University of Augsburg

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.

The authors conducted a comprehensive neutral simulation comparison based on two reference datasets from randomized controlled trials, evaluating the Cox proportional hazards model and random survival forest for patient-specific survival probability prediction across multiple performance measures following TRIPOD recommendations, and found that conclusions based solely on the C index may not generalize to other aspects of predictive performance, that measures of overall performance may generally give more reasonable results, that the standard log-rank splitting rule for the random survival forest may be outperformed by alternative splitting rules particularly in nonproportional hazards settings, that the random survival forest performance suffered less in data with treatment-covariate inte

The authors conducted a comprehensive neutral simulation comparison based on two reference datasets from randomized controlled trials, evaluating the Cox proportional hazards model and random survival forest for patient-specific survival probability prediction across multiple performance measures following TRIPOD recommendations, and found that conclusions based solely on the C index may not generalize to other aspects of predictive performance, that measures of overall performance may generally give more reasonable results, that the standard log-rank splitting rule for the random survival forest may be outperformed by alternative splitting rules particularly in nonproportional hazards settings, that the random survival forest performance suffered less in data with treatment-covariate inte