AI-based association analysis for medical imaging using latent-space
  geometric confounder correction
Xianjing Liu Bo Li Meike W. Vernooij Eppo B. Wolvius Gennady V. Roshchupkin Esther E. Bron
AI has greatly enhanced medical image analysis, yet its use in
epidemiological population imaging studies remains limited due to visualization
challenges in non-linear models and lack of confounder control. Addressing
this, we introduce an AI method emphasizing semantic feature interpretation and
resilience against multiple confounders. Our approach's merits are tested in
three scenarios: extracting confounder-free features from a 2D synthetic
dataset; examining the association between prenatal alcohol exposure and
children's facial shapes using 3D mesh data; exploring the relationship between
global cognition and brain images with a 3D MRI dataset. Results confirm our
method effectively reduces confounder influences, establishing less confounded
associations. Additionally, it provides a unique visual representation,
highlighting specific image alterations due to identified correlations.