The impact of (simulated) resolution on breast cancer diagnosis based on high-resolution 3D micro-CT microcalcification images.

BACKGROUND: Breast microcalcifications (MCs) are considered to be a robust marker of breast cancer. A machine learning model can provide breast cancer diagnosis based on properties of individual MCs - if their characteristics are captured at high resolution and in 3D. PURPOSE: The main purpose of the study was to explore the impact of image resolution (8 µm, 16 µm, 32 µm, 64 µm) when diagnosing breast cancer using radiomics features extracted from individual high resolution 3D micro-CT MC images. METHODS: Breast MCs extracted from 86 female patients were analyzed at four different spatial resolutions: 8 µm (original resolution) and 16 µm, 32 µm, 64 µm (simulated image resolutions). Radiomic features were extracted at each image resolution in an attempt, to find a compact feature signature allowing to distinguish benign and malignant MCs. Machine learning algorithms were used for classifying individual MCs and samples (i.e., patients). For sample diagnosis, a custom-based thresholding approach was used to combine individual MC results into sample results. We conducted classification experiments when using (a) the same MCs visible in 8 µm, 16 µm, 32 µm, and 64 µm resolution; (b) the same MCs visible in 8 µm, 16 µm, and 32 µm resolution; (c) the same MCs visible in 8 µm and 16 µm resolution; (d) all MCs visible in 8 µm, 16 µm, 32 µm, and 64 µm resolution. Accuracy, sensitivity, specificity, AUC, and F1 score were computed for each experiment. RESULTS: The individual MC results yielded an accuracy of 77.27%, AUC of 83.83%, F1 score of 77.25%, sensitivity of 80.86%, and specificity of 72.2% at 8 µm resolution. For the individual MC classifications we report for the F1 scores: a 2.29% drop when using 16 µm instead of 8 µm, a 4.01% drop when using 32 µm instead of 8 µm, a 10.69% drop when using 64 µm instead of 8 µm. The sample results yielded an accuracy and F1 score of 81.4%, sensitivity of 80.43%, and specificity value of 82.5% at 8 µm. For the sample classifications we report for F1 score values: a 6.3% drop when using 16 µm instead of 8 µm, a 4.91% drop when using 32 µm instead of 8 µm, and a 6.3% drop when using 64 µm instead of 8 µm. CONCLUSIONS: The highest classification results are obtained at the highest resolution (8 µm). If breast MCs characteristics could be visualized/captured in 3D at a higher resolution compared to what is used nowadays in digital mammograms (approximately 70 µm), breast cancer diagnosis will be improved.

Healthcare professionals' perspectives on development of assistive technology using the comprehensive assistive technology model.

The implementation of technology in healthcare shows promising results and provides new opportunities in rehabilitation. However, the adoption of technology into daily care is largely dependent on the acceptance rate of end-users. This study aims to gather information from healthcare professionals on the development of new assistive technology that match users' needs using the Comprehensive Assistive Technology model. In total 27 healthcare professionals (12 occupational therapists, 8 physiotherapists, 3 nurses, 2 allied health directors, a physician and a speech therapist) attended one of four online focus group discussions. These focus group discussions were structured using a question guide based on three predefined scenarios. Recordings were transcribed and data was analyzed using a thematic analysis (NVivo). Major themes identified in this study were safety, price and usability. Healthcare professionals focused on both functional capabilities of the user, as well as behavioral aspects of usability and attitude toward technology. Furthermore, the need for assistive technology that were catered toward the limitations in activity and user experience, was highlighted extensively. Based on information gathered from healthcare professionals a user-centered approach in development of safe, low-cost devices that maximize both functional outcomes and user acceptance, could potentially increase the adoption of new technology in rehabilitation.

On-Device Execution of Deep Learning Models on HoloLens2 for Real-Time Augmented Reality Medical Applications.

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.

Impact of COVID-19 on Physical Activity, Fatigue, and Frailty in Community-Dwelling Older Adults: A Cross-Sectional Study.

