an explainable representation for visual assessment. If the video contains an abnormality, the area of the region drawn by the point on the graph chart diagram is smaller than that of the normal video. This is because the normal barcode-like timeline patterns are absent if there are abnormal substructures. Therefore, we can quantitatively evaluate the abnormality score of a graph chart diagram by calculating the area of the figure drawn by the trajectory of a point. We used the Shapely package in Python to create shapes from the trajectories of points and calculate their areas in this study. Thereafter, we calculated the abnormality score by normalizing it such that the score is 0 for the maximum area and 1 for zero area: G, the function for calculating the area is The target graph chart diagram is d (c (integer of 1–5) as the confidence level and calculate the abnormality score for each video using the following formula.We conducted a comparative study to evaluate the improvement in the screening performance of examiners resulting from the use of a graph chart diagram. There were 8 experts, 10 fellows, and 9 residents enrolled in this study. All the examiners belonged to Showa University Hospitals . The procedure of this test was based on a previous study . The exaThe test procedure comprised an instruction part and two main blocks.Instruction part ⋯ The examiners were instructed on how to perform the test, and a graph chart diagram was explained. The examiners were given samples of the main-graph chart diagrams of a pair of normal and abnormal videos. We used a different model and different videos for testing to generate these main-graph chart diagrams. The performance of AI was not explained to examiners. Regarding the types of CHDs, the examiners were not informed what types of diseases would be included. Considering the ratio of normal to abnormal cases, we did not inform the examiners of the amounts of each.First block ⋯ The examiners were given 40 randomly numbered videos and an Excel file to fill in the answers. They played the videos on a laptop computer and filled in the Excel file with their decisions and confidence levels. No protocol was provided on how to assess the videos in detail to allow the examiners to perform this test as they usually would perform fetal cardiac ultrasound screening in a clinical setting. Therefore, the first block was performed depending on each examiner’s education and skill level.G and abnormality scores G and abnormality scores Second block ⋯ The examiners evaluated the same dataset independently of the first block, referring to graph chart diagrams The examiners had no time limit, and were allowed to change their decisions and confidence levels once they had made it within each block. The examiners spent 20 to 40 min to complete this test, and were not informed of their own results until every test was completed.All the numerical experiments were evaluated using the AUC of the ROC curve. The numerical experiments were run five times with different random seeds, and the mean, standard deviation, median, and maximum and minimum values of the AUC were reported. Considering the experiments in which examiners were enrolled, we calculated the accuracy, false-positive rate (FPR), precision, recall, and F1 scores for an abnormality score of Firstly, we show examples of graph chart diagrams, which are the representation proposed in this study. Subsequently, we show the evaluation of abnormality detection performance of AI only by abnormality scores calculated from graph chart diagrams. Finally, we show the performance improvement when the examiners used the graph chart diagrams. Details of the numerical experiments are explained in oxy loss . RegardiThe cascade graph encoder provides sub-graph chart diagrams as explained in ecreased a,d. ThesAs explained in c made by the examiners  that compresses and represents the information in fetal cardiac ultrasound screening videos and introduced two factors to realize the depiction of the graph chart, including a cascade graph encoder and view-proxy loss. We also demonstrated that the graph chart diagram and the abnormality score could improve the ability of examiners to detect abnormalities.Research on explainability in deep learning has concentrated on analyzing models ,12,13,14Subsequently, we discuss the two proposed techniques, view-proxy loss and a cascade graph encoder. The view-proxy loss improves performance  and reduWe analyze the qualitative features of the graph chart diagrams. The graph chart diagram discards unnecessary information in the ultrasound screening and emphasizes the necessary information. The backward and forward movements of the probe, the speed of the movement of the probe, and the movement of the fetus during video recording are not necessary information for fetal cardiac ultrasound screening. The shape does not change in the graph chart diagram, even if a phase similar to the one passed appears multiple times. This reduces the noise caused by fetal movement and probe movement. The spacing between the points also does not affect the shape. This reduces the effect of the speed at which the probe is moved. Thus, the graph chart diagram is robust to the intrinsic noise caused by probe movement. Furthermore, the graph chart diagram is helpful, considering explainability. Regarding a graph chart diagram, the coordinates corresponding to the plane of the normal structure are scattered over a two-dimensional diagram, which serves as a checkpoint. If the checkpoints cannot be seen in the video, a part of the shape will be missing. Therefore, the area of the shape functions as an indicator of the degree of abnormality. Regarding the detection of shapes, recognizing a shape from the trajectory of a point is an advanced technology. In Python, the Shapely package is a standard technology; nonetheless, a more advanced algorithm may improve the performance of the abnormality score Deep learning-based methods for automatically detecting diagnostic planes ,65,66,67Our study demonstrates that graph chart diagrams improve abnormality detection by examiners with a wide range of experience, from experts and residents. Considering the residents, the mean AUC of the ROC curve with AI assistance was There are several limitations to this study. First, our proposed graph chart diagram is robust to probe movement; however, it has not been tested and evaluated for the influence of acoustic shadows in ultrasound videos. We may have to consider preprocessing, such as shadow detection . Second,We have proposed a graph chart diagram as an explainable AI technology for fetal cardiac ultrasound screening videos. This graph chart diagram exhibited a massive enhancement of screening performance in use by examiners of all experience levels. Furthermore, we showed that skilled examiners can improve their performance by appropriately using explainable AI for their respective roles. We also showed that less skilled examiners perform better with AI assistance than by themselves; however, they could not perform better than using only AI. Our study suggests that an examiner’s expertise will still be a key factor in medical examinations in the future with the widespread use of AI assistance. To address this point, we should consider the educational process to maximize the benefit of our technology. The progress of graphical user interface (GUI) technology has also the potential to improve AI assistance. We hope that explainable AI with these enhancements will enable support examiners with a wide range of experience and augment their medical professional capabilities towards the benefit of the patients.


