Search Results (675)

Background: Chronic subdural haematoma (cSDH) is one of the most common neurocranial conditions in older adults and is increasing in incidence. Conservatively managed patients are typically older, frailer, and more comorbid than surgical candidates, yet specialist geriatrician input into their management is rarely formalised. Potential for geographic inequity in hub-and-spoke neurosurgical networks within the UK compounds this challenge. We describe a service evaluation of a novel regional geriatric trauma advice service providing specialist trauma geriatrician input into cSDH management alongside the neurosurgical team, across a single UK Neurosurgical Network. To the authors’ knowledge, this is the first of its kind in the UK. Prior to this service ‘normal’ care within our region was no routine specialist medical input. Methods: A service evaluation of all referrals received by the North Bristol National Health Service (NHS) Trust geriatric trauma team between July 2024 and March 2026 was performed by the authors. Referrals were discussed within a multidisciplinary team including geriatric trauma, interventional neuroradiology and neurosurgery. Individuals’ demographic data, advice types, management enhancement, intervention outcomes and mortality were recorded and analysed descriptively. Management enhancement was defined as a definitive management decision that resulted in either an action or an active decision not to act. Results: Ninety referrals were included. Median age was 83 years (IQR 77–88) and median Clinical Frailty Scale (CFS) score was 4, with 50% classified as frail (CFS 4–8). Referrals were received from five satellite NHS trusts and the community. Three-quarters (75.6%) required multiple concurrent advice types; Middle Meningeal Artery Embolisation (MMAE) suitability (93.3%) and antithrombotic management (51.1%) were the most frequent. Specialist geriatrician input enhanced management in 97.8% of cases. Forty percent of conservatively managed patients were converted to active intervention, most commonly standalone MMAE (50%). Of district general hospital patient referrals, 40.3% were conveyed to the neurosurgical centre following geriatric trauma review. Mortality was comparatively lower at multiple time points when compared to existing UK data for conservatively managed patients. Conclusions: A regional virtual geriatric trauma advice service for older adults with cSDH is feasible and is associated with enhanced management of this cohort, potentially improving access to treatment. Combining neurosurgical, neurointerventional and geriatrician expertise through a structured multi-disciplinary team (MDT) approach addresses the clinical complexity of this patient group and the potential geographic inequity of hub-and-spoke networks. Further research is needed to investigate the impact of routine geriatrician input into cSDH care. Full article

This article examines how journalism is practised beyond institutional frameworks in the United Kingdom, using esports journalism as a case through which to explore professional identity, labour conditions, and journalistic authority. Drawing on a mixed-methods design, the study combines content analysis of UK mainstream media coverage, surveys of journalists, and eight semi-structured interviews with esports journalists. The findings reveal a substantial divergence between journalistic practice and professional recognition: participants routinely performed core journalistic functions while often rejecting the identity of ‘journalist’ and entering the field through informal pathways. Labour conditions were characterised by precarity, freelance work, and the blurring of editorial and commercial roles, while access to information was frequently mediated by industry stakeholders. Mainstream media engagement with esports remained limited, indicating the existence of a specialist journalism ecosystem operating largely outside established institutional structures. Esports journalism therefore provides a useful analytical case through which to understand processes of de-professionalisation, in which institutional boundaries weaken while journalistic work extends beyond established professional frameworks. The study argues that the growing separation between journalistic practice and professional recognition invites a reconsideration of how journalism is defined, organised, and legitimised within platform-based media environments. Full article

Background: Returning home after neurological hospitalization, rehabilitation, or specialist care transfers responsibility to patients, caregivers, and community services. We mapped mechanisms and gaps across dementia/Alzheimer’s disease and related dementias (ADRD), Parkinson’s disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS). Methods: Following JBI guidance and PRISMA-ScR, eligibility was derived using population–concept–context. We included empirical reports involving adults with a target condition, a post-discharge, return-home, rehabilitation, telehealth, caregiver, treatment, respiratory, or palliative continuity component, and post-transition patient, caregiver, service, safety, rehabilitation, equity, or implementation outcomes. Five databases were searched through to 11 May 2026. Two reviewers independently screened records; charting and classification were verified by R.S.C., A.C., and A.Q. Results: Of 24,417 records, 69 reports were included: Dementia/ADRD, 28; PD, 10; MS, 9; and ALS, 22. Eighteen were core transition reports (26.1%), 14 return-home/community re-entry reports (20.3%), 16 adjacent continuity reports (23.2%), and 21 companion/secondary reports (30.4%). Dementia/ADRD provided discharge-anchored evidence; PD and MS mapped functional carry-over; ALS mapped adjacent respiratory, telehealth, and palliative continuity. Conclusions: The main contribution is an operational cross-disease framework separating direct discharge, return-home, adjacent-continuity, and companion evidence while linking mechanisms to disease-specific pathways. This framework maps disease-specific functions, not comparative effectiveness. The proposed frameworks are author-derived and hypothesis-generating. Future studies should use explicit anchors, standardized outcomes, longer follow-up, and equity-sensitive implementation measures addressing caregiver workload, digital access, feasibility, and sustainability. They inform testable, context-sensitive intervention designs for future neurological transition-care research and practice. Full article

Early detection of autism spectrum disorder (ASD) in young children is essential for timely referral, developmental monitoring, and access to early intervention. However, conventional screening and diagnostic pathways often depend on parent-report instruments, episodic clinical observation, and specialist-administered assessments, which may delay identification during the first years of life. This scoping review maps the methodological landscape of early ASD detection from traditional clinical screening to multimodal artificial intelligence (AI). A structured literature search was conducted across major biomedical, psychological, and engineering databases for studies published between January 2010 and May 2026. After screening and eligibility assessment, 65 evidence sources were included in the qualitative synthesis, with additional methodological guidelines used to support reporting and appraisal. The reviewed evidence shows that early ASD detection is increasingly shifting from single-session clinical assessment toward multidimensional risk characterization. Clinical and behavioral screening tools remain the foundation of early identification, while eye tracking, video-based motor analysis, acoustic and vocal biomarkers, electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and molecular or genomic indicators provide complementary information across different developmental windows. AI-based methods, including machine learning, deep learning, Transformer architectures, multimodal fusion strategies, and foundation-model-based representation learning, may improve the objective quantification of gaze, movement, vocalization, neural activity, and biological risk. Nevertheless, most AI-assisted systems remain limited by small and heterogeneous datasets, insufficient external validation, population bias, privacy concerns, computational burden, and limited interpretability. This review argues that future early ASD detection systems should be developed as clinician-supervised decision-support tools rather than autonomous diagnostic instruments. Clinically meaningful progress will require robust external validation, privacy-preserving deployment, age-appropriate risk stratification, and intrinsically interpretable architectures that align model outputs with developmental and clinical knowledge. Full article

Mild traumatic brain injury (mTBI) is a common cause for pediatric emergency department visits and can impose substantial burdens on families, including missed work and childcare needs. Socioeconomic context may influence both healthcare use and these downstream impacts. This study examined the association between Child Opportunity Index (COI), a neighborhood-level measure of socioeconomic status, and healthcare utilization and indirect costs following pediatric concussion. In a secondary analysis of a multisite randomized controlled trial of patients with acute (<72 h) concussion, participants were categorized into high, middle, and low COI groups. Outcomes included follow-up care, caregiver missed workdays, childcare needs, and indirect costs, analyzed using multivariable regression models. Participants from lower COI neighborhoods were more likely to present to the emergency department, with 93.02% compared to 71.43% in higher COI groups (p = 0.005); had higher symptom scores at 14 days, 28.69 versus 16.64 (p = 0.03); and were less likely to be seen by concussion specialists, 6.98% versus 28.57% (p = 0.005). They also had lower odds of attending follow-up within 14 days (OR = 0.36, p = 0.015) and higher caregiver burden, including more missed workdays and childcare needs. Although total indirect costs did not differ significantly for low COI groups after adjustment, middle COI participants had lower costs than high COI ones. Overall, disparities were observed in care patterns and caregiver burden despite similar recovery outcomes. These findings suggest that expanding access to affordable concussion follow-up through school-based programs, telehealth services, transportation assistance, and flexible scheduling may help reduce caregiver burden and improve continuity of care for children from lower COI neighborhoods following pediatric mTBI. Full article

Background: The COVID-19 pandemic substantially disrupted healthcare systems worldwide, raising concerns regarding delays in diagnosis, alterations in treatment pathways, and oncological outcomes for women with advanced ovarian cancer (AOC). This study evaluated the impact of the pandemic on the management and survival of patients with AOC treated at a high-volume ESGO-accredited tertiary gynaecological oncology centre. Methods: A retrospective cohort study was conducted using a prospectively maintained database of consecutive women diagnosed with FIGO stage III–IV epithelial ovarian, fallopian tube or primary peritoneal cancer between January 2017 and December 2022. Patients were divided into pre-pandemic and pandemic cohorts. Demographic characteristics, FIGO stage, treatment pathways, surgical outcomes, and overall survival were compared. Survival was analysed using Kaplan–Meier methodology and multivariable Cox proportional hazards regression was performed to evaluate the independent association between the pandemic period and overall survival after adjustment for age, FIGO stage, ECOG performance status and treatment modality. Results: A total of 700 patients were included, 406 patients treated before the pandemic and 294 during the pandemic. Patients diagnosed during the pandemic had a better ECOG performance status, but 51.3% presented with FIGO stage IV disease. Overall treatment rates were maintained (84.4% vs. 87.4% pre-pandemic), although treatment strategies shifted towards increased use of neoadjuvant chemotherapy (55% vs. 42.4%, p < 001), with a greater proportion of patients subsequently undergoing interval debulking surgery. Complete cytoreduction following interval debulking surgery was preserved throughout the study period, despite more advanced disease at presentation. One-year overall survival was similar between cohorts (69% vs. 72.2% pre-pandemic), whereas three-year overall survival was significantly lower among patients treated during the pandemic (36.1% vs. 45.1%). On multivariable Cox regression analysis, treatment during the pandemic remained independently associated with poorer overall survival after adjustment for clinically relevant baseline factors. Conclusions: Despite unprecedented disruption to healthcare services, specialist gynaecological oncology care has maintained access to multimodality treatment and preserved maximal effort cytoreductive surgery. Nevertheless, patients diagnosed during the pandemic experienced inferior long-term survival independent of baseline disease characteristics and treatment allocation, highlighting the broader indirect consequences of the pandemic on cancer outcomes. These findings emphasise the importance of protecting diagnostic pathways, multidisciplinary cancer services and surgical capacity during future healthcare crises. Full article

Diabetic retinopathy (DR) and glaucoma, globally leading causes of irreversible blindness, can be detected and managed early with timely screening. In low-resource settings, access to ophthalmological examination is limited by a variety of constraints, including scarcity of specialists, equipment costs, geographic distance from care centers, and limited patient awareness. This narrative review examines the principal barriers to early detection of glaucoma and DR and evaluates evidence-based strategies to overcome them, including low-cost portable ophthalmic testing tools, artificial intelligence (AI) analytics, risk-based resource allocation, teleophthalmology, and targeted patient education. In different populations, these strategies have been shown to meaningfully increase access to ophthalmologic screening and follow-up at a significantly lower cost. We describe these approaches, demonstrate previously successful initiatives, and propose various combined approaches adapted to the specific needs of the various low-resource settings. Full article

Background: Hyperlipidaemia is common and most patients can be managed with established oral therapies and lifestyle modifications. However, patients with complex high-risk lipid disorders require specialist input and access to novel therapies to achieve treatment targets. While new agents are progressing through late phase trials, the eligibility criteria of these studies may limit their applicability to real-world settings. Aim: Assessing the generalisability of novel lipid-lowering therapy (LLT) trials by mapping their eligibility criteria to a cohort of patients attending a specialist lipid clinic. Methods: An ethically approved study was conducted at a specialist clinic in St James’s Hospital, Dublin over 6 months, enrolling 370 patients. Demographics, clinical characteristics, lipid profiles, genetic testing results and LLT use were collected. A structured search of clinicaltrials.gov and euclinicaltrials.eu identified phase 3 pharmacological trials registered since 2018. Patient data were then mapped against trial eligibility criteria to estimate the proportion of clinic patients that would have been eligible for each study. Results: In total, 38 trials were identified, including 15 different novel LLTs. It was found that the percentage of the cohort (n = 370) that would have been eligible for these LLT trials ranged from 0% to 62.4%, with an average eligibility of 19% across the 38 trials. Further analysis of specific patient subgroups and corresponding trial categories was performed. Conclusions: The included novel LLTs trials are reasonably applicable to a specialist lipid clinic cohort, suggesting generalisability to a wider group of real-world patients seen in clinics. This study can help guide future trial design by aligning eligibility criteria more closely with real-world patient populations, ensuring safe and effective clinical practice. Full article

Objectives: Oral potentially malignant disorders (OPMDs) carry a variable risk of malignant transformation, making early detection important. Deep learning relies on specialized imaging, which is often inaccessible in routine practice, and detection from standard clinical photographs remains poorly characterized. We assessed the feasibility of automated OPMD detection from white-light intraoral photographs, compared two convolutional neural network paradigms (global classification with DenseNet-121 versus one-stage instance segmentation with YOLOv8), and identified the main barriers to clinical translation. Methods: A dataset of 1500 photographs (750 OPMD and 750 non-OPMD) from institutional archives and publicly accessible sources was split in an 80:20 ratio for training and testing. Lesion boundaries were annotated by three trainees using Cytomine and validated by three specialists. The DenseNet-121 and YOLOv8-large-segmentation models were evaluated for accuracy, sensitivity, specificity, precision, F1 score, and Wilson 95% CI. Results: DenseNet-121 required extensive manual lesion cropping to converge, negating the automation rationale. YOLOv8-large-segmentation reached 62.2% accuracy (95% CI 56.4 to 67.6), 75% sensitivity (95% CI 67.3 to 81.4), 59.7% precision (95% CI 52.4 to 66.5), 49.3% specificity (95% CI 41.3 to 57.4), and an F1 score of 66.5%, detecting lesions in 96% of the test set. High sensitivity was obtained at a low confidence threshold of 0.1, with correspondingly reduced specificity, and the model produced interpretable color-coded segmentation masks. Conclusions: One-stage instance segmentation is the more viable direction and yields spatially interpretable output, but performance at this dataset scale is not yet clinically sufficient. Dataset scale, threshold calibration, low specificity, and absent patient metadata are the key barriers to address. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder worldwide, affecting approximately 56.9 million people in 2021 and projected to reach 152 million by 2050. Its defining pathological features, amyloid-beta plaques and neurofibrillary tangles, accumulate for up to two decades before cognitive symptoms emerge, placing the preclinical and mild cognitive impairment (MCI) stages at the centre of the early detection problem. Despite this, current diagnostic practice in routine clinical settings remains unreliable, with post-mortem studies placing the specificity of clinical AD diagnosis between 44.3 and 70.8% even in specialist memory clinics. Machine learning (ML) and deep learning (DL) applied to neuroimaging and electrophysiological data have emerged as candidate tools for closing this diagnostic gap, yet whether the accuracy figures reported in published studies translate into clinically useful performance on independent data remains unresolved. This study presents a structured comparative review of machine learning and deep learning methods reported across four publicly available Alzheimer’s disease datasets, namely the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Open Access Series of Imaging Studies (OASIS), the OpenNeuro ds004504 electroencephalography (EEG) dataset, and the Kaggle Alzheimer’s magnetic resonance imaging (MRI) dataset. Thirteen model families are examined through the published literature rather than through new experiments, and for each model and dataset combination, the best accuracy reported in the source study is recorded alongside the model’s mathematical formulation. All performance figures reported in this abstract and throughout the paper are taken from the published studies reviewed, not from new experiments conducted by the authors. Across the reviewed studies, deep learning architectures pre-trained on ImageNet and fine-tuned on neuroimaging data are reported to produce the highest accuracy on MRI classification tasks. Residual Network (ResNet)-101 is reported at 98.21 percent on ADNI and 97.45 percent on OASIS, while the IncepRes fusion architecture reaches 98.35% on OASIS by combining multi-scale feature extraction from InceptionV3 with residual connectivity from ResNet152V2. Traditional machine learning classifiers remain competitive on tabular clinical and biomarker data, with Extreme Gradient Boosting (XGBoost) reaching 91% on ADNI multiclass features. Logistic Regression achieves 82 to 85% on binary ADNI classification and is the only classifier in this review that provides explicit per-feature prediction contributions without post hoc tooling. Gaussian Naïve Bayes achieves 80 to 83% on the same task. On the OpenNeuro EEG dataset, K-nearest neighbours (KNN) with singular value decomposition (SVD) entropy features achieves 91% binary accuracy, with feature engineering quality determining performance more reliably than classifier architecture. Eight principal findings emerge from the cross-dataset analysis. Binary classification consistently outperforms multiclass by 10 to 30% across all datasets, reflecting the genuine biological ambiguity of the mild cognitive impairment category. Dataset size and augmentation predict reported accuracy more reliably than model architecture. Ensemble methods outperform individual classifiers by 5 to 8% in nearly every imaging study. Deeper architectures can overfit small clinical cohorts. EEG models trail MRI models by approximately 10 to 15% on comparable binary tasks. Cross-dataset generalisation has not been systematically evaluated in most studies, and the few that have tested it report accuracy drops of 5 to 10% or more when models encounter data from different scanners or cohorts. Eight recurring limitations constrain the clinical utility of these findings. Small sample sizes and limited demographic diversity, severe class imbalance inflating raw accuracy metrics, poor cross-dataset generalisation driven by scanner heterogeneity, limited deep learning interpretability, the dominance of binary over multiclass tasks, the absence of longitudinal modelling despite available datasets, inadequate standardisation of preprocessing and evaluation protocols, and the signal-to-noise ratio constraints specific to EEG recordings of elderly patients collectively define the gap between benchmark performance and clinical readiness. Future work must prioritise multi-centre training cohorts, multimodal fusion architectures, longitudinal progression modelling, and standardised interpretability evaluation as non-optional requirements for any system intended for clinical deployment. Full article