This study aimed to describe the level of physical activity and its relation to fatigue and frailty during the COVID-19 pandemic in community-dwelling older adults aged 80 years and over. Three hundred and ninety-one older adults (aged 86.5 ± 3.00) completed a survey including physical activity, the Mobility Tiredness scale, and the FRAIL scale. Linear regression analysis was conducted to assess whether the variables age, sex, and physical activity (independent factors) were significantly related to fatigue and frailty. Respectively, 30.5% and 24.7% of the participants reported a decrease in walking and in energy-intensive activities; 25.4% reported increased sedentary behavior. A lower level of physical activity was associated with higher levels of fatigue and increased frailty risk (p < .05), independently from psychological symptoms. These results are important because participants with lower levels of physical activity and more sedentary behavior are more likely to feel fatigued and have higher risk to be frail.

Exploring technology acceptance of head-mounted device-based augmented reality surgical navigation in orthopaedic surgery.

BACKGROUND: This study used the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the acceptance of HMD-based AR surgical navigation. METHODS: An experiment was conducted in which participants drilled 12 predefined holes using freehand drilling, proprioceptive control, and AR assistance. Technology acceptance was assessed through a survey and non-participant observations. RESULTS: Participants' intention to use AR correlated (p < 0.05) with social influence (Spearman's rho (rs) = 0.599), perceived performance improvement (rs = 0.592) and attitude towards AR (rs = 0.542). CONCLUSIONS: While most participants acknowledged the potential of AR, they also highlighted persistent barriers to adoption, such as issues related to user-friendliness, time efficiency and device discomfort. To overcome these challenges, future AR surgical navigation systems should focus on enhancing surgical performance while minimising disruptions to workflows and operating times. Engaging orthopaedic surgeons in the development process can facilitate the creation of tailored solutions and accelerate adoption.

Virtual reality-enhanced walking in people post-stroke: effect of optic flow speed and level of immersion on the gait biomechanics.

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).

Neuro-oncological augmented reality planning for intracranial tumor resection.

Background: Before 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. Methods: We 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. Results: During 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%. Conclusion: By 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.

Evaluating a clinical decision support point of care instrument in low resource setting.

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.

Captured by Digitization: Algorithms, Law, and Media.

While digitization claims to provide efficiency, accessibility, expansion, speediness, and profit accumulation, it is actually colonizing every human activity. It has even become a purpose in itself. In this essay we focus on the digitization of legal practices and contents. We describe what digitization encompasses, how digitalization processes work, and to what extent they are able to replace juristic processes and produce legal outcomes. We are inspired by Walter Benjamin's essay on the influence of mechanical reproduction of the works of Art. Parallel to Benjamin's work on Art, we will analyze Law and the consequences of innovations such as mechanical mass (re)production and computerized digitization.

Muscle Endurance and Self-Perceived Fatigue Predict Decline in Gait Speed and Activities of Daily Living After 1-Year Follow-Up: Results From the BUTTERFLY Study.

BACKGROUND: Fatigue might influence the losses in activities of daily living (ADL). When fatigue parameters are present before the experience of losses in ADL and gait speed, they can be used as early warning signals. This study aimed to explore the predictive value of muscle endurance and fatigue on changes in ADL and gait speed in community-dwelling older adults aged 80 and older. METHODS: Three hundred twenty four community-dwelling older adults aged 80 and older of the BUTTERFLY study were assessed after 1 year for muscle endurance, self-perceived fatigue, ADL, and gait speed. Exploratory factor analysis (EFA) was performed to explore, whether there is an underlying arrangement of the fatigue parameters. Mediating logistic regression analyses were used to investigate whether muscle endurance mediated by self-perceived fatigue predicts the decline in gait speed and ADL after 1-year follow-up. RESULTS: EFA indicated a 2-factor model (muscle endurance factor and self-perceived fatigue factor) and had a moderate fit (X2: 374.81, df: 2, comparative fit index; 0.710, Tucker-Lewis index (TLI): 0.961, root mean square error of approximation [90%]: 0.048 [0.00-0.90]). Muscle endurance mediated by self-perceived fatigue had an indirect effect on the prediction of decline in Basal-ADL (-0.27), Instrumental-ADL (-0.25), and gait speed (-0.28) after 1-year follow-up. CONCLUSION: This study showed that low muscle endurance combined with high self-perceived fatigue can predict changes in ADL after 1-year follow-up. These parameters might be very suitable for use in evaluating intrinsic capacity and can help to reduce the limitations in clinical usage of the vitality domain in the framework of intrinsic capacity.

