Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis.

ABSTRACT: Novel features derived from imaging and artificial intelligence systems are commonly coupled to construct computer-aided diagnosis (CAD) systems that are intended as clinical support tools or for investigation of complex biological patterns. This study used sulcal patterns from structural images of the brain as the basis for classifying patients with schizophrenia from unaffected controls. Statistical, machine learning and deep learning techniques were sequentially applied as a demonstration of how a CAD system might be comprehensively evaluated in the absence of prior empirical work or extant literature to guide development, and the availability of only small sample datasets. Sulcal features of the entire cerebral cortex were derived from 58 schizophrenia patients and 56 healthy controls.

SUBMITTER: Jimenez-Mesa C 

PROVIDER: S-EPMC10973702 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature