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AI has greatly enhanced medical image analysis, yet its use inepidemiological population imaging studies remains limited due to visualizationchallenges in non-linear models and lack of confounder control. Addressingthis, we introduce an AI method emphasizing semantic feature interpretation andresilience against multiple confounders. Our approach's merits are tested inthree scenarios: extracting confounder-free features from a 2D syntheticdataset; examining the association between prenatal alcohol exposure andchildren's facial shapes using 3D mesh data; exploring the relationship betweenglobal cognition and brain images with a 3D MRI dataset. Results confirm ourmethod effectively reduces confounder influences, establishing less confoundedassociations. Additionally, it provides a unique visual representation,highlighting specific image alterations due to identified correlations.

AI-based association analysis for medical imaging using latent-space geometric confounder correction