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Abstract In total hip arthroplasty (THA), determining the center of rotation (COR) and diameter of the hip joint (acetabulum and femoral head) is essential to restore patient biomechanics. This study investigates on-the-fly determination of hip COR and size, using off-the-shelf augmented reality (AR) hardware. An AR head-mounted device (HMD) was configured with inside-out infrared tracking enabling the determination of surface coordinates using a handheld stylus. Two investigators examined 10 prosthetic femoral heads and cups, and 10 human femurs. The HMD calculated the diameter and COR through sphere fitting. Results were compared to data obtained from either verified prosthetic geometry or post-hoc CT analysis. Repeated single-observer measurements showed a mean diameter error of 0.63 mm ± 0.48 mm for the prosthetic heads and 0.54 mm ± 0.39 mm for the cups. Inter-observer comparison yielded mean diameter errors of 0.28 mm ± 0.71 mm and 1.82 mm ± 1.42 mm for the heads and cups, respectively. Cadaver testing found a mean COR error of 3.09 mm ± 1.18 mm and a diameter error of 1.10 mm ± 0.90 mm. Intra- and inter-observer reliability averaged below 2 mm. AR-based surface mapping using HMD proved accurate and reliable in determining the diameter of THA components with promise in identifying COR and diameter of osteoarthritic femoral heads.

BackgroundA clinical decision support system (CDSS) based on the logic and philosophy of clinical pathways is critical for managing the quality of health care and for standardizing care processes. Using such a system at a point-of-care setting is becoming more frequent these days. However, in a low-resource setting (LRS), such systems are frequently overlooked. ObjectiveThe purpose of the study was to evaluate the user acceptance of a CDSS in LRSs. MethodsThe CDSS evaluation was carried out at the Jimma Health Center and the Jimma Higher Two Health Center, Jimma, Ethiopia. The evaluation was based on 22 parameters organized into 6 categories: ease of use, system quality, information quality, decision changes, process changes, and user acceptance. A Mann-Whitney U test was used to investigate whether the difference between the 2 health centers was significant (2-tailed, 95% CI; α=.05). Pearson correlation and partial least squares structural equation modeling (PLS-SEM) was used to identify the relationship and factors influencing the overall acceptance of the CDSS in an LRS. ResultsOn the basis of 116 antenatal care, pregnant patient care, and postnatal care cases, 73 CDSS evaluation responses were recorded. We found that the 2 health centers did not differ significantly on 16 evaluation parameters. We did, however, detect a statistically significant difference in 6 parameters (P

Abstract Background Optic flow—the apparent visual motion experienced while moving—is absent during treadmill walking. With virtual reality (VR), optic flow can be controlled to mediate alterations in human walking. The aim of this study was to investigate (1) the effects of fully immersive VR and optic flow speed manipulation on gait biomechanics, simulator sickness, and enjoyment in people post-stroke and healthy people, and (2) the effects of the level of immersion on optic flow speed and sense of presence. Methods Sixteen people post-stroke and 16 healthy controls performed two VR-enhanced treadmill walking sessions: the semi-immersive GRAIL session and fully immersive head-mounted display (HMD) session. Both consisted of five walking trials. After two habituation trials (without and with VR), participants walked three more trials under the following conditions: matched, slow, and fast optic flow. Primary outcome measures were spatiotemporal parameters and lower limb kinematics. Secondary outcomes (simulator sickness, enjoyment, and sense of presence) were assessed with the Simulator Sickness Questionnaire, Visual Analogue Scales, and Igroup Presence Questionnaire. Results When walking with the immersive HMD, the stroke group walked with a significantly slower cadence (-3.69strides/min, p = 0.006), longer stride time (+ 0.10 s, p = 0.017) and stance time for the unaffected leg (+ 1.47%, p = 0.001) and reduced swing time for the unaffected leg (− 1.47%, p = 0.001). Both groups responded to the optic flow speed manipulation such that people accelerated with a slow optic flow and decelerated with a fast optic flow. Compared to the semi-immersive GRAIL session, manipulating the optic flow speed with the fully immersive HMD had a greater effect on gait biomechanics whilst also eliciting a higher sense of presence. Conclusion Adding fully immersive VR while walking on a self-paced treadmill led to a more cautious gait pattern in people post-stroke. However, walking with the HMD was well tolerated and enjoyable. People post-stroke altered their gait parameters when optic flow speed was manipulated and showed greater alterations with the fully-immersive HMD. Further work is needed to determine the most effective type of optic flow speed manipulation as well as which other principles need to be implemented to positively influence the gait pattern of people post-stroke. Trial registration number: The study was pre-registered at ClinicalTrials.gov (NCT04521829).

