Search Results (178)

Background/Objectives: Shoulder disorders are among the most prevalent musculoskeletal conditions, with lifetime prevalence reaching 67% and substantial associated disability and economic burden. Geographic barriers and workforce shortages impede access to optimal rehabilitation. This narrative review aims to synthesize current evidence on tele-diagnostics and tele-rehabilitation in shoulder disorders, evaluate clinical outcomes and implementation factors, and explore models for integrating these complementary approaches. Methods: A structured but non-systematic literature search was conducted across PubMed, Scopus, and Web of Science covering publications from January 2010 through December 2025, using terms related to telehealth, tele-rehabilitation, tele-diagnostics, and shoulder disorders. Priority was given to randomized controlled trials, systematic reviews, feasibility studies, and clinical practice guidelines in adult populations. A total of 97 articles were included in the final narrative synthesis. Results: Tele-diagnostic approaches demonstrate acceptable reliability for range-of-motion assessment and general diagnostic classification, though glenohumeral instability evaluation remains challenging remotely. Multiple randomized controlled trials suggest non-inferior outcomes for tele-rehabilitation compared to conventional physiotherapy across rotator cuff repair, shoulder arthroplasty, and conservative management, with generally high patient satisfaction. Certainty of evidence is currently low to moderate due to short follow-up durations, modest sample sizes, and heterogeneous protocols. Key implementation barriers include the digital divide, inability to deliver manual therapy, and insufficient long-term outcome data. Conclusions: Current evidence supports telehealth as a viable complement to conventional shoulder care, with the strongest evidence base for postoperative tele-rehabilitation. Hybrid care models appear clinically feasible, though widespread adoption requires standardized outcomes, longer-term trials, and strategies addressing health equity barriers. Full article

Background: Accurate evaluation of the Near Point of Convergence (NPC) is essential for diagnosing and managing convergence insufficiency (CI). Conventional assessment relies on the patient’s verbal feedback and the examiner’s visual observation, making it subjective and examiner-dependent. The AI-based MobileS platform, previously validated for both diagnosis and home-based therapy of CI, enables smartphone-based measurement and visualisation of NPC through eye tracking, without the need for verbal responses or additional equipment. This study, the third stage of our research programme, examined how ophthalmologists interpret NPC data when presented as videos versus AI-derived graphs. Methods: Twenty-two ophthalmologists completed an online questionnaire with 20 NPC test cases from the validated MobileS database, presented as both silent videos and AI-derived graphs. Accuracy was analysed using mixed-effects logistic regression, and continuous error was assessed using clustered bootstrap. Results: Graph-based interpretation showed higher odds of accurate NPC identification than video-based interpretation at the primary ±5 mm threshold (OR = 19.7, 95% CI: 13.50–28.74; p < 0.0001). Absolute error was lower for graphs than videos (Graphs − Videos: −22.73 mm; 95% CI: −26.88 to −18.59; p < 0.0001). “Uncertain” responses occurred in 28.2% of video-based assessments and 0% of graph-based assessments. Off-target errors decreased from 50.2% (videos) to 3.6% (graphs). Conclusions: AI-derived graphs of eye-movement data were associated with improved NPC estimation, suggesting a potential role in supporting clinical and tele-ophthalmology workflows. Full article

Background/Objectives: Stroke is a leading cause of long-term disability, resulting in motor and functional impairments that compromise independence and quality of life. Telerehabilitation offers a promising solution by providing remote, continuous, and accessible post-stroke therapy. This systematic review examined the effects of telerehabilitation on functional capacity, mobility, balance, and quality of life in stroke survivors. Methods: A systematic search was conducted following PRISMA guidelines and registered in PROSPERO (CRD420251169784). Searches in PubMed, Cochrane Library, PEDro, Web of Science, Scopus and CINAHL ultimately identified randomized controlled and quasi-experimental trials from the last decade involving adult stroke patients receiving exercise-based telerehabilitation. Methodological quality was assessed using Joanna Briggs Institute tools and Cochrane risk of bias evaluation. Twenty-one studies with a total of 1067 participants were included, featuring supervised tele-sessions, autonomous exercises, caregiver-assisted training, and hybrid approaches. Results: Results demonstrated significant improvements in functional capacity, motor performance, balance, and quality of life, comparable to conventional rehabilitation. Additional benefits included enhanced self-efficacy, treatment adherence, and caregiver satisfaction. Overall risk of bias was low, though participant blinding was unfeasible. Conclusions: Telerehabilitation may represent a strategy for post-stroke recovery, with studies suggesting outcomes comparable to conventional face-to-face rehabilitation while enhancing accessibility and psychosocial well-being. However, further well-designed, standardized trials with longer follow-up periods are required to confirm its clinical effectiveness. Full article