Chronic kidney disease (CKD) represents a heavy burden on the healthcare system because of the increasing number of patients, high risk of progression to end-stage renal disease, and poor prognosis of morbidity and mortality. The aim of this study is to develop a machine-learning model that uses the comorbidity and medication data obtained from Taiwan’s National Health Insurance Research Database to forecast the occurrence of CKD within the next 6 or 12 months before its onset, and hence its prevalence in the population. A total of 18,000 people with CKD and 72,000 people without CKD diagnosis were selected using propensity score matching. Their demographic, medication and comorbidity data from their respective two-year observation period were used to build a predictive model. Among the approaches investigated, the Convolutional Neural Networks (CNN) model performed best with a test set AUROC of 0.957 and 0.954 for the 6-month and 12-month predictions, respectively. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. The models can allow close monitoring of people at risk, early detection of CKD, better allocation of resources, and patient-centric management. Chronic Kidney Disease (CKD) is a condition resulting in insufficient kidney function, where patients have to live with a compromised quality of life. Asia has the highest prevalence of CKD in the world, led by Japan and followed by Taiwan. In Taiwan, CKD has been the eighth leading cause of death since 1997. Compared to other countries, Taiwan has higher incidences and mortality rates, with the prevalence increasing from 1.99% in 1996 to 9.83% in 2003 [CKD is a substantial financial burden on patients, healthcare services, and the government. Treatments of the ESRD with Renal Replacement Therapy are either expensive  or complex (transplantation). Taiwan has about 0.1%–0.2% of the population receiving dialysis—contributing to about 7% of the total budget of the National Health Insurance (NHI) program . The assWith the availability of biomedical data, the use of machine-learning techniques in healthcare for developing disease prediction models has become common. Further, methods such as deep learning and techniques like ensemble learning have greatly improved the predictive power of machine learning models. By deriving features from Electronic Health Records (EHR), accurate disease prediction models can be developed ,6. At thCommon approaches for developing disease prediction models with EHR data involve collecting clinical and laboratory data from sources such as billing or claims data, discharge summaries, patient history, etc., and building models on features extracted from them. Some previous studies attempted to use longitudinal data to capture temporal patterns to develop disease prediction models for CKD. Ren et al. 2019) developed a predictive model for kidney disease among patients with hypertension from EHR consisting of textual and numeric information. They proposed a neural network framework based on Bidirectional long short-term memory and auto-encoders to encode the textual and numerical information, respectively. They performed under-sampling to balance the data. They achieved 89.7% accuracy with 10-fold cross-validation  develope.Similarly, some studies used non-temporal EHR data to develop disease prediction models. Song et al. (2019) extracted several significant clinical features from EHR data using an ensemble feature selection method to predict the risk of kidney disease among diabetes patients. They achieved an AUROC of 0.71 on an external validation set . Jardin In this paper, we aimed to develop machine-learning models that predict the onset of CKD within the next 6 and 12 months. The model is based on the insurance claims data  over an observation period of 24 months. Further, we aim to assess the reliability of the models by identifying the comorbidities and medications that impact the development of CKD the most.index date as the time at which the patient was diagnosed with CKD  for the first time. For the non-CKD group, the index date is randomly sampled while ensuring a similar distribution of years with the CKD group. We aim to predict the onset of CKD 6 and 12 months before the index date (referred to as the lead time) by processing data from the preceding two years from the lead time (referred to as the observation time). This is a retrospective study on patients who have been diagnosed with CKD and a group of patients without CKD within the chosen observation period. We define the The study was conducted using Taiwan’s National Health Insurance Research Database (NHIRD) , comprisAggregated data;Temporal monthly;Temporal quarterly.To subsample a population for our study, we selected all the patients with at least 2 recorded CKD diagnoses across visits . We then sampled non-CKD patients  using propensity score matching  with theTo obtain the aggregated data, we discarded the temporal component by summing the occurrences of comorbidities and medications for each patient across the observation period. The aggregated data is thus represented with a vector where each element represents the count of comorbidities/medications throughout the observation period. Including age, sex, comorbidities, and medications, the processed data contained 1504 features.th comorbidities/medication during the jth month from the end of the observation period.The temporal-monthly was obtained by aggregating the comorbidities and medications over each month during the observation period. Each patient’s comorbidities and medications were thus represented with a matrix where each row represents the comorbidities (ICD codes) or medications (ATC codes), and each column represents the month of observation . The index  in the matrix represents the number of times the patient was diagnosed with/prescribed the iTo obtain the temporal-quarterly data, we aggregated the features across each quarter  and created a vector and a matrix (as described above) of input for each patient. Because the flattened vectors create an intractable number of features, we limited the number of features by taking the top 100 comorbidities/medications features through feature selection . Adding age and sex to the feature set results in 802 features for each patient. We used the same feature set for the matrix variant to ensure comparability.Note that the vector variant of the temporal-quarterly data and aggregated data was in a one-dimensional vector because it was used with classical machine learning, and this is the required type of input for such algorithms. The matrix variant of the temporal-quarterly and temporal-monthly data was in a two-dimensional matrix and was used with our deep neural network architectures, for which the two-dimensional data was appropriate.We used various modeling algorithms from packages like Scikit-learn   was the feasible choice to model the temporal-monthly data owing to its ability to handle large data inputs through GPU parallelization, while the number of features in temporal data was too large for the other algorithms. Conversely, the aggregate data was not suitable for the deep learning approaches since they are explicitly designed for two-dimensional or temporal data.To prevent any bias in our models from the skewed class distribution, we assigned weights to the following classes: 4 to the minority class (CKD) and 1 to the majority class (non-CKD). The Logistic Regression required further data normalization. This was achieved by scaling the age by the maximum age and binarizing the diagnosis and drug prescription counts. The binarization was done by setting any non-zero values to 1. Normalization had no positive effect on the other models.To incorporate temporal effects in the machine learning models and compare their performance with the deep learning approaches, we used the temporal-quarterly data. We used the feature importance score based on information gain from our LightGBM model (trained on the aggregated data) to select the strongest predictors. An in-depth explanation of the feature importance analysis is available in A subset of methods’ hyperparameters was tuned with 5-fold cross-validation on the training set. The test set on which the final models were evaluated was not used for tuning to obtain an unbiased evaluation of the models. The values of the hyperparameters used in the final models are listed in the Supporting Information.We split the dataset into an 80% training set and a 20% test set. Then, we trained our models on the training set  and reported their performance on the test set. The train-test and cross-validation splits were performed by stratifying using the target label. We primarily used the area under the ROC curve (AUROC) as our evaluation metric . The RecThe confusion matrices for the CNN models are shown in To evaluate the performance of our models with comorbidities and medications independently, we ran 6-month CNN models with 64 and 32 CNN units, respectively. The comorbidity and the medication model scored 0.753 and 0.751 AUROC, respectively, indicating a strong interaction effect between the two data types.To understand the amount of training data required to build a CNN model, we trained multiple CNN models while varying the size of the input training data and recorded the AUROC performance on our test set. Similarly, we also compared the performance of the model while varying the size of the input feature set. The features were sorted based on the importance score obtained from our LightGBM model . While a human can simply look at plain decision trees, interpretation is more difficult for boosting and forest models. However, feature importance can still be derived from such models using the computed information gain values.The information gain measures the reduction in entropy when dividing a set of data into subsets, where entropy is a measure of uncertainty. For example, if in a set of patients, the probability of having CKD is 50%, the entropy is the highest. If we could divide this set into two pure sets containing CKD and non-CKD patients, respectively, the resulting entropy is 0. For each attribute, we could measure its contribution to the reduction in entropy, i.e., its contribution to the improved explanation of the target variable. This also enabled its application in feature selection.We plotted the feature importance for men and women to identify the key predictors associated with sex. The normalized feature importance was derived from the LightGBM models. This model interpretation primarily corroborates the reliability of the models developed from our approach by comparing it with our prior knowledge on CKD.The results unveiled in Among the machine-learning models, deep neural networks (CNN and BLSTM) outperformed the classical models considerably. This is interesting as they are not as well established in EHR-based health risk prediction as in some other fields (most notably computer vision). Considering that our networks were not very large or complex, the most likely reason was that they took advantage of temporal information. This is apparent from the improved performance of the classical models on the temporal-quarterly data. Introduction of temporal effects with the temporal-quarterly data to the models significantly improved the performance of the boosting methods while reducing the performance of the decision tree and logistic regression. This is in agreement with boosting models’ ability to capture complex relationships on large feature spaces.In contrast to the commonly used approach that processes laboratory data in addition to other patient data, we developed a method that does not require laboratory data but processes only patients’ diagnoses, prescriptions, and basic demographic data , since such data is typically available on a larger scale. This was considerably different from related studies that used laboratory values; for example, Jardim et al.—a comparison of the performances is not directly applicable.Looking at the most prominent features in the LightGBM models, we see that age was among the strongest predictors in both the 6 and 12 month models. Among comorbidities, diabetes mellitus was the strongest predictor in both models, followed by gout, chronic glomerulonephritis, and essential hypertension. The medications most associated with the target, according to the model, were sulfonamides, followed by angiotensin, dihydropyridine derivatives, and antacids with antiflatulents. There are some differences in feature importance for men and women. While age appeared to be a slightly stronger predictor for men and diabetes mellitus for women, the most obvious difference was in gout, where the feature importance is much stronger for men than for women.It is reasonable to see age as a strong predictor as CKD typically appears in older people. Diabetes mellitus and gout are strongly correlated with decreasing kidney function, as do chronic glomerulonephritis and essential hypertension . Gout isOur study was built upon the data extracted from Taiwan NHI that includes age, sex, comorbidities, and medications. However, laboratory test results were not included. Therefore, our approach is appropriate for a population study but not recommended for assisting clinicians in assessing the risk for an individual patient. For clinical practice, the decisions based on laboratory tests would be more reliable. Adaptation of our method for clinical application would require further analysis and evaluation in a clinical trial. We would also need to evaluate the model on more recent data to identify potential model drift. If the model was used for decisions such as the need to set up a new dialysis center or to launch a public awareness campaign, the performance metrics we obtain are adequate. These models can also aid in proactively identifying CKD susceptible individuals in an entire region or in large groups without the need for laborious physical tests.Since the study was carried out on the data from Taiwan, it was limited by geography and demographics. Hence, it imposes constraints on the generalizability of the model to the global population or a different region. The presence of noise in the data from human and technical errors, which are difficult to identify, may also affect the performance of the model.In this study, we developed and evaluated a series of artificial intelligence-based models considering minimum variables such as sex, age, comorbidities, and medications. These models predict patients’ risk of developing chronic kidney disease after a period of 6 or 12 months. Among various models tested, convolutional neural networks (CNN) performed best, with an AUROC metric of 0.957 and 0.954 for 6 and 12 months, respectively. To see which features are the most prominent for prediction, we looked at the tree-based LightGBM model. The most prominent features included diabetes mellitus, age, gout, and use of sulfonamides and angiotensins, which are all reasonable in view of CKD.From a policymaker’s point of view, these ML-based models could be efficiently used in resource management and initiating public health initiatives such as closely monitoring and early detection of CKD. Clearly, for the application of such models into clinical practice dealing with individual patients, the feature set would have to be expanded to include laboratory measurements and possibly lifestyle information, which falls within the scope of future work.


