# ProtoGestalt

Rare genetic disorders are often missed at first presentation, and most facial diagnostic models are end-to-end black boxes that cannot say what drove a call. ProtoGestalt adds facial-landmark constraints to PIP-Net prototype learning, so each prototype draws evidence only from one of ten anatomical regions and predictions are sums of those local prototypes. On controls matched by age, sex and ethnicity it separated syndromic from unaffected faces at 0.85 accuracy, versus 0.73 for the region-masked variant; AUPRC fell as controls increased but stayed above 0.70.

Rare genetic disorders are often missed at first presentation, and most facial diagnostic models are end-to-end black boxes that cannot say what drove a call. ProtoGestalt adds facial-landmark constraints to PIP-Net prototype learning, so each prototype draws evidence only from one of ten anatomical regions and predictions are sums of those local prototypes. On controls matched by age, sex and ethnicity it separated syndromic from unaffected faces at 0.85 accuracy, versus 0.73 for the region-masked variant; AUPRC fell as controls increased but stayed above 0.70.