Musculoskeletal disorders such as rheumatoid arthritis and osteoarthritis affect millions worldwide and are projected to rise sharply by 2050, highlighting the importance of scalable telerehabilitation. This paper introduces Rehabi, a mobile, user-friendly tele-rehabilitation platform that centrally integrates markerless motion for biomechanical assessment and monitoring. Rehabi development followed a user-centered methodology, combining questionnaires, interviews, and natural language processing to elicit requirements from patients and clinicians. The system architecture was implemented in accordance with Clean Architecture principles to ensure modularity and scalability. In a pilot clinical validation of the markerless motion algorithm integrated into Rehabi, 14 post-arthroplasty patients showed moderate agreement for hip flexion (ICC = 0.686) and good agreement for knee flexion (ICC = 0.801). Although the sample was small, the results show a promising trend suggesting that mobile markerless motion capture may be a viable option for remote assessment and monitoring. Full article

The increasing use of telerehabilitation has intensified the need for validated smartphone sensor-based tools capable of accurately capturing joint range of motion (ROM). This study examined the criterion validity of the PhysioMaster application compared with a universal goniometer during in-person assessments and evaluated the inter-method reliability between in-person and online PhysioMaster measurements. Thirty healthy young adults underwent standardized hip, knee, and ankle ROM testing using both approaches. The criterion validity was limited for most joints, with only ankle plantarflexion demonstrating the highest validity and dorsiflexion showing a moderate association; in contrast, hip and knee ROM exhibited poor agreement with goniometric values. Despite limited absolute agreement, PhysioMaster demonstrated moderate to good inter-method reliability for hip and knee ROM, indicating consistency across assessment modes. These findings suggest that while PhysioMaster may not serve as a direct substitute for in-person goniometry, it shows potential as a consistent tool for tracking ROM changes remotely, particularly for hip and knee movements. The application may support remote musculoskeletal monitoring within telerehabilitation contexts where repeated, standardized assessments are required. Full article

Background/Objective: Post-COVID survivors present significant respiratory deficiency that has been associated with ongoing shortness of breath and impaired lung function. Inspiratory muscle training (IMT) is increasingly used in survivors of COVID-19 rehabilitational programs as a means to facilitate recovery of the respiratory system. Yet, its home-based effectiveness across clinically relevant outcomes remains unclear. This systematic review aimed to present current evidence on home- or tele-delivered IMT in the post-COVID-19 population. Methods: PubMed, Scopus, Cochrane library and Science Direct were systematically searched for studies evaluating home-based (or telerehabilitation) IMT, alone or as part of a respiratory muscle training program, in adults with post-COVID-19 symptoms. The primary outcome was inspiratory muscle strength. Secondary outcomes included dyspnea, pulmonary function, exercise capacity and health-related quality of life. The methodological quality of the included studies was assessed via the PEDro scale. Owing to clinical and methodological heterogeneity, we performed only a qualitative synthesis. Results: Eight studies met the inclusion criteria. Two included both inspiratory and expiratory muscles training and three included physical training as well. The methodological quality was found to be good. IMT consistently increased inspiratory muscle strength across trials. Respiratory muscle training (RMT) programs that combined inspiratory and expiratory training also improved maximal expiratory pressure. IMT reduced dyspnea versus control/sham or baseline and several studies reported improvements in exercise capacity and physical function. Spirometry/DLCO changes were small or null in most cohorts. HRQoL gains were domain-specific in anxiety and depression. Adherence was generally good. No serious adverse events attributable to IMT were reported. Conclusions: Home-based IMT for adults with post-COVID-19 conditions is safe and seems to improve inspiratory muscle strength and dyspnea, with signs of benefit for exercise capacity, physical function, and selected HRQoL domains. Effects on ventilatory efficiency and conventional lung function appear limited. Future multicenter, sham-controlled RCTs should further explore the characteristics of IMT, employ core outcome sets, include longer follow-up, and predefine phenotype-based subgroups. Full article

