Machine learning or morphometric scaling? a systematic review of methodological confounds and the generalizability of sex classification in neuroimaging - Sapuan, Abdul HaliM; Jamaludin, Iqbal; Abdul Majid, Zafri Azran; Mohd Tamrin, Mohd Izzuddin; Che Azemin, Mohd Zulfaezal; Turaev, Sherzod (Open Exploration Publishing Inc., 2026)
Background: This systematic review critically evaluates whether machine learning (ML) identifies biologically meaningful sex-related brain architecture or merely exploits methodological artifacts and allometric scaling. While ML models achieve high classification accuracies, it remains unclear if these reflect stable, mechanistically informative dimorphism or are driven by confounds such as total intracranial volume (TIV) and site-specific noise. We examine how imaging modalities, algorithms, and population strata influence both classification outcomes and biological interpretability. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Web of Science, PubMed, and Scopus through January 2024. Included studies [healthy humans, 3T magnetic resonance imaging (MRI), ML-based sex classification] were assessed for risk of bias, focusing on data leakage, validation strategies, and confound management. Results: Thirty-five studies (n > 110,000) were included. While reported accuracies reached 98.06% for T1-weighted MRI, 96.0% for diffusion MRI (dMRI), and 94.72% for functional MRI (fMRI), performance was highly dependent on population characterization and age. Deep learning consistently outperformed traditional ML (TML) but showed high sensitivity to methodological artifacts. Notably, studies failing to correct for TIV reported potentially inflated accuracies, suggesting that many models identify physical scale rather than intrinsic neuroanatomical dimorphism.