Neuroimaging and machine learning in eating disorders: a systematic review

Purpose Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED),
are complex psychiatric conditions with high morbidity and mortality. Neuroimaging and machine learning (ML) represent
promising approaches to improve diagnosis, understand pathophysiological mechanisms, and predict treatment response.
This systematic review aimed to evaluate the application of ML techniques to neuroimaging data in EDs.
Methods Following PRISMA guidelines (PROSPERO registration: CRD42024628157), we systematically searched PubMed
and APA PsycINFO for studies published between 2014 and 2024. Inclusion criteria encompassed human studies using
neuroimaging and ML methods applied to AN, BN, or BED. Data extraction focused on study design, imaging modalities,
ML techniques, and performance metrics. Quality was assessed using the GRADE framework and the ROBINS-I tool.
Results Out of 185 records screened, 5 studies met the inclusion criteria. Most applied support vector machines (SVMs) or
other supervised ML models to structural MRI or diffusion tensor imaging data. Cortical thickness alterations in AN and
diffusion-based metrics effectively distinguished ED subtypes. However, all studies were observational, heterogeneous, and
at moderate to serious risk of bias. Sample sizes were small, and external validation was lacking.
Conclusion ML applied to neuroimaging shows potential for improving ED characterization and outcome prediction. Nev-
ertheless, methodological limitations restrict generalizability. Future research should focus on larger, multicenter, and mul-
timodal studies to enhance clinical applicability.
Level of Evidence: Level IV, multiple observational studies with methodological heterogeneity and moderate to serious risk
of bias.