Machine learning (ML) is increasingly influencing the assessment and delivery of movement and exercise, yet its role within adapted physical activity (APA) for individuals with chronic conditions has not been comprehensively synthesized. ML-based approaches have the potential to enhance functional assessment, support individualized exercise prescription, and facilitate scalable monitoring across preventive, community-based, and long-term adapted exercise settings, particularly in populations characterized by functional heterogeneity and variable responses to exercise. The aim of this narrative review is to synthesize and critically discuss current ML applications relevant to the core professional processes of APA practice. A structured narrative review was conducted using searches in PubMed/MEDLINE, Scopus, and Web of Science, complemented by targeted searches in engineering-oriented sources to capture ML methods not consistently indexed in biomedical databases. The search covered the period in which contemporary ML approaches have been increasingly applied to human movement and exercise research and was last updated in January 2026. Evidence was synthesized thematically into application-oriented domains relevant to APA practice. ML applications in APA include markerless motion and gait analysis, wearable-sensor data processing, balance and fall-risk assessment, and functional classification. Predictive and adaptive models support individualized regulation of exercise intensity, progression, and workload, including remote and hybrid delivery models. Applications span oncology, cardiometabolic, respiratory, neuromuscular conditions, and adapted sport contexts. Ethical, legal, and governance issues, such as algorithmic bias, data privacy, and professional accountability, emerge as central considerations for safe and equitable implementation. ML represents a promising decision-support layer for APA, complementing professional expertise through enhanced assessment, personalization, and monitoring. Its effective integration requires robust validation, interpretability, and responsible governance to ensure that ML augments, rather than replaces, professional judgment in APA practice. Full article

The rapid expansion of digital technologies and artificial intelligence (AI) has profoundly transformed cardiovascular imaging, enabling more precise, efficient, and reproducible assessment of cardiac structure and function. This narrative review summarizes recent advances in AI-driven methods across echocardiography, cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging, with emphasis on image acquisition, automated quantification, and diagnostic and prognostic interpretation. We reviewed contemporary literature describing machine-learning and deep-learning applications for image reconstruction, segmentation, radiomics, and multimodal data integration. Current evidence demonstrates that AI improves image quality, reduces acquisition and analysis time, and enables automated, highly reproducible measurements of chamber volumes, function, tissue characterization, coronary anatomy, and myocardial perfusion, while facilitating advanced pattern recognition for differential diagnosis and risk stratification. Furthermore, digital platforms support remote acquisition, tele-echocardiography, and AI-assisted training of non-expert operators. Despite these advances, challenges remain regarding external validation, generalizability across vendors and populations, explainability, data governance, and regulatory compliance. In conclusion, AI and digital technologies are reshaping cardiovascular imaging by enhancing accuracy, efficiency, and accessibility, but their safe and effective clinical integration requires robust multicenter validation, transparent reporting, and ethical-legal frameworks that ensure trust, equity, and accountability. Full article

Background and Objectives: Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into audiology, supporting diagnosis, screening, rehabilitation, and digital health. Despite rapid growth, the literature remains methodologically and clinically heterogeneous, limiting a consolidated view of research trajectories and translational readiness. This scoping review examined the evolution of AI and ML applications in audiology and hearing disorders, focusing on thematic development, research productivity, collaboration patterns, and clinical orientation. Methods: A scoping review was conducted using the Web of Science Core Collection (Science Citation Index Expanded). Original and review articles published between 1995 and 2025 were included. Bibliometric and thematic mapping were applied to analyze publication trends, citation patterns, keyword evolution, and collaboration networks. A structured translational categorization assessed clinical domains and validation maturity. Findings reflect the Web of Science-indexed segment of the literature. Results: A total of 127 publications were analyzed. Research output increased markedly after 2020, with an estimated doubling time of approximately 2.1 years. China, the United States, and South Korea contributed the highest publication volumes, although citation impact did not consistently parallel productivity. Thematic analyses revealed a shift toward AI-driven methodological frameworks, particularly in machine learning, deep learning, and cochlear implant-related applications. Most studies remain at proof-of-concept or internally validated stages, with limited external validation. Emerging areas include tele-audiology and personalized hearing aid optimization. Conclusions: AI and ML research in audiology is increasingly application-oriented; however, broader external validation and prospective implementation are required to support routine clinical integration. Full article

Oral health in older adults is a critical component of overall well-being requiring integrated, interdisciplinary approaches to address its complex interplay of medical, functional, and psychosocial challenges. The aim of this is to examine strategies to enhance interdisciplinary collaboration among dental professionals, physicians, nurses, nutritionists, and caregivers to improve oral health outcomes in aging populations. Older adults commonly face dental problems such as periodontal disease which can be exacerbated by polypharmacy, systemic diseases, and barriers to accessing care. These multifaceted needs necessitate coordinated efforts across dentistry, geriatric medicine, nursing, and social support systems. Strategies of effective interdisciplinary care include: (1) Medical-dental integration, enabling physicians to screen for oral health issues during routine assessments; (2) Nursing and caregiver engagement in daily oral hygiene support and early problem identification; (3) Nutritional interventions tailored to address chewing difficulties and prevent malnutrition; (4) Social support systems to improve access to affordable care; and (5) Technology-driven solutions such as tele-dentistry to enhance communication, early detection, and care coordination. Despite these opportunities, systemic barriers persist, including fragmented healthcare systems, financial constraints, workforce shortages, cultural biases, and technological gaps. Progress requires commitment from policymakers, healthcare institutions, and health care professionals to prioritize geriatric oral health as a public health imperative. In conclusion, interdisciplinary collaboration enhances older adults’ oral-systemic health via cross-sector policies and healthcare workforce education. Implementing these strategies can mitigate oral health disparities, reduce the burden of chronic diseases, and improve quality of life for aging populations through holistic, patient-centered care. Full article

