Background: Accurate segmentation and characterization of brain tumors from magnetic resonance imaging (MRI) are paramount for diagnosis, treatment planning, and monitoring. While deep learning models like U-Net have set a high standard for segmentation, they can fail to detect complex, multifocal disease and often lack the tools for in-depth clinical characterization beyond basic volume. Methods: A comprehensive, web-based platform built upon a hybrid Convolutional Neural Network (CNN) and Transformer architecture is presented. An end-to-end workflow is provided by the system from medical image upload (DICOM/NIfTI) to final analysis. It features a dedicated, interactive 3D reconstruction environment with real-time controls for mesh manipulation, lighting, and data export (STL for 3D printing, JSON for reports). All measurements are performed in native medical imaging units (millimeters) to ensure clinical accuracy. The platform also includes a detailed analysis tool for calculating a full suite of morphological and clinical metrics, including an estimated WHO grade. Results: Quantitatively, the Hybrid model achieved a mean Dice coefficient of 0.91 and a mean sensitivity of 0.94 across the test set, outperforming the U-Net (0.86 Dice, 0.88 sensitivity) and a traditional algorithm (0.72 Dice, 0.75 sensitivity). In a representative case of multifocal glioma, the hybrid model identified three distinct tumor foci with a total volume of 67,480 mm³, whereas the U-Net identified only a single mass of 15,140 mm³, representing a 4.4-fold increase in detected tumor burden. These results were visualized and explored in the platform's interactive 3D viewer, which provided real-time statistics and allowed for immediate export of the 3D model. Conclusion: Our work demonstrates a complete platform that not only leverages a state-of-the-art segmentation model but also provides the necessary tools for interactive visualization, analysis, and data dissemination. By seamlessly integrating a high-performance algorithm with a user-centric interface, the system serves as a powerful tool for medical education, clinical training, and reproducible research.

Mandatory disease reporting by radiologists is a critical yet inefficient component of public health infrastructure. Current manual, disruptive, and unidirectional processes create a significant administrative burden for clinicians and deliver data that is often delayed and fragmented for public health agencies. This manuscript examines these workflow inefficiencies through a business process analysis, which identified key pain points including context switching, manual data entry, and a fundamental lack of systems integration. To address this, we propose a modernized framework based on the adoption of structured SNOMED CT AU coding, HL7® FHIR® standards, and API-driven interoperability. The proposed model automates reporting through event-driven triggers within radiologists’ existing systems, ensuring timely and accurate data transfer. Furthermore, it introduces a critical bi-directional feedback loop, providing clinicians with confirmation and valuable outcome data. The implementation of this integrated framework can transform mandatory reporting from a bureaucratic task into a seamless byproduct of care delivery. This promotes a collaborative partnership between clinical care and public health, ultimately enhancing the timeliness, efficiency, and overall efficacy of population health surveillance.

Aim: The aim of this observational study was to evaluate the relationship between neck pain and fatigue levels among individuals engaged in desk-based occupations. Methodology: Neck pain–related disability was assessed using the Neck Disability Index (NDI). Fatigue levels were measured using the Fatigue Severity Scale (FSS) and the Visual Analogue Fatigue Scale (VAFS). A total of 20 office-based workers (13 females, 7 males) participated in the study. The mean age of the sample was 31.30 years. The mean height was 1.64 ± 7.6 m, and the mean weight was 63.4 ± 9.8 kg. Participants completed the NDI, FSS, and VAFS questionnaires through a survey method after providing written informed consent. Results: A statistically significant negative correlation was found between NDI and VAFS scores (Pearson’s r = –0.45421, p = 0.0037). Higher neck disability was associated with greater fatigue levels. Mean scores for individual FSS items ranged from 2.76 to 4.37. Conclusion: The study demonstrated that greater neck disability is associated with increased fatigue among desk-job workers. These findings highlight the importance of posture correction, ergonomic modifications, and early screening to reduce neck-related disability and fatigue in individuals engaged in prolonged sitting occupations.

Blood flow restriction (BFR) training is increasingly applied in rehabilitation and performance settings as a low-load alternative to traditional resistance exercise. BFR neuromuscular activation during dynamic, functional activities is less understood, particularly in an acute scenario. Purpose: To investigate how inter-peak muscle activation time (IPMAT) of lower limb muscles (proximal and distal to the cuff) adapted to blood flow restriction while pushing a sled (constant resistance acquired with continuous speed) at two consistent walking speeds. Methods: Sixty-two healthy adults (8 men, 54 women; mean age = 23.0 ± 3.0 years) participated. Anthropometrics, vital signs, and limb dominance were documented. Surface electromyography (EMG; Delsys Trigno system) recorded activity of the gluteus maximus, medial gastrocnemius, and tibialis anterior of the dominant leg. Participants pushed an XPO Trainer sled (85 lb total load) over 40 ft at a slow walk (80 bpm) and a fast walk (140 bpm). Three randomized trials were performed under unrestricted and BFR conditions. BFR was applied with Delfi’s Personalized Tourniquet System at 80% limb occlusion pressure. The primary outcome was BFR versus non-BFR IPMAT for all muscles, analyzed using multivariate analysis of variance (MANOVA). Results: BFR significantly increased IPMAT in the gluteus maximus (slow walk: 1.0672 ± 0.1086 s vs. non-BFR 0.9524 ± 0.1228 s, p < .001; fast walk: 1.1061 ± 0.0955 s vs. non-BFR 0.9428 ± 0.1150 s, p < .001) and medial gastrocnemius (slow walk: 1.1076 ± 0.0798 s vs. 0.8040 ± 0.0969 s, p < .001; fast walk: 1.1435 ± 0.1064 s vs. 1.0719 ± 0.1292 s, p = .008). No significant differences were observed in the tibialis anterior (p > .05). Conclusions: During the blood-constriction settings, IPMAT adapts the primary pushing muscles (gastrocnemius and gluteus muscles), regardless of occlusion cuff location (proximal versus distal), suggesting delayed recovery between activation bursts due to increased neuromuscular demand under restricted blood flow. This adaptation may represent compensatory strategies to sustain task performance under fatigue or metabolic stress. Clinical Relevance: BFR sled pushing provides a low-load alternative that increases neuromuscular variation, increases fatigue and compensatory demands, and supports endurance. Clinicians should consider these timing adaptations when prescribing BFR to individuals with lower extremity weakness, balance deficits, or gait impairments.