Ultrasonic equipment is used to insert a thermoplastic fastener into the substrate material and partially melt it into the porous internal structure. This creates a highly interlocked connection (connection strength is above 500 N) suitable for semi-structural applications. This study focused on the simulation and experimental validation of this process, mainly on the interaction between the pin and the substrate material during the joining process. The dynamic thermo-mechanical model showed reasonable agreement with experimental methods such as process data, high-speed camera monitoring or computed tomography and allowed the prediction of the connection quality by evaluation of the degree of interlock. The connection strength prediction by the developed model was validated within several various process setups, resulting in a prediction accuracy between 94–99% depending on the setup.Creating connection points for sandwich-structured composites without losing technical performance is key to realising optimal lightweight structures. The patented LiteWWeight Reliable and cost-effective joining technologies must be developed to reduce production time and enable the manufacture and assembly of composite structural parts. Here, sandwich-structured composite materials provide clear advantages in terms of weight savings and mechanical performance in the aerospace and automotive industries owing to their high stiffness and strength to weight ratios. Such structures consist of two thin high density face sheets, bonded to a thick core made from low density foam material. Variation of the materials combination and foam to void ratio allows production of lightweight structural systems with desirable resulting material properties.The introduction of fibre-reinforced polymers in load-bearing automotive structures provides a great potential to significantly reduce weight, fuel consumption, and consequently COUsing appropriate materials for appropriate scenarios is becoming more popular in transportation areas such as in the automotive and railway sectors, leading to multi-material design concepts with a high demand for reliable, cost-efficient joining methods ,2. NeverAdhesive bonding is an alternative to metallic fasteners. However, these techniques strongly depend on the contacting surfaces morphology and presence of contaminants. Bonding processes are time-consuming and difficult to analyse once cured. Moreover, debonding leads to significant damage to the contact region ,8. Fibre® LiteWWeight® technology is especially designed for sandwich structures with honeycomb and foamed core materials . Computed tomography (CT) scans were used to reconstruct the sandwich geometry and pin insertion position. Furthermore, a high-speed camera was used to visually assess the LiteWWeight® process and calculate the hammering coefficient. Additional studies were performed to evaluate the damage rate and the temperature distribution of the pin during joining.This study aims to assess whether it is feasible to create a dynamic numerical impact model of the LiteWWeightThermoplastic material: The thermoplastic pins were made from the fibre-reinforced polymer PA66 GF30  and were produced by injection moulding. The use of a glass fibre-reinforced polymer is advantageous because of the increased Young’s modulus and mechanical loss factor of the material. Therefore, the ultrasonic vibrations were converted into heat more efficiently, which was crucial for the MM-Welding® LiteWWeight® process. The thermoplastic pins were dried at 90 °C for 8 h because of the influence of the absorbed humidity in PA66 GF30 [A66 GF30 . The pinSandwich-structured composite: A lightweight panel, which is typically integrated in the automotive sector as an interior component, was used. It is based on face sheets made of glass fibre-reinforced polymers and a honeycomb core structure made of cardboard. The components were bonded together using a polyurethane resin that partially expanded into the core layer. The elastic modulus for the face sheet is 4550 MPa, 400 MPa for the honeycomb cardboard internal structure and 300 MPa for the foam. The overall thickness of the panel was 16 mm, whereby top and bottom face sheets were 0.5 mm. The length and width of a honeycomb element was respectively 13 mm and 6 mm.Various methods have been used to characterise the mechanical and thermal material properties of the pin material PA66 GF30 and sandwich material to identify parameters for the finite-element simulation model. First, differential scanning calorimetry experiments were performed to analyse the crystallisation behaviour at different cooling rates and to derive heat capacities. Temperature- and frequency-dependent dynamic mechanical analysis measurements were performed for the purpose of storage and loss modulus. Furthermore, the following mechanical tests were conducted: tensile test on the face sheet substrate material, compression behaviour of the pins, and the sandwich substrate. The face sheet tensile test is essential for the implementation of the Hashin damage criteria. A detailed representation of the data is documented in Brauner et al.  and summ® pins was optimised in a preliminary study by systematic variation of relevant processing parameters using a design of experiments [−1 and an amplitude of 76 μm operating at 20 kHz were applied.In this study, a servo-driven ultrasonic welder  was used. The equipment monitored the force, displacement, amplitude, frequency, velocity, and energy with a sampling rate of 1000 Hz. The ultrasonic insertion of LiteWWeighteriments . Hence, ® process is highly dynamic, high time resolution methods were exploited to allow further data analysis and interpretation. A FASTCAM SA5 high-speed camera  was used to analyse effects such as pin movement, hammering (contact between the pin and sonotrode), and potential pin-tip failure. To derive the concept of the hammering coefficient, which plays an important role in calculating heat by friction and dissipation, a MATLAB algorithm was developed and coupled with a high-speed camera to track various points on the pin and sonotrode through the process  was used to record the reaction force below the sandwich panel with a high time resolution of 1 kHz. The unfiltered force curve results from oscillatory reaction forces combined with measurement errors due to high sampling rate. The measured force signal was smoothed using a Gaussian filter method see , which sAdditionally, CT scans were performed. The scans presented an inside view of the inserted pin, highlighted the mechanical strength of the pin and the surrounding foam content. Furthermore, it allowed quick measurement of the densities of the components used and showed possible weaknesses that were introduced during manufacturing . The mechanical strength of the pin was measured by a pulling out procedure in a Zwick 100 kN universal tensile test machine  at a testing speed of 5 mm/min to determine the maximum force. Furthermore, a partial insertion study was performed with variations in insertion collapse length. This method allowed the assessment of the damage rate of the thermoplastic pin tips at various phases during the process.The commercial software Abaqus  was used for the simulation of the heat transfer, elastic and plastic deformations, and failure in pin and substrate elements. A fully explicit coupled thermal-mechanical dynamic analysis was implemented. This full coupling was needed because most of the heat arose from friction and dissipation, and the material properties are temperature-dependent.Three general heat sources arose in the pin and substrate during the process: heat from friction, plastic deformation, and ultrasonic energy dissipation. The heat fluxes generated on the contacting surfaces of the pin and substrate were estimated according to the following:Here, ectively . The valides. In  it was sented in ,23, the ® technology, because extensive inelastic deformations occur rapidly in a material whose mechanical properties are temperature-dependent, and heat has no significant time to dissipate. The heat flux per unit volume was estimated according to the equationAbaqus allows the calculation of the inelastic heat fraction, which defines the amount of heat generated by mechanical dissipation associated with plastic strain. This term can be introduced as a source of coupling for the thermal-mechanical analysis. The inelastic heat fraction plays a significant role in LiteWWeightHere, The Coulomb friction model was implemented to carry shear stresses by the definition of critical shear stress, at which sliding of the surfaces started as a fraction Abaqus/Explicit predicts material progressive damage and failure based on the undamaged elastic-plastic response of the material, damage initiation criterion, and damage. Plastic stress-strain ratio is defined from the experiments for the pin, and plastic properties of honeycomb cells and foam are known as a range of possible values . In both cases, the von Mises yield surface was used to define isotropic yielding. It is defined as the value of the uniaxial yield stress as a function of the uniaxial equivalent plastic strain.The pin destroyed the upper face sheet and the internal structure of the sandwich panel through the substrate. Therefore, failure models and element removal criteria were defined and integrated into the model. Further governing equations related to Abaqus/Explicit were complemented as ® technology is to form an interlock between the pin and substrate and provide a high value of pullout force. The form-locking process is a function of the process parameters  and the geometry of the contacting surfaces. A geometric description of the surfaces is critical for modelling. Surface topography can be