Background/Objectives: The Speech-Language Pathologists (SLP) are an integral part of the multidisciplinary team approach to rehabilitation of persons with aphasia (PWA). However, the efficacy of treatment provided by SLPs can vary due to several factors related to clinicians, patients, and the availability of services. The present study was conducted with the aim of investigating current practices in aphasia rehabilitation, key challenges, and future directions as perceived by the SLPs in the Indian context. Methods: The study was conducted using a web-based survey comprising a 32-item questionnaire to gather information related to demographic and professional details, knowledge and use of aphasia rehabilitation approaches, patient education, counselling, bilingual & multilingual contexts, and challenges faced by SLPs. A total of 142 responses were analyzed after initial screening to assess the knowledge, use, and confidence of aphasia rehabilitation along with challenges faced by SLPs in the Indian context. Results: The results indicated significant challenges in the assessment of aphasia due to a lack of formal screening and diagnostic languages in several languages. Further, the results also indicated variations in the knowledge level and confidence in the use of various approaches to aphasia rehabilitation, which warrants the urgent need for organizing short-term training programs for SLPs. The participants also self-reported significant challenges in managing bilingual and multilingual patients with aphasia due to differences in their knowledge and confidence in the selection of language for treatment. On the other side, major patient-related challenges include inadequate logistics, lack of funding, unavailability of speech and language therapy services, social acceptance, and support from family members. The participants also reported the necessity of improving tele-rehabilitation services and developing materials and mobile apps for rehabilitation in Indian languages as future directions for aphasia rehabilitation. Conclusions: The present study through a self-reported questionnaire identified key challenges in aphasia rehabilitation related to the clinician and PWA in the Indian context. The results of the study warrant the need for immediate action to overcome the challenges to enhance the rehabilitation services to PWAs. Full article

Frailty is a multidomain reduction in physiologic reserve that impacts recovery and can contribute to poor outcomes following trauma beyond what chronological age, comorbidities, or injury severity predicts. In geriatric trauma patients, a large proportion are frail or prefrail on initial encounter in the emergency department, and because there are opportunities for actionable management plans, major trauma guidelines endorse systematic screening integrated into coordinated geriatric trauma care. We reviewed the literature and identified practical instruments used in the acute trauma setting for risk stratification. Additionally, we highlight the feasibility of using these instruments, as some can be completed via patient report, proxy input, or chart review when cognition, language, or caregiver availability limits history-taking. Implementation efforts succeed when shared mental models are leveraged and screening is embedded in the electronic health record system, linked to order sets and trigger-based pathways that offer downstream goal-directed care management, such as early mobility, delirium prevention, nutrition, medication review, and comprehensive geriatric assessment. Additionally, we highlight the importance of initiating early goals-of-care discussions and coordinating care with palliative care services. Resource-limited systems can preserve the same architecture by using nurse-led or allied staff-led screening, tele-geriatric consultation, and virtual interdisciplinary huddles. Lastly, we expand upon opportunities for longitudinal post-discharge follow-up. We describe how targeted initiatives translate research into practice, improve outcomes, and support longitudinal reassessment through in-person and telehealth follow-up visits. Full article

