HIGHLIGHTS
SUMMARY
The authors examined potential differences between FEP, CHR with later transition to psychosis (CHR_T), CHR with no transition to psychosis (CHR_NT), and HC, by employing six texture feature maps extracted from non-segmented MR images and feeding into a deep neural_network binary classification schema. Instead of applying conventional methods which show greater performance than deep neural_networks, the authors employed an innovative approach that addresses a frequent concern about artificial_intelligence methods, i.e., the explainability of results. The goal is to gain insights into the examined disorders using radiomics texture features and explainable AI which . . .
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