Access to neurological care remains profoundly unequal worldwide, driven by the increasing burden of chronic and neurodegenerative disorders and a persistent shortage of specialist neurologists. Tele-neurology has emerged as a promising strategy to improve access to neurological expertise, while recent advances in artificial intelligence (AI) have expanded its capabilities beyond remote consultation toward data-driven diagnosis, monitoring, and clinical decision support. This narrative review critically synthesizes current evidence on the evolution of tele-neurology and the emerging role of AI across multiple domains of neurological care. The review examines AI-enhanced diagnostic applications, including neuroimaging, electroencephalography, and digital biomarkers, as well as AI-assisted remote monitoring, patient engagement, and predictive analytics in disorders such as stroke, epilepsy, Parkinson’s disease, multiple sclerosis, dementia, headache disorders, and neuromuscular diseases. Available evidence suggests that tele-neurology can achieve clinical outcomes comparable to in-person care in selected settings while improving accessibility, continuity of care, and patient satisfaction. AI applications have shown promising early results in image interpretation, automated EEG analysis, remote disease monitoring, and individualized risk prediction. Despite these advances, important challenges remain. Limitations include constraints in remote neurological examination, evidence gaps regarding clinical validation, unequal access to digital infrastructure, data governance and cybersecurity concerns, fragmented regulatory frameworks, and difficulties integrating AI into routine clinical workflows. Furthermore, the clinical maturity of AI applications varies substantially, with many systems remaining at the proof-of-concept or early validation stage. To make this variation explicit, we introduce a transparent, criteria-based three-tier classification of the clinical practicability of AI applications, graded by strength of evidence, degree of validation, and real-world integration. Tele-neurology is increasingly evolving toward hybrid models that combine in-person neurological assessment with digitally enabled longitudinal monitoring and AI-supported decision tools. Future progress will depend on rigorous clinical validation, equitable implementation strategies, integration into healthcare systems, and the development of multimodal AI approaches that combine clinical, imaging, electrophysiological, and digital biomarker data to support personalized neurological care. Full article

Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disease primarily affecting upper and lower motor neurons. Although cerebrovascular disease (CVD) and ALS have traditionally been studied as distinct entities, a growing body of evidence indicates meaningful epidemiological, pathophysiological, and clinical overlap between the two conditions. This narrative review synthesizes current evidence on the coexistence of ALS and cerebrovascular disease, examines shared mechanistic pathways, addresses diagnostic challenges, including stroke mimicry, considers clinical management implications, and identifies priorities for future research. A search of PubMed, Scopus, Web of Science, and EMBASE was conducted through February 2026 using the terms “amyotrophic lateral sclerosis,” “motor neuron disease,” “cerebrovascular disease,” “stroke,” “ischemic stroke,” “blood-brain barrier,” “neuroinflammation,” and “neurovascular coupling,” alone and in combination. Peer-reviewed original research, systematic reviews, meta-analyses, population-based studies, registry analyses, and expert consensus statements were included. Studies were assessed for methodological quality and relevance to the review objectives. This review is reported as a narrative synthesis. Population-based data demonstrate a bidirectional relationship between ALS and cerebrovascular events. ALS patients face an approximately 2.6-fold elevated risk of ischemic stroke, and prior cerebrovascular injury modestly increases subsequent ALS risk. Shared pathophysiological mechanisms include neuroinflammation with microglial M1/M2 polarization imbalance, pro-inflammatory cytokine cascades mediated via NF-κB signaling, oxidative stress and SOD1 pathway dysregulation, glutamate excitotoxicity, blood–brain barrier (BBB) dysfunction, and impaired neurovascular coupling. Diagnostic confusion arises because upper motor neuron–predominant ALS can closely mimic acute ischemic stroke. Concurrent cerebrovascular disease appears to accelerate functional decline and reduce survival in ALS. Resource-limited settings face compounded challenges from diagnostic misclassification, restricted EMG access, and limited specialist availability. The ALS–cerebrovascular overlap is clinically relevant, biologically plausible, and systematically understudied. Integrated multidisciplinary management, prospective longitudinal cohort studies, and linked registry analyses are urgently needed to clarify causal relationships, characterize shared disease mechanisms, and improve patient outcomes. Full article

