Utility of Face2Gene’s DeepGestalt and D-Score applications in paediatric neurodevelopmental disorders in South Africa

Abstract

Abstract Face2Gene is a clinical tool that leverages facial features to aid genetic diagnosis. The DeepGestalt application suggests potential diagnoses based on facial similarity, while the D-Score evaluates likelihood of an individual having dysmorphic features suggestive of a possible genetic diagnosis. Given performance variability across populations and limited data from South Africa, this study assessed clinical utility in South African children with neurodevelopmental disorders (NDDs). Facial photographs from 301 children were analysed. The cohort comprised three groups: 36 children with NDDs with confirmed molecular diagnoses, 176 with NDDs without molecular diagnoses and 89 unaffected children. Diagnostic (recognition) accuracy was measured by whether the confirmed diagnosis appeared in the top-1 or top-10 ranked algorithm-generated suggestions (DeepGestalt). D-Scores were extracted to calculate group differences. Among children with confirmed molecular diagnoses, accuracy was 19% (95% CI: 9–35%) (top-1) and 34% (95% CI: 20–52%) (top-10), improving to 33% (95% CI: 16–56%) and 61% (95% CI: 39–80%) when limited to conditions included in the DeepGestalt training set. One-way ANOVA revealed differences between participants with and without significant dysmorphic features, as assessed by clinicians. The D-Score demonstrated moderate sensitivity (78%, [95% CI: 0.64, 0.88]) and low specificity (42%, [95% CI: 0.38, 0.50]), but high negative predictive value (91%, [95% CI: 0.84, 0.95]), suggesting it may be more useful for ruling out dysmorphism; however, the low specificity indicates a high rate of false positives, even among clinically non-dysmorphic children. These findings suggest that under-representation of African populations may limit clinical performance and equity of AI-based facial phenotyping tools.