Breast cancer’s high mortality rate is often linked to late diagnosis, with mammograms as key but sometimes limited tools in early detection. To enhance diagnostic accuracy and speed, this study introduces a novel computer-aided detection (CAD) ensemble system. This system incorporates advanced deep learning networks—EfficientNet, Xception, MobileNetV2, InceptionV3, and Resnet50—integrated via our innovative consensus-adaptive weighting (CAW) method. This method permits the dynamic adjustment of multiple deep networks, bolstering the system’s detection capabilities. Our approach also addresses a major challenge in pixel-level data annotation of faster R-CNNs, highlighted in a prominent previous study. Evaluations on various datasets, including the cropped DDSM (Digital Database for Screening Mammography), DDSM, and INbreast, demonstrated the system’s superior performance. In particular, our CAD system showed marked improvement on the cropped DDSM dataset, enhancing detection rates by approximately 1.59% and achieving an accuracy of 95.48%. This innovative system represents a significant advancement in early breast cancer detection, offering the potential for more precise and timely diagnosis, ultimately fostering improved patient outcomes.

Aggression in children is highly prevalent and can have devastating consequences, yet there is currently no objective method to track its frequency in daily life. This study aims to investigate the use of wearable-sensor-derived physical activity data and machine learning to objectively identify physical-aggressive incidents in children. Participants (n = 39) aged 7 to 16 years, with and without ADHD, wore a waist-worn activity monitor (ActiGraph, GT3X+) for up to one week, three times over 12 months, while demographic, anthropometric, and clinical data were collected. Machine learning techniques, specifically random forest, were used to analyze patterns that identify physical-aggressive incident with 1-min time resolution. A total of 119 aggression episodes, lasting 7.3 ± 13.1 min for a total of 872 1-min epochs including 132 physical aggression epochs, were collected. The model achieved high precision (80.2%), accuracy (82.0%), recall (85.0%), F1 score (82.4%), and area under the curve (89.3%) to distinguish physical aggression epochs. The sensor-derived feature of vector magnitude (faster triaxial acceleration) was the second contributing feature in the model, and significantly distinguished aggression and non-aggression epochs. If validated in larger samples, this model could provide a practical and efficient solution for remotely detecting and managing aggressive incidents in children.

Introduction: With the outbreak of the COVID-19 disease and the virtualization of education, many challenges were created in the field of medical education. Many of these challenges were turned into opportunities with the help of new technologies such as virtual reality. The purpose of this research was to investigate the applications of virtual reality in medical education in the era of COVID-19. Methods: We aimed to investigate new technologies’ applications in medical education during the COVID-19 pandemic. Original English articles were browsed in online databases of PubMed, Embase, Scopus, and Web of Scienceas of November 24, 2022. Data of eligible publications were extracted following screening/ selection in two steps and applying inclusion/ exclusion criteria. This systematic review follows PRISMA checklist and Newcastle-Ottawa Scale (NOS) bias assessment tool. Results: Based on the included articles, Microsoft HoloLense2 and Meta Oculus devices were used extensively in medical training studies. In some of the studies, the results demonstrated that the use of these technologies resulted in high levels of engagement, was suitable for training purposes, and decreased the risk of medical learning practicums. Moreover, some studies observed improvement in training compared to traditional training systems. Conclusion: Extended reality use including Virtual Reality (VR), Mixed Reality (MR), and Augmented Reality (AR) concepts in teaching activities and practical procedures can improve the overall educational process, while also increasing engagement, motivation, and understanding of key concepts of participants, especially medical students.

