Quantitative Facial Phenotyping and Age-Associated Differences in Sickle Cell Disease Using Interpretable Machine Learning

Background Sickle cell disease (SCD) is a common inherited genetic disorder and contributor to global childhood mortality and morbidity, particularly in Sub-Saharan Africa. Craniofacial and skeletal changes have been described in SCD, but the subtle soft-tissue facial patterns and their variation have not been quantitatively characterized using machine-learning methods.

Methods

We analyzed 308 front-facing smartphone photographs from 154 participants with electrophoresis-confirmed SCD and 154 age- and sex-matched controls aged from 5 months to 19 years recruited in Kinshasa, Democratic Republic of the Congo. Predefined geometric and texture-based facial descriptors were extracted from standardized images using manually annotated landmarks. An interpretable linear support vector machine with recursive feature elimination was evaluated using leave-one-out cross-validation, with features ranked according to their relevance for distinguishing participants with SCD from controls. Cross-sectional age- and sex- stratified analyses evaluated variation in selected features Results: The facial analysis algorithm identified eight geometric and six texture discriminative features that were significantly different between the cohorts. A combination of six geometric features contributed most consistently to internal discrimination: distance between medial and lateral canthi, angle at nasal ala, distance from nasion to philtrum, distance from medial canthi to the columella, distance from columella to the lower lip, and distance between nasal alae. The model achieved an AUROC of 0.843 [95% CI: 0.798,0.887], and an accuracy of 0.795 [95% CI: 0.750,0.840]. Between-group difference was limited in the youngest age groups and were more pronounced in several older age groups.