The timely integration of technology in education is a key factor in developing competitive specialists. Given this, the development of a digital educational platform for training engineering specialists is of strategic importance. The paper presents the development of a proprietary digital educational platform using immersive technologies for training engineering specialists while providing educators with easier access to their educational progress and ability to spend less time processing it. The results are considered data from an initial test, aimed primarily at assessing user perception, usability, and the potential of the immersive environment. The platform’s effectiveness stems from its ability to simulate complex processes and allow students to independently control the experiment. Full article

This article reports the design, implementation, and formative perception-based evaluation of an early-stage virtual reality (VR) prototype that integrates a virtual cane, localized haptic feedback, 3D audio, and a three-layer bioinspired sensing framework. The prototype was implemented in Unity 2022.3 using the XR Interaction Toolkit and URP and was structured according to design science research methodology (DSRM). The bat–whisker–contact framework was used as a functional abstraction to organize distal auditory reference or warning, proximal haptic feedback, and contact confirmation; it was not evaluated against a non-bioinspired baseline. The completed evaluation consisted of an anonymous, voluntary, post-use questionnaire administered to 25 sighted participants who could select which visual-input configurations to experience. The analysis focused on reported clarity, tolerability, initial signal interpretability, and design feedback; it did not include objective navigation metrics or assess clinical efficacy, training transfer, accessibility outcomes, or orientation-and-mobility performance in blind or low-vision users. General responses suggested favorable perceived clarity and multimodal usefulness, while cane length, floor-versus-wall/obstacle differentiation, and reported discomfort identified priorities for technical refinement. In the simulated no-vision condition (n = 21), participants reported high reliance on the cane response, whereas reported initial insecurity or doubt and basic mental map ratings remained mixed. The study contributes an early-stage technological artifact and a formative basis for subsequent controlled evaluations with objective performance measures, reference conditions, and target users or orientation and mobility specialists. Full article

Background: Tobacco smoking remains one of the leading preventable causes of morbidity and mortality worldwide and is associated with cardiovascular, respiratory, oral, and psychological disorders. Although conventional smoking cessation interventions are effective, barriers related to accessibility, adherence, healthcare costs, and continuity of care frequently limit long-term success. Recent advances in technology may offer innovative opportunities to improve multidisciplinary smoking cessation pathways. Methodology: The Wear-Smile project is a non-profit, monocentric, randomized controlled trial registered in the Clinical Trial Protocol Registry and Results System (code No.: NCT07593742), designed to evaluate the effectiveness of a multidisciplinary telemedicine-based smoking cessation program assisted by wearable remote-monitoring devices. Adults who smoke combustible or heated tobacco or electronic nicotine delivery systems will be randomly allocated in a 1:1 ratio to either an experimental group receiving telemedicine-assisted rehabilitation integrated with wearable monitoring devices or a control group receiving standard in-person smoking cessation care. The intervention will include a multidisciplinary behavioral smoking cessation intervention involving cardiology, respiratory, dental, and psychology specialists. Primary outcomes will include Continuous Abstinence Rates and Point Prevalence Abstinence assessed at 6 and 12 months. Secondary outcomes will include cardiopulmonary and oral health parameters, smoking-related variables, health- and oral health-related quality of life (QoL), as well as adherence, usability, acceptability, and satisfaction with the rehabilitation program. Conclusions: The project may provide preliminary evidence regarding the potential feasibility, acceptability, and effectiveness of a multidisciplinary smoking cessation program integrating telemedicine and wearable monitoring technologies in improving abstinence outcomes, patient engagement, continuity of care, and QoL. Full article