Neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS), pose a growing global health burden with limited early diagnostic tools. Radiomics, which extracts high-dimensional quantitative features from medical images [1, 2], combined with artificial intelligence (AI) methods, has emerged as a promising approach to enhance diagnostic accuracy and prognostic prediction in neuroimaging. However, no prior systematic review has comprehensively evaluated the methodological quality, reproducibility challenges, and diagnostic performance of AI-driven radiomics studies across multiple neurodegenerative diseases using established quality assessment frameworks. This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [3]. A comprehensive literature search was performed across PubMed/MEDLINE, Scopus, Web of Science Core Collection, and Embase databases from January 2017 to March 2026. Grey literature sources including conference proceedings from RSNA, ISMRM, and OHBM were systematically searched but excluded from the final synthesis. Two independent reviewers screened titles, abstracts, and full texts with substantial inter-reviewer agreement (Cohen’s kappa = 0.87). Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) tool and the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Disagreements were resolved through consensus discussion, with a third reviewer consulted when necessary. Data extraction included study design, imaging modality, radiomic feature extraction methodology, AI/ML algorithm, sample size, performance metrics, validation strategy, and external validation status. From 1452 screened records, 60 studies met inclusion criteria and were included in the qualitative synthesis. The majority focused on AD and mild cognitive impairment (MCI) (n = 35, 58%), followed by PD and movement disorders (n = 15, 25%), FTD (n = 6, 10%), and other neurodegenerative conditions (n = 4, 7%). Structural MRI was the most commonly used modality (n = 38, 63%), followed by PET (n = 14, 23%) and SPECT (n = 8, 13%). Support vector machines (n = 22), convolutional neural networks (n = 18), and random forests (n = 12) were the most frequently employed AI methods. Reported area under the receiver operating characteristic curve (AUC) values ranged from 0.75 to 0.98 for AD diagnosis and 0.78 to 0.95 for PD classification. However, quality assessment revealed that only 12 studies (20%) performed external validation, and 28 studies (47%) were rated as having high risk of bias, primarily due to small sample sizes, lack of independent test sets, absence of prospective validation, and inadequate reporting of feature extraction parameters. Stratified analysis revealed that studies employing deep learning methods reported significantly higher AUC values (median 0.91) compared to classical machine learning approaches (median 0.85), though deep learning studies also exhibited higher risk of bias due to greater model complexity relative to sample sizes. Meta-analysis was not feasible due to substantial heterogeneity in imaging protocols, feature extraction pipelines, and outcome definitions. AI-driven radiomics demonstrates potential for improving neuroimaging-based diagnosis and prognosis of neurodegenerative diseases. However, the field remains substantially limited by methodological heterogeneity, insufficient external validation (only 20% of studies), high risk of bias (47% of studies), and critical reproducibility challenges including scanner variability, feature instability, and data leakage. The predominantly retrospective, single-center nature of existing evidence limits clinical generalizability. Future research should prioritize multi-center prospective validation with pre-registered protocols, standardized radiomics workflows adhering to Image Biomarker Standardisation Initiative (IBSI) guidelines, rigorous assessment of feature reproducibility across scanners and sites, and integration with multiomics data to facilitate responsible clinical translation.
Shih-Shuan Fang, Sheng-Han Chen· The Egyptian Journal of Radi...· 0 citations
Continuous physiological monitoring in pediatric patients is crucial for the early identification of critical events such as cardiac arrhythmias, seizures, and sepsis. Wearable devices embedded with analytical algorithms offer a novel approach for non-invasive, continuous surveillance. Despite rapid technological advances, a comprehensive evaluation of their diagnostic accuracy, clinical effectiveness, safety, and acceptability in pediatric cohorts remains lacking. To systematically evaluate the diagnostic performance, clinical outcomes, safety, and usability of wearable continuous monitoring devices with integrated analytical algorithms in pediatric patients (neonates to adolescents), compared with standard monitoring methods or wearable devices without analytical integration. A systematic search of PubMed, Embase, Cochrane Library, Web of Science, and Scopus was conducted from inception through March 31, 2026. Eligible studies included randomized controlled trials, prospective and retrospective cohorts, and diagnostic accuracy investigations involving wearable devices with embedded analytical algorithms for continuous monitoring in pediatric populations. Independent dual reviewers performed study selection, data extraction, and risk of bias assessment. Diagnostic accuracy metrics were synthesized using bivariate random-effects models. Clinical outcomes were pooled using random-effects meta-analyses. Certainty of evidence was appraised using the GRADE framework. From 3,842 screened records, 24 studies enrolling 4,376 pediatric patients were included. Wearable analytical devices demonstrated a pooled sensitivity of 87.4% (95% CI 82.1–91.5) and specificity of 89.2% (95% CI 85.0–92.4) for detecting critical events. Device use was associated with reduced hospitalization rates (RR 0.68; 95% CI 0.52–0.89) and shorter time to clinical intervention (MD − 1.8 h; 95% CI − 2.7 to − 0.9). Subgroup analyses showed consistent diagnostic accuracy across device modalities and clinical conditions. No significant publication bias was detected. Wearable continuous monitoring devices integrating analytical algorithms show promising diagnostic accuracy and potential clinical benefits in pediatric populations. However, the evidence base remains limited by moderate heterogeneity and a predominance of observational designs. Further large-scale, high-quality randomized controlled trials are warranted to validate long-term efficacy, safety, and cost-effectiveness before widespread clinical adoption.
Autoimmune encephalitis (AE) is an increasingly recognised cause of subacute encephalopathy, behavioural change, seizures, and movement disorders in children. The recognition of antibody-mediated syndromes—principally anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis—has transformed the field, and refined diagnostic criteria now allow earlier treatment initiation. However, diagnostic delay remains common in paediatric practice and contributes to long-term neurocognitive morbidity. We review the contemporary spectrum of paediatric autoimmune encephalitis, including anti-NMDAR encephalitis, MOG antibody-associated disease overlap syndromes, and seronegative AE phenotypes, with emphasis on early recognition, diagnostic algorithms, immunotherapy, and long-term outcomes. Anti-NMDAR encephalitis is the most frequent paediatric AE worldwide, characterised by behavioural change, language regression, seizures, dyskinesias, and autonomic instability; ovarian teratoma is uncommon in prepubertal children. MOG antibody-associated disease can present with encephalopathy, particularly in younger children, often with concurrent demyelinating features. Seronegative AE poses particular diagnostic challenges. Across cohorts, diagnostic delay is associated with worse outcomes, more relapses, and persistent cognitive, behavioural, and academic difficulties. Earlier recognition through clinician education, paediatric-specific diagnostic algorithms, and rapid antibody testing pathways is essential. Long-term neurocognitive surveillance and individualised rehabilitation are critical, even in children with apparently good motor recovery. Research priorities include validation of paediatric-specific diagnostic criteria, biomarker-guided immunotherapy escalation, and population-based cohorts capturing developmental trajectories.