described as a nonstationary random process [The main goal of the LiteWWeight process  or fract process . With si process  represen process  and Mant process  proposed process  was implHere, ® technology is not a trivial task because the interface surface is non-constant, elements of pin and substrate were deleted according to the defined element removal criteria, and the whole pin moves relative to the substrate. For the validation of the proposed approach for the performance quality prediction, five different insertion setups were considered: reference, faster insertion speed, lower insertion speed, higher US amplitude, and lower US amplitude. Three trials were performed per setup. The specifications are presented in The calculation of the degree of interlock according to expression (8) for the LiteWWeightAn Anton-Paar MCR 300 rheometer was used to perform rheological measurements that were used to describe the temperature-dependent viscosity of PA66 GF30. A plate-plate configuration was used for the measurements. The plate diameter was 25 mm, with a gap thickness of approximately 1 mm. The samples were dried before testing for 12 h at 90 °C. Additionally, the samples were exposed to nitrogen during the measurement because of the accelerated thermal degradation of PA66 GF30 at high temperatures. Furthermore, the confocal laser microscope (Keyence VK-X1100) enabled the characterisation of the surface roughness at different locations on the pin surface. The surface roughness values were evaluated with one-dimensional line roughness measurements according to standard DIN EN ISO 4287.The overall objective of this section is the validation of the numerical model by comparison with experimental results. The numerical model should adequately predict process performance which was represented by the degree of interlock that is dependent on the input parameters temperature, stress, and failure fields. After the insertion process, the most important process signals were exported from the ultrasonic welder, as shown in CT scans were analysed before and after the insertion process at the same position for validation. Several cross-sections of the sandwich structure were imaged to replicate an approximated structure in the impact model. The wall thickness (0.5 mm) was measured on the basis of CT scans. The internal space in each honeycomb cell at every level was filled with a 3D hexagonal prism domain, depending on the foam volume content in this region. The internal space of the cell was filled if the CT scan analyses showed that foam occupied more than 50% of the 3D hexagonal prism volume. Performing simplification of the foam distribution to FEM geometry allows the consideration of relatively large void areas inside the honeycomb, neglecting small local variations. The top and bottom composite surfaces were presented as two-dimensional shell structures defined as the skin of the 3D sandwich domain. The proposed method, for the simplified representation of a real 3D lightweight structure, considers non-uniform foam distribution inside the honeycomb with the composite face sheets, and its block-wise domain allows sweeping of the triangular mesh on the face sheets through the thickness direction, both of which provide an increased calculation accuracy in the impact region and reduce computational time.The model assembly consists of three parts: ultrasonic sonotrode, thermoplastic pin, and sandwich structure . The son3, of the substrate’s elements is 1.41 mm3 and the average area of the face sheet elements is 1.24 mm2. Abaqus/Explicit automatically evaluates the time step on the base of the minimal element size and mechanical properties of the considered domains. Therefore, proposed finite-element mesh is a result of the compromise between the simulation accuracy and total calculation time. Defined finite-element mesh provides an average time step being equal to 2 × 10−8 s, which leads to approximately 25 million iterations for the simulation of 0.5 s of the joining process. The numerical model was solved using 16 processors within 27 h. Decrease of the average element size leads to the significant increase of the calculation time, while increase of element size leads to no solver convergence due to the hour-glassing problem.The pins domain was meshed using 12′647 linear coupled temperature-displacement tetrahedron elements. The substrate’s domain was meshed using 62′656 linear coupled temperature-displacement triangular prism elements and composite shells were meshed with 3′194 triangular elements. Both sandwich and pin domains were meshed using sweep meshing routine, face sheets were meshed with free triangular mesh. The average volume of the pin’s elements is 0.95 mmThe displacement and its derivatives were set to zero and the temperature was set to 20 °C for all domains at the beginning of the simulation. The model contained two node-based boundary conditions: displacement for the rigid sonotrode and fixed constraint (zero displacements and rotations) for the sandwich bottom face sheet see . The verHere, The solution of the heat-transfer equations is shown in The reaction force curve represents the main phases of the process . TherefoA ‘partial insertion’ study was performed to measure the destruction rate of the pins tips on different phases of the process. The destruction rate in the final phase of the process plays a significant role in the formation of an interlock between the pin and bottom composite face sheet. A high destruction rate leads to the shortening of the pins body, and possibly to the situation where the pin does not reach the bottom face sheet and does not form an interlock, thereby significantly reducing the joint quality. On the other hand, a low destruction rate could lead to penetration of the bottom face sheet by the tips, which is unacceptable in terms of the visual appearance.Before the upper face sheet collapse (collapse length is 0–1.5 mm): tips were partially melted and slightly deformed; approximate loss of original tip’s height was 10%.After the upper face sheet collapsed (collapse length is 1.5–3 mm): tips were partially destroyed; approximate loss of original tip’s height was 10–30% depending on local stiffness under a single tip.Before the pins reached the bottom face sheet (collapse length is 3–16 mm), the destruction rate was slightly increased. Since the substrate (foam) material was much softer than the pins, the pin did not meet significant resistance during its insertion through the core of the sandwich structure. However, during this step, the temperature of the tips increased significantly owing to friction.After the pins reached the bottom face sheet (collapse length is 18 mm): the tips lost most of their original height.In the frame of the ‘partial insertion’ study, various collapse lengths were set in a series of trials. After partial insertion, the pin was pulled out and the reduction in the tip height was measured. Owing to the strong influence of the internal structure on the process, the pin lost different percentages of the original tip’s height with the same collapse distance at different insertion locations. However, in most cases, the pin destruction rate increased significantly after the upper face sheet collapsed and after reaching the bottom face sheet. In general, the pin-tip damage rate evolution can be described in four steps:A comparison between the results of the ‘partial insertion’ study and simulation for the key steps of the joining process is presented in The idea of calculating the hammering coefficient is based on the determination of the contact and non-contact points within the measured signals. With the labelled points, the proportion can be calculated and referenced as the hammering coefficient. First, both data arrays (sonotrode and pin) were normalised by polynomial regression. Subsequently, the signal for clean oscillations was evaluated. The next step was to sum up the distances between the pin and the sonotrode and divide it by an averaged value. Then, the ‘contact’ and ‘non-contact’ points were defined and the occurrence was calculated. The hammering coefficient was evaluated by dividing both the sums. To date, this approach is the most robust and repeatable method.Evaluation of the hammering coefficient is an important step in understanding sonotrode-pin interactions during the process. For the experiment, the hammering coefficient was evaluated using the MATLAB tracking algorithm, as described in the experimental methods. The hammering coefficient was also evaluated on the basis of the simulated reaction force. When the reaction force is equal to 0, there is no contact between the pin and sonotrode. The hammering coefficient value within the current time range can be evaluated as the ratio of non-zero force values to the total amount of force values. A comparison between the measured and simulated hammering coefficients is shown in Both FEM and MATLAB tracking algorithms show that after the ultrasonic vibrations started, the hammering coefficient was close to 1 (full contact) with small local reductions. The local hammering coefficient predicted by the FEM and MATLAB tracking algorithms did not always correlate with each other. This could be explained by the limited accuracy of the implemented methods (coarse finite-element mesh or low resolution of the high-speed camera) or by the inaccuracy of the substrate geometry reconstruction since local variation in the substrate’s foam content plays a significant role in the pins’ movement.Abaqus allows the consideration of heat from plastic deformation and friction. The Abaqus results for temperature are presented in The temperature increase due to the elastic deformations was calculated using Equation (5) for the heat from the ultrasonic