Machine Learning for Diagnosis and Differentiation of Central Disorders of Hypersomnolence: A Systematic Review

BACKGROUND AND PURPOSE: Central disorders of hypersomnolence (CDH) are, except for Narcolepsy Type 1 (NT1), difficult to diagnose and manage because of overlapping features and the lack of reliable biomarkers. Machine learning (ML) has the potential to improve diagnosis by detecting subtle physiological patterns and distinguishing between CDH subtypes. This review systematically explores current ML applications in CDH, assesses their limitations, and suggests future directions. METHODS: Following PRISMA guidelines, MEDLINE, Embase, PsycINFO, IEEE Xplore, CINAHL, Web of Science, and Google Scholar (up to June 2025) were searched for studies using ML to classify or characterize CDH in adults. ML methods, data types, and diagnostic outcomes were extracted and analyzed. RESULTS: Out of 3274 studies, 41 met the inclusion criteria (37 peer-reviewed articles and 4 preprints). Data sources included neuroimaging (fMRI, PET), sleep assessments (MSLT, polysomnography), demographics, and standardized questionnaires. Supervised ML reliably identified known features, including early REM onset, hypocretin deficiency, and spectral EEG changes, showing strong performance for NT1 but limited generalizability across other CDH subtypes. Although many studies reported high accuracy, clinical relevance was often limited by rigid diagnostic labels that may not reflect the true complexity of CDH. Unsupervised learning uncovered heterogeneous phenotypes and exposed limitations in existing diagnostic labels. CONCLUSION: ML has the potential to improve CDH diagnosis. Deep learning models are promising for feature extraction; however, their black-box nature and high data requirements hinder clinical application. Future advancements depend on large, diverse datasets, multimodal and longitudinal data, and close collaboration between clinicians and data scientists.