Assessment of formats and completeness of paper-based referral letters among urban hospitals in Rwanda: a retrospective baseline study.

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.

Analysis of low resource setting referral pathways to improve coordination and evidence-based services for maternal and child health in Ethiopia.

BACKGROUND: In 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. OBJECTIVE: The 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. METHODS: This 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. RESULTS: A 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. CONCLUSION: The 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.

A Hybrid Approach for Designing Dynamic and Data-Driven Clinical Pathways Point of Care Instruments in Low Resource Settings.

Though a clinical pathway is one of the tools used to guide evidence-based healthcare, promoting the practice of evidence-based decisions on healthcare services is incredibly challenging in low resource settings (LRS). This paper proposed a novel approach for designing an automated and dynamic generation of clinical pathways (CPs) in LRS through a hybrid (knowledge-based and data-driven based) algorithm that works with limited clinical input and can be updated whenever new information is available. Our proposed approach dynamically maps and validate the knowledge-based clinical pathways with the local context and historical evidence to deliver a multi-criteria decision analysis (concordance table) for adjusting or readjusting the order of knowledge-based CPs decision priority. Our finding shows that the developed approach successfully delivered probabilistic-based CPs and found a promising result with Jimma Health Center "pregnancy, childbearing, and family planning" dataset.

Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks.

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.

Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images.

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.

The effect of augmented reality on the accuracy and learning curve of external ventricular drain placement.

OBJECTIVE: The traditional freehand technique for external ventricular drain (EVD) placement is most frequently used, but remains the primary risk factor for inaccurate drain placement. As this procedure could benefit from image guidance, the authors set forth to demonstrate the impact of augmented-reality (AR) assistance on the accuracy and learning curve of EVD placement compared with the freehand technique. METHODS: Sixteen medical students performed a total of 128 EVD placements on a custom-made phantom head, both before and after receiving a standardized training session. They were guided by either the freehand technique or by AR, which provided an anatomical overlay and tailored guidance for EVD placement through inside-out infrared tracking. The outcome was quantified by the metric accuracy of EVD placement as well as by its clinical quality. RESULTS: The mean target error was significantly impacted by either AR (p = 0.003) or training (p = 0.02) in a direct comparison with the untrained freehand performance. Both untrained (11.9 ± 4.5 mm) and trained (12.2 ± 4.7 mm) AR performances were significantly better than the untrained freehand performance (19.9 ± 4.2 mm), which improved after training (13.5 ± 4.7 mm). The quality of EVD placement as assessed by the modified Kakarla scale (mKS) was significantly impacted by AR guidance (p = 0.005) but not by training (p = 0.07). Both untrained and trained AR performances (59.4% mKS grade 1 for both) were significantly better than the untrained freehand performance (25.0% mKS grade 1). Spatial aptitude testing revealed a correlation between perceptual ability and untrained AR-guided performance (r = 0.63). CONCLUSIONS: Compared with the freehand technique, AR guidance for EVD placement yielded a higher outcome accuracy and quality for procedure novices. With AR, untrained individuals performed as well as trained individuals, which indicates that AR guidance not only improved performance but also positively impacted the learning curve. Future efforts will focus on the translation and evaluation of AR for EVD placement in the clinical setting.

The interrelationship between grip work, self-perceived fatigue and pre-frailty in community-dwelling octogenarians.