energy dissipation. According to this equation, the temperature grew only by about 0.1 degree after 0.5 s, which was significantly lower than the heat from the plastic deformations and friction and was, therefore, neglected in further calculations of the degree of interlock.The sonotrode boundary conditions were reconstructed for every setup on the basis of the exported data according to Equation (9). Since Abaqus software does not support the calculation of the connection quality, a MATLAB routine was developed for the evaluation of the degree of interlock on the base of thermal and stress distribution exported from the finite-element model. Thus, the Abaqus solution for the temperature and pressure was exported for every finite element (except the head region and elements that were destroyed during the process) for every time step starting from the Phase III , and implemented into Equation (8) for the degree of interlock. According to the simulated and experimentally-observed temperature distributions, only the tip and energy director area of the pin were subjected to melting. Therefore, only a small percentage of all nodes reached a fully interlocked state (The calculated average degrees of the interlock and measured pullout forces are presented in ® LiteWWeight® technology was presented, and a thermo-mechanical numerical model that predicts the joining quality was developed. Based on the results obtained, the following conclusions can be highlighted:Material properties of the pin and sandwich were defined using various experimental measuring techniques.The geometry in the numerical model was reconstructed based on computed tomography scans to ensure identical process conditions, pin position, and local foam distribution.The numerical model successfully calculated the damage rates and heat from friction and plastic deformations of the pin and sandwich structures.The model was verified and validated based on comparative experiments.The routine for the prediction of the connection quality from the finite-element model temperature and stress distribution was derived based on the degree of the interlock approach, which only showed reasonable agreement for significantly different process setups resulting in a prediction accuracy between 94–99%. Despite this fact, the methodology applied suits the potential for lowering the effort towards new pin and sonotrode materials.The numerical simulation and data analyses conducted during the process enabled the discrimination and identification of critical process and design features, such as hammering, damage rate or reaction force.In this study, the MM-WeldingPresented results will not only accelerate process development cycles, but also provide the potential for an advanced online process quality monitoring system. Predictive capabilities of the developed model enable the possibility of numerical-based processes and pin geometry optimization, which is the focus of the future studies.


Respiratory failure complicates most critically ill patients with COVID-19 and is characterized by heterogeneous pulmonary parenchymal involvement, profound hypoxemia and pulmonary vascular injury. The high incidence of COVID-19 related respiratory failure has exposed critical shortages in the supply of mechanical ventilators, and providers with the necessary skills to treat. Traditional mass-produced ventilators rely on an internal compressor and mixer to moderate and control the gas mixture delivered to a patient. However, the current emergency has energized the pursuit of alternative designs, enabling greater flexibility in supply chain, manufacturing, storage, and maintenance considerations. To achieve this, we hypothesized that using the medical gasses and flow interruption strategy would allow for a high performance, low cost, functional ventilator. A low-cost ventilator designed and built-in accordance with the Emergency Use guidance from the US Food and Drug Administration (FDA) is presented wherein pressurized medical grade gases enter the ventilator and time limited flow interruption determines the ventilator rate and tidal volume. This simple strategy obviates the need for many components needed in traditional ventilators, thereby dramatically shortening the time from storage to clinical deployment, increasing reliability, while still providing life-saving ventilatory support. The overall design philosophy and its applicability in this new crisis is described, followed by both bench top and animal testing results used to confirm the precision, safety and reliability of this low cost and novel approach to mechanical ventilation. The ventilator meets and exceeds the critical requirements included in the FDA emergency use guidelines. The ventilator has received emergency use authorization from the FDA. COVID-19 is highly contagious and can lead to respiratory distress, severe hypoxemia and respiratory failure . With thMechanical ventilators, devices that facilitate both oxygenation and ventilation, first became widely available in the 1950s. Initially, mechanical ventilators used time-cycled negative pressure to facilitate gas exchange, technology that addressed the respiratory insufficiency due to muscular weakness resulting from infection with the polio virus. Subsequently, with more respiratory failure resulting from lung injury, positive pressure ventilation became more standard . As moreGiven the profound shortage of resources, especially mechanical ventilators, our group, like many others , sought To address the critical shortage of mechanical ventilators, the FDA provided detailed information surrounding the process and requirements for devices eligible for Emergency Use Authorization (EUA) which limited requirements to those vital to health and safety requirements. The EUA pathway authorized use of the products for the duration of the emergency with further regulatory clearance and FDA approval required upon the conclusion of the emergency. The EUA process was applicable to many product types in the fight against COVID-19, not just for ventilators , as detaEmergency Resuscitators, devices that provide positive pressure via mask or nasal interface, and Emergency Ventilators. Our ventilator, termed the O2U ventilator, falls under the Emergency Ventilator category which entails the capacity to deliver time-cycled, positive pressure breaths, limited by either pressure or volume [The FDA offered guidance on the creation of 2 distinct ventilator categories. Specifically, the EUA offered guidance on r volume . The desThe primary gas delivery power source for the O2U ventilator is from the pressurized medical gases which are plumbed into the hospital and subsequently into the ventilators . The goaThis represents the first time that the FDA has implemented an Emergency Use Authorization (EUA) for ventilators. The FDA streamlined the traditional 510(k) pathway for ventilators as there were several tests that would normally take a significant amount of time (and money). These were predominantly biocompatibility tests. Examples of these were:Permitting the use of known (approved) materials without requiring biocompatibility testing.A reduction of the electrical safety requirements the EUA ventilators need to meet to those which are most important to patient and user safety and functionality.Allowing risk analyses and materials analysis to fill the gap left by a lack of testing.Continued usage of EUA ventilators after the emergency period concludes will entail completion of all 510k-required testing and subsequent clearance from the FDA.Our design was unique from the outset as it was always intended to exist after the EUA phase had passed.Most ventilators offer pressure-controlled or volume-controlled ventilation. This requires the use of a closed feedback loop in the case of pressure-controlled, or at least one flow sensor in the volume-controlled case. The O2U ventilator relied on a known flow rate entering the system and control of the valve timings. The pressure-limited, time-cycled design included continuous monitoring of the pressures to detect any leaks or obstructions that could risk patient or device safety.2) was mixed with medical-grade air prior to entering the ventilator by means of a gas mixer. During non-invasive operation modes this gas mixture was allowed to pass continuously through the device, entering and exiting the patient breathing circuit, operating in the same manner as other CPAP or High Flow Nasal Cannula systems. Inspiratory and expiratory valves were kept open in this state and the system monitored the pressures in cases of leaks or blockages or any other risks to patient and device safety. For invasive ventilation modes, either assisted or mandatory, the inspiratory and expiratory valves were used to allow or prevent the passage of incoming and outgoing gas, respectively, to enforce the respiration cycle within the patient. To overcome the lack of availability in flow-measuring sensors, the flow rate was set on the ventilator using a manual control valve, also known as a Thorpe Tube, and the inspiratory time was set such that a known volume of gas was delivered during each inspiration phase. The input Thorpe tube regulated the pressure from wall pressures of typically 50 psi down to 1–3 psi, the regular range for patient breathing circuits. A specific tidal volume could then be delivered by knowing the flow rate and adjusting the inspiratory time such that the tidal volume VT could be calculated simply as VT = (Flow rate x Inspiratory time) where the operator could determine, for a user-set flow rate and inspiratory time, the delivered flow volume. Volume calculations are still important for patient care, so in addition to this table for setting the desired delivered volume, a spirometer-based