Abstract Background Clinical pathways are one of the main tools to manage the health care’s quality and concerned with the standardization of care processes. They have been used to help frontline healthcare workers by presenting summarized evidence and generating clinical workflows involving a series of tasks performed by various people within and between work environments to deliver care. Integrating clinical pathways into Clinical Decision Support Systems (CDSSs) is a common practice today. However, in a low-resource setting (LRS), this kind of decision support systems is often not readily accessible or even not available. To fill this gap, we developed a computer aided CDSS that swiftly identifies which cases require a referral and which ones may be managed locally. The computer aided CDSS is designed primarily for use in primary care settings for maternal and childcare services, namely for pregnant patients, antenatal and postnatal care. The purpose of this paper is to assess the user acceptance of the computer aided CDSS at the point of care in LRSs. Methods For evaluation, we used a total of 22 parameters structured in to six major categories, namely “ease of use, system quality, information quality, decision changes, process changes, and user acceptance.” Based on these parameters, the caregivers from Jimma Health Center's Maternal and Child Health Service Unit evaluated the acceptability of a computer aided CDSS. The respondents were asked to express their level of agreement using 22 parameters in a think-aloud approach. The evaluation was conducted in the caregiver's spare-time after the clinical decision. It was based on eighteen cases over the course of two days. The respondents were then asked to score their level of agreement with some statements on a five-point scale: strongly disagree, disagree, neutral, agree, and strongly agree. Results The CDSS received a favorable agreement score in all six categories by obtaining primarily strongly agree and agree responses. In contrast, a follow-up interview revealed a variety of reasons for disagreement based on the neutral, disagree, and strongly disagree responses. Conclusions Though the study had a positive outcome, it was limited to the Jimma Health Center Maternal and Childcare Unit, and hence a wider scale evaluation and longitudinal measurements, including computer aided CDSS usage frequency, speed of operation and impact on intervention time are needed.

Convolutional neural networks have been widely used to detect and classify various objects and structures in computer vision and medical imaging. Access to large sets of annotated data is commonly a prerequisite for achieving good performance. Before the deep learning era, systems based on handcrafted features were employed, which typically required less annotated data but also reached inferior performance. In this work, we investigate the benefit of combining deep learning using a convolutional neural network (CNN), with handcrafted features for lung nodule detection from CT imaging. We investigate three fusion strategies with increasing complexity, and evaluate their performance for varying amounts of training data. Our results indicate that combining handcrafted features with a 3D CNN approach significantly improves lung nodule detection performance in comparison to an independently trained CNN model, regardless of the fusion strategy. Comparatively larger increases in performance were obtained when less training data was available. The fusion strategy in which features are combined with a CNN using a single end-to-end training scheme performed best overall, allowing to reduce training data by 33% to 43%, while maintaining performance. Among the investigated handcrafted features, those that describe the relative position of the candidate with respect to the lung wall and mediastinum, were found to be of most benefit.

Abstract Background Patient referral is a process in which a healthcare provider decides to seek assistance due to the limitations of available skills, resources and services offered locally. Paper-based referrals predominantly used in low-income countries hardly follow any procedure. This causes a major gap in communication, coordination, and continuity of care between primary and specialized levels, leading to poor access, delay, duplication and unnecessary costs. The goal of this study is to assess the formats and completeness of existing paper-based referral letters in order to improve health information exchange, coordination, and continuity of care. Methods A retrospective exploratory research was conducted in eight public and three private healthcare facilities in the city of Kigali from May to October 2021. A purposive sampling method was used to select hospitals and referral letters from patients’ files. A data capture sheet was designed according to the contents of the referral letters and the resulting responses were analyzed descriptively. Results In public hospitals, five types of updated referral letters were available, in total agreement with World Health Organization (WHO) standards of which two (neonatal transfer form and patient monitoring transfer form) were not used. There was also one old format that was used by most hospitals and another format designed and used by a district hospital (DH) separately. Three formats were designed and used by private hospitals (PH) individually. A total of 2,304 referral letters were perused and the results show that “external transfer” forms were completed at 58.8%; “antenatal, delivery, and postnatal external transfer” forms at 47.5%; “internal transfer” forms at 46.6%; “Referral/counter referral” forms at 46.0%; district hospital referrals (DH2) at 73.4%. Referrals by private hospitals (PH1, PH2 and PH3) were completed at 97.7%, 70.7%, and 0.0% respectively. The major completeness deficit was observed in counter referral information for all hospitals. Conclusion We observed inconsistencies in the format of the available referral letters used by public hospitals, moreover some of them were incompatible with WHO standards. Additionally, there were deficits in the completeness of all types of paper-based referral letters in use. There is a need for standardization and to disseminate the national patient referral guideline in public hospitals with emphasis on referral feedback, referral registry, triage, archiving and a need for regular training in all organizations.

