Advances in Machine Learning for the Identification and Phenoconversion Prediction of Isolated Rapid Eye Movement Sleep Behavior Disorder
Year: 2026
Type: article
Abstract: Isolated rapid eye movement sleep behavior disorder (iRBD) is an important prodromal clinical phenotype of α-synucleinopathies.Individuals with iRBD may subsequently develop Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy, although the timing and clinical endpoint of phenoconversion vary substantially among individuals.Machine learning can integrate video-polysomnography, electroencephalography, electrocardiography, neuroimaging, clinical scales, biomarkers, and digital behavioral data, providing new approaches to iRBD screening, computeraided identification, and individualized phenoconversion prediction.In this narrative review, we use clinical tasks as the organizing framework to summarize representative studies of automated identification, fixed-horizon phenoconversion classification, time-to-phenoconversion prediction, and disease-specific phenoconversion prediction.We also compare their study populations, input modalities, prediction endpoints, validation strategies, and performance metrics.Current evidence indicates that automated identification models have achieved good discriminatory performance in internal validation.By contrast, the prediction of phenoconversion timing and disease-specific outcomes remains largely limited to exploratory studies with small samples and internal validation.Insufficient sample sizes and phenoconversion events, inconsistent definitions of labels and endpoints, patient-level data leakage, limited external validation, and inadequate probability calibration are the main barriers to model reliability and clinical application.Future studies should establish standardized, multicenter longitudinal cohorts; adopt survival analysis, competing-risk, and dynamic prediction frameworks; and assess model calibration, clinical net benefit, cost, fairness, and effects on clinical workflows.These efforts will help shift the field from comparisons of internal model performance toward interpretable and externally validated risk tools that can support clinical management.
Source: Advances in Clinical Medicine
Author: 睿怡 张
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7205002617