expiratory volume sensor was used on the expiration side of the circuit to measure exhaled volumes. This spirometer-based volume sensor was based on the work of Edmunds et al. [The ventilator was connected to pressurized sources of air and oxygen gas. The desired fraction of inspired oxygen  contains an order of magnitude more component pieces. This component count difference results in an overall cost of build for the O2U ventilator at $1000 USD, compared to the 5,000–40,000 USD cost for conventional ventilators . The maiAs previously stated, the goal of the O2U ventilator was to simplify the process of respiratory care in the event of a pandemic. This meant a simple to use device was needed, with minimal features, but sufficient fidelity between the input settings and ventilator performance. To demonstrate this, a number of bench-top tests, designed to fulfil the requirements of the EUA, and an animal test were conducted to observe the flexibility of this ventilator in the cases where it was designed to be used. While the EUA did not require any animal testing, this animal test, performed on a 65 kg adult pig, described in The focus of the EUA requirements was to ensure ventilators would be effective in the treatment of COVID-19 patients. As such, the following variables, relevant to COVID-19 treatment, were tested:Positive End-Expiratory Pressure (PEEP): to ensure that the lung always has some positive pressure inside so that the gas exchange units of the lung do not collapse during exhalation. A small amount of PEEP is often used to protect the lungs.Breath Rate (breaths/min delivered): contingent on patient condition, age, lung disease, and overall acid-base status, the number of breaths per minute will vary.Tidal Volume: Lung volumes typically relate to overall body size and hence the tidal volume  needs to be variable to treat patients of different size and type of lung pathology.Inspiratory Time (similar to I:E ratio): based on the breath rate, a fixed amount of time is placed between breaths, this can be split into inspiration and exhalation phases at a ratio, normally more time is given for exhalation as the lungs contract slowly, resulting in a low expiratory flow rate compared to the ventilator driven inspiratory flow rate.Bench top tests were conducted using a Michigan Instruments test lung. The protocols for these parameter sweeps are outlined in Data for both the test lung testing and animal testing was gathered with two TSI 5320–2 Advanced Gas Mass Flowmeters, placed at the ends of the inspiratory and expiratory lungs.Pressure and flow data from the TSI 5320–2 Advanced Gas Mass Flowmeters were analyzed using Python 3.7 and Jupyter notebooks. Volume was calculated by integrating the flow values to derive minute ventilation and all other volume values. Data was obtained from both the inspiratory and expiratory limbs of the circuit to measure the delivered and exhaled volumes, PIP and PEEP, respectively.2O. All of the tests are listed in Data was recorded on the TSI flow meters and processed as comma separated value text files. These files were then post-processed using custom Python scripts to analyze the flow data. Flow of gasses were recorded, along with the pressure. These pressures were measured as absolute values and atmospheric pressure was measured during testing to calculate the gauge pressure, measured in cmHDuring mechanical ventilation (MV), lung injury can occur, especially in the context of respiratory failure. High tidal volumes, high driving pressure, and atelectasis are injurious to the lung and can even lead to damage in end-organs such as the kidney or intestine . In the 2O. PEEP was controlled by a separate control on the expiratory limb of the device , without causing injury. Even though lung injury can present after only 20 minutes of ventilation in some murine models, the present design does not permit a definitive conclusion surrounding the safety of the present ventilator over an extended time interval. Testing the O2U ventilator in a preclinical lung injury model will provide still more information regarding the capacity to facilitate oxygenation when the Alveolar-arteriolar gradient (A-a O2) gradient is compromised.Use of a preclinical porcine model permitted rigorous testing of the device relative to efficacy and safety. The high correlation between minute ventilation input parameters and changes in blood gases in terms of both PaCOWhile the results of the testing and the animal subject were very promising, and sufficient to qualify for FDA Emergency Use authorization, there are still some limitations of the proposed design and the presented study. Ideally, for a more thorough and lengthier study of the use of the design in large animals, additional experiments would be conducted. While not required for the FDA, or present in the works of other competing designs, additional experiments would allow for a better understanding of the O2U ventilator function under different physiological conditions. Testing on both healthy and unhealthy subjects would be of value to better assess ventilator function in the context of lung injury, the condition wherein the O2U ventilator would likely be used. As the device has now received FDA authorization, human data will be extremely valuable to ensure efficacy of the O2U ventilator in the clinical arena. The O2U ventilator design, while intended to provide a straightforward interface, possesses limited flexibility relative to conventional ventilators. The reduction in component count for the O2U ventilator is motivated, to a significant degree, by use of an external source of pressurized, premixed, medical grade gas. While this is something readily available in hospitals and other health care facilities, this precludes use of the O2U ventilator in the field. The respiratory cycle of the ventilator includes, at the outset, closure of the inspiratory valve. With the onset of inspiration, the valve is opened and pressurized gas flow unimpeded to the patient until the inspiration time is complete. At the end of the inspiratory time, the inhalation valve is closed and the expiratory value is opened and passive recoil of the lung and patient chest wall permits the patient to exhale. The respiratory cycle of the ventilator includes, at the outset, closure of the inspiratory valve. With the onset of inspiration, the valve is opened and pressurized gas flow unimpeded to the patient until the inspiration time is complete. At the end of the inspiratory time, the inhalation valve is closed, and the expiratory value is opened and passive recoil of the lung and patient chest wall permits the patient to exhale. Effective ventilation requires, at a minimum that the pressure of the gas source be greater than atmospheric pressure. This method of utilizing the pre-pressurized gas is different to the traditional approach whereby a gas mixture is provided to a chamber in the ventilator and compressor within the ventilator’s design delivers the gas to the patient breathing circuit to a predefined pressure. Consequently, this device can be used as a transport ventilator in the presence of a portable supply of pressurized air and oxygen with sufficient volume to provide a continuous supply of medical-grade gasses. Further, given the focus on simplicity of design, a back-up power supply is not included in the present design.Finally, the current firmware of the O2U ventilator supports mandatory, CPAP, and basic SIMV modes of ventilation. While these modes are extremely useful to support most patients with lung disease, the ability to ventilate using more complex, automated ventilation modes, if enabled, would further improve the efficacy and versatility of the design, but also increase complexity and expense.Among the limitations of the experiment performed was the absence of humidification. The O2U ventilator, like most ventilators, will rely on external devices. Though ventilation for short periods of time without humidification can be effective, for extended ventilation with the O2U ventilator, or any other mechanical ventilator, use of humidification, to prevent drying of secretions, retention of secretions, blockage of the endotracheal tube, and epithelial injury  is necesWith the pandemic continuing, hospitals and medical professionals now have a far greater understanding of how to manage those infected. Early guidance at the outset of the COVID-19 crisis suggested that early initiation of mechanical ventilation would improve outcomes and mitigate the risk of dissemination of the virus . With inGiven uneven access to the COVID-19 vaccine, the pandemic continues to cause illness and death in many parts of the world. Hence, ventilators remain in short supply wherever the disease surges. Likely, with the emergence of new pathogens, the episodic increase in demand for ventilators will persist for decades to come. In North America, the pre-pandemic ventilator rate of production was approximately 700 new ventilators per week , but sweIn this study we presented a new, versatile, rapid-response based design ventilator for the current COVID-19 pandemic. Our design has been authorized under the FDA’s Emergency Use requirements. The relatively straightforward, physiologically well-grounded design will permit ready deployment into the clinical arena. The cost and simplicity of the design promises to motivate production at scale. These properties may enable the O2U ventilator to fill a large gap in our current healthcare armamentarium, specifically a scalable, inexpensive, and highly functional mechanical ventilator. The O2U ventilator is especially well positioned to address the marked divergence of supply and demand that has happened, and will continue to occur, in large scale pandemics worldwide.


