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While traditional statistical analysis has been widely employed to investigate these MRI changes in CD, it has lacked the ability to predict individual\u2010level outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To address this problem, this paper has proposed an interpretable machine learning (ML) framework, including model\u2010level assessment, feature\u2010level assessment, and biology\u2010level assessment to ensure a comprehensive analysis based on structural MRI of CD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The ML framework has effectively identified the changes in brain regions in the stage of model\u2010level assessment, verified the effectiveness of these altered brain regions to predict CD from normal controls in the stage of feature\u2010level assessment, and carried out a correlation analysis between altered brain regions and clinical symptoms in the stage of biology\u2010level\u00a0assessment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The experimental results of this study have demonstrated that the Insula, Fusiform gyrus, Superior frontal gyrus, Precuneus, and the opercular portion of the Inferior frontal gyrus of CD showed significant alterations in brain regions. Furthermore, our study has revealed significant correlations between clinical symptoms and the frontotemporal lobes, insulin, and olfactory cortex, which also have been confirmed by previous\u00a0studies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The ML framework proposed in this study exhibits exceptional potential in uncovering the intricate pathophysiological mechanisms underlying CD, with potential applicability in diagnosing other\u00a0diseases.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17032","type":"journal-article","created":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T13:48:44Z","timestamp":1711979324000},"page":"5479-5491","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Identifying gray matter alterations in Cushing's disease using machine learning: An interpretable 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