Objectives: Telemedicine has rapidly expanded in oral and maxillofacial surgery (OMFS), especially during the COVID-19 pandemic, but its specific roles and limitations across the care pathway remain unclear. This narrative review aimed to map telemedicine modalities and indications in OMFS, summarize reported outcomes, and identify priorities for future research. Methods: A narrative synthesis was undertaken after a systematic search of medical and engineering databases to 10 October 2025. Studies applying telemedicine, telehealth, telepresence or teleradiology to OMFS practice were eligible, including trials, observational cohorts, technical reports and surveys. Data were extracted in duplicate and organized thematically; heterogeneity precluded meta-analysis. Results: Fifty studies met the inclusion criteria. Telemedicine was mainly used for preoperative consultation and triage, postoperative follow-up, trauma teleradiology and tele-expertise, oncologic and oral medicine follow-up, temporomandibular disorders, and education or humanitarian work. In low-risk outpatient and postoperative settings, remote consultations showed high concordance with in-person plans, similar complication or reattendance rates, reduced travel, and high satisfaction. In trauma networks, telemedicine supported timely triage and reduced unnecessary inter-hospital transfers. Evidence in oral oncology and complex mucosal disease was more cautious, favouring hybrid models and escalation to face-to-face assessment. Data on cost-effectiveness and impacts on equity were limited. Conclusions: Telemedicine in OMFS has moved from niche innovation to a pragmatic adjunct across the clinical pathway. Current evidence supports its use for selected pre- and postoperative care and trauma triage within risk-stratified hybrid models, while underscoring the need for stronger comparative and implementation studies, clear governance on equity and data protection, and alignment with wider digital and AI-enabled health systems. Full article

Background/Objectives: Cardiovascular (CV) diseases remain the leading cause of morbidity and mortality across Europe. Despite substantial progress in prevention, diagnostics, and therapeutics, outpatient cardiology care continues to face systemic challenges, including limited consultation time, workforce constraints, and incomplete clinical information at the point of care. The primary objective of this study is threefold. First, to evaluate whether AI-enhanced telemedicine improves clinical control of hypertension, dyslipidemia, and heart failure compared to standard ambulatory care. Second, to assess the impact on physician workflow efficiency and documentation burden through AI-assisted clinical documentation. Third, to determine patient satisfaction and safety profiles of integrated telemedicine–AI systems. Clinical control will be measured by a composite endpoint of disease-specific targets assessed at the 12-month follow-up visit. Methods: The TeleAI-CVD Concept Study aims to evaluate the integration of telemedicine and artificial intelligence (AI) to enhance the efficiency, quality, and individualization of cardiovascular disease management in the ambulatory setting. Within this framework, AI-driven tools will be employed to collect structured clinical histories and current symptomatology from patients prior to outpatient visits using digital questionnaires and conversational interfaces. Results: Obtained data, combined with telemonitoring metrics, laboratory parameters, and existing clinical records, will be synthesized to support clinical decision-making. Conclusions: This approach is expected to streamline consultations, increase diagnostic accuracy, and enable personalized, data-driven care through continuous evaluation of patient trajectories. The anticipated outcomes of the TeleAI-CVD study include the development of optimized, AI-assisted management protocols for cardiology patients, a reduction in unnecessary in-person visits through effective telemedicine-based follow-up, and accelerated attainment of therapeutic targets. Ultimately, this concept seeks to redefine the paradigm of outpatient cardiovascular care by embedding advanced digital technologies within routine clinical workflows. Full article

This paper describes a compact low-cost telemetry system featuring ready-made sensors and an acquisition unit based on the ESP32, which makes use of the LoRa/Wi-Fi wireless standard for communication, and autonomous fallback logging to guarantee data recovery during communication loss. Ensuring safe atmospheric re-entry requires reliable onboard monitoring of capsule conditions during descent. The system is intended for sub-orbital, low-cost educational capsules and experimental atmospheric descent missions rather than full orbital re-entry at hypersonic speeds, where the environmental loads and communication constraints differ significantly. The novelty of this work is the development of a fully self-contained telemetry system that ensures continuous monitoring and fallback logging without external infrastructure, bridging the gap in compact solutions for CubeSat-scale capsules. In contrast to existing approaches built around UAVs or radar, the proposed design is entirely self-contained, lightweight, and tailored to CubeSat-class and academic missions, where costs and infrastructure are limited. Ground test validation consisted of vertical drop tests, wind tunnel runs, and hardware-in-the-loop simulations. In addition, high-temperature thermal cycling tests were performed to assess system reliability under rapid temperature transitions between −20 °C and +110 °C, confirming stable operation and data integrity under thermal stress. Results showed over 95% real-time packet success with full data recovery in blackout events, while acceleration profiling confirmed resilience to peak decelerations of ~9 g. To complement telemetry, the TeleCapsNet dataset was introduced, facilitating a CNN recognition of descent states via 87% mean Average Precision, and an F1-score of 0.82, which attests to feasibility under constrained computational power. The novelty of this work is twofold: having reliable dual-path telemetry in real-time with full post-mission recovery and producing a scalable platform that explicitly addresses the lack of compact, infrastructure-independent proposals found in the existing literature. Results show an independent and cost-effective system for small re-entry capsule experimenters with reliable data integrity (without external infrastructure). Future work will explore AI systems deployment as a means to prolong the onboard autonomy, as well as to broaden the applicability of the presented approach into academic and low-resource re- entry investigations. Full article