Introduction: Elite youth soccer players suffer increasing numbers of injuries owing to constantly increasing physical demands. Deficits in neuromuscular performance may increase the risk of injury. Injury risk factors need to be identified and practical cut-off scores defined. Therefore, the purpose of the study was to assess neuromuscular performance parameters within a laboratory-based injury risk screening, to investigate their association with the risk of non-contact lower extremity injuries in elite youth soccer players, and to provide practice-relevant cut-off scores.Methods: Sixty-two elite youth soccer players (age: 17.2 ± 1.1 years) performed unilateral postural control exercises in different conditions, isokinetic tests of concentric and eccentric knee extension and knee flexion (60°/s), isometric tests of hip adduction and abduction, and isometric tests of trunk flexion, extension, lateral flexion and transversal rotation during the preseason period. Non-contact lower extremities injuries were documented throughout 10 months. Risk profiling was assessed using a multivariate approach utilizing a Decision Tree model [Classification and Regression Tree (CART) method].Results: Twenty-five non-contact injuries were registered. The Decision Tree model selected the COP sway, the peak torque for knee flexion concentric, the functional knee ratio and the path of the platform in that hierarchical order as important neuromuscular performance parameters to discriminate between injured and non-injured players. The classification showed a sensitivity of 0.73 and a specificity of 0.91. The relative risk was calculated at 4.2, meaning that the risk of suffering an injury is four times greater for a player, who has been classified as injured by the Decision Tree model.Conclusion: Measuring static postural control, postural control under unstable condition and the strength of the thigh seem to enable a good indication of injury risk in elite youth soccer players. However, this finding has to be taken with caution due to a small number of injury cases. Nonetheless, these preliminary results may have practical implications for future directions in injury risk screening and in planning and developing customized training programs to counteract intrinsic injury risk factors in elite youth soccer players. Prospective research provide data on injury incidences of youth soccer players, especially from the amateur level  of the youth academies of two professional German soccer clubs were contacted and invited to participate. Three teams  agreed to take part in the study. All members of the playing squad for each team received written information on the purpose of the study, the preseason screening and the injury documentation. If the players were not of full age, the parents gave their consent. Immediately before testing, the players were asked to complete a questionnaire on paper to collect demographics, background information and the histories of any previous lower extremity injuries. A research assistant guided the interview. All lower extremity injuries up to 6 months prior to the assessments had to be reported. Players were excluded if they wore a prophylactic device , if they reported a recent (<6 weeks) musculoskeletal injury or if they had any physical complaints  that might have impaired their performance. Players who met the inclusion criteria were tested via injury risk screening at the beginning of the preparation period of the 2018/2019 season. The Ethics Committee of TU Dortmund University confirmed that the requirements of the Declaration of Helsinki were met.The data collection followed the consensus statement on injury definitions and data collection procedures in studies of soccer injuries . Furthermore, previous data have identified weak relationships between static and dynamic tasks used to assess postural control in youth male soccer players, thus different postural control tests are necessary . The players were instructed to start on the right leg and to maintain balance for 10 s in a static position with the eyes open. The hands were placed on the hips and the swinging leg was flexed 90° in the hip, knee and ankle to minimize contributions from the contralateral leg  devised by Wikstrom et al. . DynamicPostural control under unstable conditions was measured while single-leg standing on a multi-axial free-swinging platform . The quadratic support platform of the Posturomed guarantees free movement in the medial-lateral and anterior-posterior direction  with the Pegasus 3-D system . The players were seated in an upright position with the hips and knees flexed at an angle of 90° on the dynamometer chair. To avoid segmental body movement, adapters were attached to both knees, both hips and both shoulders  of 5 s prior to the following testing condition . After aIsometric hip adduction and abduction was measured by an isokinetic dynamometer IsoMed 2000 . The players were attached on their sides to the dynamometer chair in a standardized position to limit segmental body movement  of 5 s prior to the following testing condition . After aFinally, the measurement of concentric and eccentric knee extension and flexion was performed by an isokinetic dynamometer IsoMed 2000 . A shin pad for unilateral knee flexion and extension was attached to the motor-driven axis of the active dynamometer. The players were seated in an upright position on the dynamometer chair and secured with straps across the shoulder, chest and hip in a standardized position. The backrest was tilted at 75°. The range of motion was set at 0° (full extension) to 90° (flexion)  at both testing conditions (extension and flexion) and both contraction modes (concentric and eccentric) at the testing speed of 60°/s . The COP sway (cm) was calculated for each leg and each trial from the COP data, using a custom R routine according to the following formula:The lowest COP sway for each leg was determined. The mean of both limbs was used for further analysis.For postural control under the dynamic condition, the average magnitude of the ground reaction force vector around zero points in the medial–lateral direction , anterior–posterior direction  and vertical direction  of the force plate was calculated. The medial-lateral stability index and anterior-posterior stability index assess the fluctuations from zero along the frontal and sagittal axes of the force plate, respectively. The vertical stability index assesses the fluctuation from the subject's body weight  and at a 50 Hz sample rate by a manufacturer's accelerometer, which was attached to the center of the platform's bottom. The manufacturer's software provided the raw data after converting the analog signals through a 14 bit A/D converter. According to the manufacturer's guidelines , the raw data of the accelerometer were integrated twice using a custom R routine, resulting in the total path of the platform (mm). The shortest path of the platform (mm) for each leg was determined. The mean of both limbs was used for statistical analysis.For trunk strength testing, raw data were recorded at a sampling rate of 100 Hz, using the manufacturer's software . The highest isometric peak torque (Nm) for trunk flexion, trunk extension, trunk lateral flexion (right and left) and trunk transversal rotation (right and left) was determined. Each relevant trunk strength parameter was expressed to body mass (N·m·kg-1) and was used for the statistical analysis. The normalization to the body mass enabled inter-individual comparison Jaric, . The resFor the hip and knee strength testing, raw data were recorded at a sampling rate of 200 Hz by the manufacturer's software . All relevant test parameters were computed by a self-developed software written in C++  method  having one or more non-contact injuries. The overall non-contact injury incidence was 1.2/1,000 h of total exposure time . The most frequently injured body parts were the ankle (36%) and the thigh muscles , as well as the adductors (16%) and the knee (12%).Sixty-two elite youth soccer players  were enrolled in the study. Twenty-five non-contact injuries were registered over the 2018/2019 season, with 39% of the players . Given this limitation, we would like to explicitly point out that the following discussion and the interpretation of the results should be treated with caution. Thus, the present study should be considered as a preliminary study and further studies are necessary to confirm the findings of this study. Nonetheless and to the best of our knowledge, the present study was the first to analyze postural control and the strength of the thigh, trunk and hip muscles as injury risk factors in elite youth soccer players. In this respect, appropriate comparisons with previous studies are limited.Multivariate analysis using a Decision Tree