The integration of Deep Learning (DL) models with the HoloLens2 Augmented Reality (AR) headset has enormous potential for real-time AR medical applications. Currently, most applications execute the models on an external server that communicates with the headset via Wi-Fi. This client-server architecture introduces undesirable delays and lacks reliability for real-time applications. However, due to HoloLens2’s limited computation capabilities, running the DL model directly on the device and achieving real-time performances is not trivial. Therefore, this study has two primary objectives: (i) to systematically evaluate two popular frameworks to execute DL models on HoloLens2—Unity Barracuda and Windows Machine Learning (WinML)—using the inference time as the primary evaluation metric; (ii) to provide benchmark values for state-of-the-art DL models that can be integrated in different medical applications (e.g., Yolo and Unet models). In this study, we executed DL models with various complexities and analyzed inference times ranging from a few milliseconds to seconds. Our results show that Unity Barracuda is significantly faster than WinML (p-value < 0.005). With our findings, we sought to provide practical guidance and reference values for future studies aiming to develop single, portable AR systems for real-time medical assistance.

BackgroundBefore starting surgery for the resection of an intracranial tumor, its outlines are typically marked on the skin of the patient. This allows for the planning of the optimal skin incision, craniotomy, and angle of approach. Conventionally, the surgeon determines tumor borders using neuronavigation with a tracked pointer. However, interpretation errors can lead to important deviations, especially for deep-seated tumors, potentially resulting in a suboptimal approach with incomplete exposure. Augmented reality (AR) allows displaying of the tumor and critical structures directly on the patient, which can simplify and improve surgical preparation.MethodsWe developed an AR-based workflow for intracranial tumor resection planning deployed on the Microsoft HoloLens II, which exploits the built-in infrared-camera for tracking the patient. We initially performed a phantom study to assess the accuracy of the registration and tracking. Following this, we evaluated the AR-based planning step in a prospective clinical study for patients undergoing resection of a brain tumor. This planning step was performed by 12 surgeons and trainees with varying degrees of experience. After patient registration, tumor outlines were marked on the patient's skin by different investigators, consecutively using a conventional neuronavigation system and an AR-based system. Their performance in both registration and delineation was measured in terms of accuracy and duration and compared.ResultsDuring phantom testing, registration errors remained below 2.0 mm and 2.0° for both AR-based navigation and conventional neuronavigation, with no significant difference between both systems. In the prospective clinical trial, 20 patients underwent tumor resection planning. Registration accuracy was independent of user experience for both AR-based navigation and the commercial neuronavigation system. AR-guided tumor delineation was deemed superior in 65% of cases, equally good in 30% of cases, and inferior in 5% of cases when compared to the conventional navigation system. The overall planning time (AR = 119 ± 44 s, conventional = 187 ± 56 s) was significantly reduced through the adoption of the AR workflow (p < 0.001), with an average time reduction of 39%.ConclusionBy providing a more intuitive visualization of relevant data to the surgeon, AR navigation provides an accurate method for tumor resection planning that is quicker and more intuitive than conventional neuronavigation. Further research should focus on intraoperative implementations.