Background: Smart offloading technologies enable the real-time, objective monitoring of adherence in patients with diabetic foot ulcers (DFUs). Although remote tracking may reinforce adherence and improve wound healing, effectiveness depends on sustained device use, particularly as devices are often removed during rest periods. Real-time, behavior-contingent feedback informed by sensor data, including AI-supported messaging capable of detecting nonadherence, may enhance reinforcement. However, the feasibility and behavioral impact of such strategies remain unclear. Methods: We conducted a prospective feasibility case series nested within a larger DFU cohort of 210 participants, enrolling eight adults with active DFUs. Participants used a sensor-integrated offloading device paired with a smartwatch (SmartBoot) and a mobile application (CORA) that delivered notifications to their smartphones. Notifications were either schedule-based or context-aware, using real-time SmartBoot data to generate personalized messages. The primary outcome was a sensor-detected transition from nonadherent to adherent offloading within 60 min. Results: A total of 130 notifications were delivered, with 125 included in the behavioral response analysis. Context-aware notifications demonstrated higher transition rates than schedule-based notifications. Adaptive Reinforcement yielded the highest response rate (77.4%, 24/31), followed by Clinical Course Correction (71.4%, 20/28), whereas Safety and Technical Assurance (40.7%, 11/27) and Motivational Coaching (30.8%, 12/39) showed lower response rates. Conclusions: Real-time, context-aware feedback is feasible and associated with improved short-term adherence, supporting evaluation in larger trials.

Background: Biological aging varies substantially among individuals of the same chronological age, yet biological aging biomarkers require biospecimen collection and laboratory processing, limiting scalability for repeated monitoring. Objective: This study evaluated whether consumer smartwatch-derived digital biomarkers are associated with the pace of biological aging, quantified using DunedinPACE, a biomarker that measures biological aging rates. Methods: Baseline data were analyzed from the Healthy Minds for Life study within the Precision Aging Network. Among 1305 enrolled participants, 621 community-dwelling adults aged 50–79 years had both DunedinPACE and smartwatch data available. Smartwatch-derived biomarkers were grouped into mobility, cardiorespiratory physiology, sleep, and physiological stress domains. Associations with DunedinPACE were examined using Spearman correlations and adjusted linear regression models. Results: Significant associations were observed across all four domains. Daily step count showed the strongest mobility-domain association with DunedinPACE (r = −0.25, p < 0.01). Mean sleep peripheral oxygen saturation showed the strongest sleep-domain association (r = −0.22, p < 0.01). Resting heart rate showed the strongest physiological stress-domain association (r = 0.24, p < 0.01). Adjusted regression models supported these findings, with lower daily mobility, poorer sleep oxygenation and efficiency, greater wake after sleep onset, and higher resting heart rate showing the most consistent associations with higher DunedinPACE values. Conclusions: Smartwatch-derived biomarkers captured a multidomain profile of accelerated biological aging characterized by lower mobility, poorer sleep health, and elevated physiological stress. Consumer wearables can provide low-burden, real-world indicators of biological vulnerability, although longitudinal studies are needed before clinical use.

Background: Biological ageing varies among individuals of the same chronological age. DunedinPACE estimates the pace from DNA methylation, requiring biospecimen collection and a laboratory assay. Passively collected smartwatch data may offer a non-invasive way to prioritise individuals for this assessment. We studied whether smartwatch-derived biomarkers of mobility, sleep, stress, and cardiorespiratory function could identify fast agers. Methods: This cross-sectional analysis included 614 community-dwelling adults aged 50–79 years from four Precision Aging Network sites (of 1,309) with blood-derived DunedinPACE and smartwatch data. Participants were classified as slow (n=67), average (n=443), or fast (n=104) agers using ±1 SD cut-points of age- and sex-normed DunedinPACE. Univariable and adjusted (age, sex, BMI, race) logistic regression related each smartwatch feature to fast-ager status versus all others. On a held-out test set, we compared a four-predictor smartwatch model with an age-only model using AUC, sensitivity, predictive values, calibration, SHAP importance, and decision curves. Findings: On held-out data, chronological age discriminated fast agers with an AUC of 0.54; smartwatch biomarkers reached 0.75 (95% CI 0.62–0.85), with daily steps accounting for 37% of classifier output. The smartwatch model (Brier score 0.133, NPV 0.90) returned positive net clinical benefit across 5–45% threshold probability. Mobility biomarkers separated the groups most sharply, with the largest gap we observed. Fast agers also showed lower nocturnal oxygen saturation, higher resting heart rate, reduced heart-rate variability, and lower sleep efficiency. Interpretation: Fast agers move less, carry a higher cardiorespiratory load, and sleep less continuously than their peers, a pattern revealed in passively collected smartwatch data. Consumer smartwatch data may therefore offer a noninvasive and scalable way to screen for accelerated biological aging and identify individuals who may benefit most from confirmatory blood-based testing. Validation in larger and more diverse populations is needed before clinical implementation.