model selected the COP sway, the peak torque for knee flexion concentric the functional knee ratio and the path of the platform as neuromuscular performance parameters associated with the injury outcome. The analysis in this study indicated that players who have been classified as injured by the Decision Tree model had a four-fold increase in their injury risk compared with those who have been classified as non-injured. The properties of the Decision Tree model indicated that this classification correctly identified 73% of injured players as being injured. Furthermore, the classification correctly identified 91% of uninjured players as such. Therefore, measuring static postural control, postural control under unstable condition and the strength of the thigh seem to enable a good indication of injury risk in elite youth soccer players. However, there is an important limitation to interpret the results of the present study due to a small number of injury cases (Read et al.  reviewedIn addition, postural control under unstable conditions was selected in the optimization of the Decision Tree model. It shows that the risk of suffering a non-contact lower extremity injury is higher for players with a higher path of the platform meaning that a lower ability to maintain balance under unstable conditions increase the injury risk. Prospective research on postural control under unstable conditions and the risk of injury is rare. However, research on injury-preventive balance training programs showed that dynamic exercises like single-leg stance on unstable training devices reduce the risk of suffering a lower extremity injury (Caraffa et al., Actions in soccer that require rapid decelerations involve substantial eccentric muscle force contributions from the knee extensors, increasing the risk of non-contact ligament injuries (Simonsen et al., Although finding small differences in knee extension concentric and knee flexion eccentric between injured and non-injured groups, Van Dyk et al.  concludeAlthough the eccentrically co-acting hamstrings have a dynamic role in maintaining the stability of the knee during forceful knee extension (Coombs and Garbutt, In the present study, the functional knee ratio was selected as the third splitting variable of the optimization of the Decision Tree. This result is difficult to interpret in that the ratio should potentially be balanced according to normative values to indicate a significant functional capacity of the hamstrings muscles providing muscular stability at the knee joint (Aagaard et al., In principle, the classification shows that the risk of suffering a non-contact lower extremity injury is higher for players with a higher functional knee ratio. The related leaf node includes 24% of the players with better static postural control, lower concentric strength of the hamstring and a higher functional knee ratio, of which 67% suffered an injury. Since this path of the Decision Tree includes only players with lower concentric strength of the hamstrings, we assume that these players also have lower eccentric strength of the hamstrings. Therefore, higher functional knee ratios in this group of players result from both lower eccentric strength of the hamstrings and lower concentric strength of the quadriceps. This leads to the conclusion that players with lower strength of the thigh are at increased risk of injury even if they display better static postural control. However, as there are conflicting results in previous and more recent prospective studies, which also include methodological differences like the choice of the speed of testing, the strength of the knee extensors and knee flexors as injury risk factors must be comprehensively investigated in future studies. Especially instead of using H:Q ratios, which incorporate peak moments emerging at considerable different knee joint angles (Alt et al., Finally, the fourth cut of the Decision Tree model has to be discussed. The related leaf node includes 11% of the players with better static postural control, lower concentric strength of the hamstring, lower functional knee ratio and lower ability to maintain balance under unstable conditions, of which 57% sustained an injury. Although at first it seems surprising that this path of the Decision Tree includes players with better static postural control and lower postural control under unstable conditions, this can be explained based on previous research. In older basic research, postural control has often been treated as a general ability (Ringhof and Stein, Read et al.  propose Strength deficits of the hip muscles are not listed in the systematic model of Read et al. , but theThis study has limitations. Although fatigue has been shown to lead to a higher risk of injury, each injury was weighted equally, regardless of its occurrence during the season. As the physical performance level of the players has a tendency to deteriorate over the season and the overall level of fatigue may accumulate along as the number of matches and training sessions increases, the injury risk has to be evaluated during an etiologically relevant time period – that is, during the time more closely preceding the injury event (Emery et al., The sample size and injury occurrence in our study are still limited, too. Therefore, caution should be taken into interpretation of the present results. Additionally, the number and distribution of injuries do not allow in-depth analyses by subgroups, such as by types of injuries. Furthermore, one of the methodological approaches of the study was to investigate between-group differences. However, it is important to consider that soccer players are exposed to an asymmetric musculoskeletal loading owing to dominant and non-dominant leg. Therefore, future injury risk analyses of inter-subject differences should be conducted not only between injured and non-injured legs, but also between dominant and non-dominant legs.Finally, although an injury may occur because of a single risk factor, it is more likely to appear to be the result of an interaction between multiple risk factors. Therefore, we have conducted a multivariate analysis by using a decision tree that maintains the ability of providing distinct cut-off values to validate a screening program in the future and high interpretability. In comparison to multivariate regression analysis, which provides only information about the weighting of the parameters and their interaction, this approach allows drawing conclusions with high practice-relevant implications.In sports practice, it is desirable to identify all players with an increased risk of injury. The use of the Decision Tree model and the provided cut-off values will allow practitioners and coaches in the future to determine the injury risk of their players after conducting the biomechanical injury risk screening with them. Identifying injury risk factors and assessing individual risk of injury of each player enables customized injury prevention interventions to be provided as part of the player's daily training schedule. Several meta-analyses on the effect of multicomponent exercise prevention programs highlight the role of neuromuscular training and postural control components to be of importance for effective injury risk reduction (Donnell-Fink et al., This research suggested that measuring static postural control, postural control under unstable conditions and the strength of the thigh seem to enable a good indication of injury risk in elite youth soccer players. However, this finding has to be taken with caution due to a small number of injury cases and further studies with larger sample sizes are necessary to confirm the findings of this study. Nonetheless, these preliminary results may have practical implications for future directions in injury risk screening and in planning and developing customized training programs to counteract intrinsic injury risk factors in elite youth soccer players.mathias.kolodziej@bvb.de.The datasets presented in this article are not readily available because the rights to the data as intellectual property belong to Borussia Dortmund. Requests to access the datasets should be directed to Mathias Kolodziej, The studies involving human participants were reviewed and approved by Ethical Committee of the TU Dortmund University. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.MK devised the project, designed and performed the experiments, and wrote the paper with input from all authors. MK and KN verified the analytical methods and analyzed the data. MK and MS worked out the technical details of the postural control testing. MK and TA worked out the technical details of the thigh strength testing. TJ supervised the project. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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