Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

AbstractBackground The development of machine learning models for aiding in the diagnosis of mental disorder is rec‑ognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains achallenge, with poor generalizability being a major limitation.Methods Here, we conducted a pre‑registered meta‑research assessment on neuroimaging‑based models in thepsychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a viewthat has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment.Based on these findings, we built a comprehensive 5‑star rating system to quantitatively evaluate the quality of exist‑ing machine learning models for psychiatric diagnoses.Results A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient(G) = 0.81, p 15). In light of this, we proposed a purpose‑built quantitative assess‑ment checklist, which demonstrated that the overall ratings of these models increased by publication year but werenegatively associated with model performance.Conclusions Together, improving sampling economic equality and hence the quality of machine learning modelsmay be a crucial facet to plausibly translating neuroimaging‑based diagnostic classifiers into clinical practice.Keywords Psychiatric machine learning, Diagnostic classification, Meta‑analysis, Neuroimaging, Sampling inequalitie