Abstract Background The detection of suspicious microcalcifications on mammography represents one of the earliest signs of a malignant breast tumor. Assessing microcalcifications’ characteristics based on their appearance on 2D breast imaging modalities is in many cases challenging for radiologists. The aims of this study were to: (a) analyse the association of shape and texture properties of breast microcalcifications (extracted by scanning breast tissue with a high resolution 3D scanner) with malignancy, (b) evaluate microcalcifications’ potential to diagnose benign/malignant patients. Methods Biopsy samples of 94 female patients with suspicious microcalcifications detected during a mammography, were scanned using a micro-CT scanner at a resolution of 9 μm. Several preprocessing techniques were applied on 3504 extracted microcalcifications. A high amount of radiomic features were extracted in an attempt to capture differences among microcalcifications occurring in benign and malignant lesions. Machine learning algorithms were used to diagnose: (a) individual microcalcifications, (b) samples. For the samples, several methodologies to combine individual microcalcification results into sample results were evaluated. Results We could classify individual microcalcifications with 77.32% accuracy, 61.15% sensitivity and 89.76% specificity. At the sample level diagnosis, we achieved an accuracy of 84.04%, sensitivity of 86.27% and specificity of 81.39%. Conclusions By studying microcalcifications’ characteristics at a level of details beyond what is currently possible by using conventional breast imaging modalities, our classification results demonstrated a strong association between breast microcalcifications and malignancies. Microcalcification’s texture features extracted in transform domains, have higher discriminating power to classify benign/malignant individual microcalcifications and samples compared to pure shape-features.

BackgroundIn low-resource settings, patient referral to a hospital is an essential part of the primary health care system. However, there is a paucity of study to explore the challenges and quality of referral coordination and communication.ObjectiveThe purpose of this research was to analyze the existing paper-based referral registration logbook for maternal and child health in general and women of reproductive age in particular, to improve referral coordination and evidence-based services in Low-Resource Settings.MethodsThis study analyzed the existing paper-based referral registration logbook (RRL) and card-sheet to explore the documentation of the referral management process, and the mechanism and quality of referrals between the health center (Jimma Health Center-case, Ethiopia) and the Hospital. A sample of 459 paper-based records from the referral registration logbook were digitized as part of a retrospective observational study. For data preprocessing, visualization, and analysis, we developed a python-based interactive referral clinical pathway tool. The data collection was conducted from August to October 2019. Jimma Health Center's RRL was used to examine how the referral decision was made and what cases were referred to the next level of care. However, the RRL was incomplete and did not contain the expected referral feedback from the hospital. Hence, we defined a new protocol to investigate the quality of referral. We compared the information in the health center's RRL with the medical records in the hospital to which the patients were referred. A total of 201 medical records of referred patients were examined.ResultsA total of 459 and 201 RRL records from the health center and the referred hospital, respectively, were analyzed in the study. Out of 459, 86.5% referred cases were between the age of 20 to 30 years. We found that "better patient management", "further patient management", and "further investigation" were the main health-center referral reasons and decisions. It accounted for 40.08%, 39.22%, and 16.34% of all 459 referrals, respectively. The leading and most common referral cases in the health center were long labor, prolonged first and second stage labor, labor or delivery complicated by fetal heart rate anomaly, preterm newborn, maternal care with breech presentation, premature rupture of membranes, malposition of the uterus, and antepartum hemorrhage. In the hospital RRL and card-sheet, the main referral-in reasons were technical examination, expert advice, further management, and evaluation. We found it overall impossible to match records from the referral logbook in the health center with the patient files in the hospital. Out of 201, only 13.9% of records were perfect matching entries between health center and referred hospital RRL. We found 84%, 14.4%, and 1.6% were appropriate, unnecessary and unknown referrals respectively.ConclusionThe paper illustrates the bottlenecks encountered in the quality assessment of the referrals. We analyzed the current status of the referral pathway, existing communications, guidelines and data quality, as a first step towards an end-to-end effective referral coordination and evidence-based referral service. Accessing, monitoring, and tracking the history of referred patients and referral feedback is challenging with the present paper-based referral coordination and communication system. Overall, the referral services were inadequate, and referral feedback was not automatically delivered, causing unnecessary delays.

Abstract Background Friedreich ataxia (FRDA) is a disease with neurological and systemic involvement. Clinical assessment tools commonly used for FRDA become less effective in evaluating decay in patients with advanced FRDA, particularly when they are in a wheelchair. Further motor worsening mainly impairs upper limb function. In this study, we tested if serious games (SG) developed for rehabilitation can be used as an assessment tool for upper limb function even in patients with advanced FRDA. Methods A specific SG has been developed for physical rehabilitation of patients suffering from neurologic diseases. The use of this SG, coupled with Kinect sensor, has been validated to perform functional evaluation of the upper limbs with healthy subjects across lifespan. Twenty-seven FRDA patients were included in the study. Patients were invited to perform upper limb rehabilitation exercises embedded in SG. Motions were recorded by the Kinect and clinically relevant parameters were extracted from the collected motions. We tested if the existence of correlations between the scores from the serious games and the severity of the disease using clinical assessment tools commonly used for FRDA. Results of patients were compared with a group a healthy subjects of similar age. Results Very highly significant differences were found for time required to perform the exercise (increase of 76%, t(68) = 7.22, P

