Evaluation of a computer-based facial dysmorphology analysis algorithm (Face2Gene) using standardized textbook photos

Genetic syndromes often have ocular involvement. Ophthalmologists may have difficulty identifying dysmorphic features in genetic syndrome evaluations. We investigated the sensitivity and specificity of Face2Gene (F2G), a digital image analysis software trained on integrating dysmorphic features, by analysing patient photos from genetics textbooks.

We analysed all clear facial photos contained within the textbooks Smith's Recognizable Patterns of Human Malformation and Genetic Diseases of the Eye using F2G under standard lighting conditions. Variables captured include colour versus grey scale photo, the gender of the patient (if known), age of the patient (if known), disease categories, diagnosis as listed in the textbook, and whether the disease has ophthalmic involvement (as described in the textbook entries). Any photos rejected by F2G were excluded. We analysed the data for accuracy, sensitivity, and specificity based on disease categories as outlined in Smith's Recognizable Patterns of Malformation.

We analysed 353 photos found within two textbooks. The exact book diagnosis was identified by F2G in 150 (42.5%) entries, and was included in the top three differential diagnoses in 191 (54.1%) entries. F2G is highly sensitive for craniosynostosis syndromes (point estimate [PE] 80.0%, 95% confidence interval [CI] 56.3-94.3%, P = 0.0118) and syndromes with facial defects as a major feature (PE 77.8%, 95% CI 52.4-93.6%, P = 0.0309). F2G was highly specific (PE > 83percentage with P < 0.001) for all disease categories.

F2G is a useful tool for paediatric ophthalmologists to help build a differential diagnosis when evaluating children with dysmorphic facial features.