INTRODUCTION: Low grip work and high feelings of self-perceived fatigue could be an early characteristic of decline in reserve capacity, which comes to full expression as physical frailty in a later stage. When grip work and self-perceived fatigue can be identified as characteristics differentiating between robustness and pre-frailty it might allow to identify pre-frailty earlier. Therefore, this study aimed to investigate whether the combination of grip work and self-perceived fatigue is related to pre-frailty in well-functioning older adults aged 80 and over. METHODS: Four-hundred and five community-dwelling older adults aged 80 and over (214 robust and 191 pre-frail) were assessed for muscle endurance (grip Work corrected for body weight (GW_bw)), self-perceived fatigue (MFI-20) and frailty state (Fried Frailty Index, FFI). A Capacity to Perceived Vitality ratio (CPV) was calculated by dividing GW_bw by the MFI-20 scores. ANCOVA analysis (corrected for age and gender) was used to compare robust and pre-frail older adults, and binary logistic regressions were applied to analyze the relationship between CPV and pre-frailty status. RESULTS: Pre-frail older adults who scored negative on the exhaustion item of the FFI still showed significantly lower GW (p < 0.001), CPV ratios (p < 0.001) and higher self-perceived fatigue (p < 0.05) compared to the robust ones. The likelihood for pre-frailty related significantly to higher age, being men and lower CPV ratios. In women, every unit increase in CPV ratio decreased the likelihood for pre-frailty by 78% (OR 0.22; 95% CI: 0.11-0.44), for men this effect was less strong (34%, OR 0.66; 95% CI: 0.47-0.93). CONCLUSIONS: Pre-frail community-dwelling persons aged 80 years and over without clinical signs of exhaustion on the FFI still experience significantly higher fatigue levels (lower Grip Work, higher self-perceived fatigue and lower CPV levels) compared to robust ones. CPV ratio could therefore be a good tool to identify subclinical fatigue in the context of physical (pre-)frailty.

Augmented Reality-Assisted Neurosurgical Drain Placement (ARANED): Technical Note.

BACKGROUND: Many surgical procedures, such as placement of intracranial drains, are currently being performed blindly, relying on anatomical landmarks. As a result, accuracy results still have room for improvement. Neuronavigation could address this issue, but its application in an urgent setting is often impractical. Augmented reality (AR) provided through a head-worn device has the potential to tackle this problem, but its implementation should meet physicians' needs. METHODS: The Surgical Augmented Reality Assistance (SARA) project aims to develop an AR solution that is suitable for preoperative planning, intraoperative visualisation and navigational support in an everyday clinical setting, using a Microsoft HoloLens. RESULTS: Proprietary hardware and software adaptations and dedicated navigation algorithms are applied to the Microsoft HoloLens to optimise it specifically for neurosurgical navigation. This includes a pipeline with an additional set of advanced, semi-automated algorithms responsible for image processing, hologram-to-patient registration and intraoperative tracking using infrared depth-sensing. A smooth and efficient workflow while maintaining high accuracy is prioritised. The AR solution provides a fully integrated and completely mobile navigation setup. Initial preclinical and clinical validation tests applying the solution to intracranial drain placement are described. CONCLUSION: AR has the potential to vastly increase accuracy of everyday procedures that are frequently performed without image guidance, but could still benefit from navigational support, such as intracranial drain placements. Technical development should go hand in hand with preclinical and clinical validation in order to demonstrate improvements in accuracy and clinical outcomes.

A Systematic Literature Review of the Successors of "NeuroEvolution of Augmenting Topologies".

NeuroEvolution (NE) refers to a family of methods for optimizing Artificial Neural Networks (ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting Topologies (NEAT) is considered one of the most influential algorithms in the field. Eighteen years after its invention, a plethora of methods have been proposed that extend NEAT in different aspects. In this article, we present a systematic literature review (SLR) to list and categorize the methods succeeding NEAT. Our review protocol identified 232 papers by merging the findings of two major electronic databases. Applying criteria that determine the paper's relevance and assess its quality, resulted in 61 methods that are presented in this article. Our review article proposes a new categorization scheme of NEAT's successors into three clusters. NEAT-based methods are categorized based on 1) whether they consider issues specific to the search space or the fitness landscape, 2) whether they combine principles from NE and another domain, or 3) the particular properties of the evolved ANNs. The clustering supports researchers 1) understanding the current state of the art that will enable them, 2) exploring new research directions or 3) benchmarking their proposed method to the state of the art, if they are interested in comparing, and 4) positioning themselves in the domain or 5) selecting a method that is most appropriate for their problem.

Data Augmentation of Surface Electromyography for Hand Gesture Recognition.

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.