In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. Recently, a specific class of these models called Temporal Convolutional Networks (TCNs) has been successfully applied to this task. (2) Methods: In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using TCNs. Specifically, we investigate the real-time behavior of our previous TCN model by performing a simulation experiment on a recorded sEMG dataset. (3) Results: The proposed network trained with data augmentation yields a small improvement in accuracy compared to our existing model. However, the classification accuracy is decreased in the real-time evaluation, showing that the proposed TCN architecture is not suitable for such applications. (4) Conclusions: The real-time analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.

Major hurdles for Microsoft's HoloLens as a tool in medicine have been accessing tracking data, as well as a relatively high-localisation error of the displayed information; cumulatively resulting in its limited use and minimal quantification. The following work investigates the augmentation of HoloLens with the proprietary image processing SDK Vuforia, allowing integration of data from its front-facing RGB camera to provide more spatially stable holograms for neuronavigational use. Continuous camera tracking was able to maintain hologram registration with a mean perceived drift of 1.41 mm, as well as a mean sub 2-mm surface point localisation accuracy of 53%, all while allowing the researcher to walk about a test area. This represents a 68% improvement for the later and a 34% improvement for the former compared with a typical HoloLens deployment used as a control. Both represent a significant improvement on hologram stability given the current state-of-the-art, and to the best of the authors knowledge are the first example of quantified measurements when augmenting hologram stability using data from the RGB sensor.

The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies–Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.

Thanks to the evolution of game controllers video games are becoming more and more popular in physical rehabilitation. The integration of serious games in rehabilitation has been tested for various pathologies. Parallel to this clinical research, a lot of studies have been done in order to validate the use of these game controllers for simple biomechanical evaluation. Currently, it is thus possible to record the motions performed by the patients during serious gaming exercises for later analysis. Therefore, data collected during the exercises could be used for monitoring the evolution of the patients during long term rehabilitation. Before using the parameters extracted from the games to assess patients’ evolution two important aspects must be verified: the reproducibility of measurement and a possible effect of learning of the task to be performed. Ten healthy adults played 9 sessions of specific games developed for rehabilitation over a 3-weeks period. Nineteen healthy children played 2 sessions to study the influence of age. Different parameters were extracted from the games: time, range of motion, reaching area. Results of this study indicates that it is possible to follow the evolution of the patients during the rehabilitation process. The majority of the learning effect occurred during the very first session. Therefore, in order to allow proper regular monitoring, the results of this first session should not be included in the follow-up of the patient.

Road network analysis can require distance from points that are not on the network themselves. We study the algorithmic problem of connecting a point inside a face (region) of the road network to its boundary while minimizing the detour factor of that point to any point on the boundary of the face. We show that the optimal single connection (feed-link) can be computed in O(lambda_7(n) log n) time, where n is the number of vertices that bounds the face and lambda_7(n) is the slightly superlinear maximum length of a Davenport-Schinzel sequence of order 7 on n symbols. We also present approximation results for placing more feed-links, deal with the case that there are obstacles in the face of the road network that contains the point to be connected, and present various related results.

Optoelectronic devices are the gold standard for 3D evaluation in clinics, but due to the complexity of this kind of hardware and the lack of access for patients, affordable, transportable, and easy-to-use systems must be developed to be largely used in daily clinics. The KinectTM sensor has various advantages compared to optoelectronic devices, such as its price and transportability. However, it also has some limitations: (in)accuracy of the skeleton detection and tracking as well as the limited amount of available points, which makes 3D evaluation impossible. To overcome these limitations, a novel method has been developed to perform 3D evaluation of the upper limbs. This system is coupled to rehabilitation exercises, allowing functional evaluation while performing physical rehabilitation. To validate this new approach, a two-step method was used. The first step was a laboratory validation where the results obtained with the KinectTM were compared with the results obtained with an optoelectronic device; 40 healthy young adults participated in this first part. The second step was to determine the clinical relevance of this kind of measurement. Results of the healthy subjects were compared with a group of 22 elderly adults and a group of 10 chronic stroke patients to determine if different patterns could be observed. The new methodology and the different steps of the validations are presented in this paper.