Genetic syndromes screening by facial recognition technology: VGG-16 screening model construction and evaluation

• Published in: Orphanet journal of rare diseases 2021-08, Vol.16 (1), p.1-344, Article 344
• Main Authors: Hong, Dian, Zheng, Ying-Yi, Xin, Ying, Sun, Ling, Yang, Hang, Lin, Min-Yin, Liu, Cong, Li, Bo-Ning, Zhang, Zhi-Wei, Zhuang, Jian, Qian, Ming-Yang, Wang, Shu-Shui
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Children; Chromosomes; Classification; Clinical medicine; Computer-aided medical diagnosis; Deep learning; Diagnosis; Facial recognition; Facial recognition technology; Family physicians; Feasibility studies; Genetic disorders; Genetic syndrome; Medical research; Methods; Neural networks; Pattern recognition; Physiological aspects; Rare diseases; Transfer learning
• ISSN: 1750-1172; 1750-1172
• DOI: 10.1186/s13023-021-01979-y

Many genetic syndromes (GSs) have distinct facial dysmorphism, and facial gestalts can be used as a diagnostic tool for recognizing a syndrome. Facial recognition technology has advanced in recent years, and the screening of GSs by facial recognition technology has become feasible. This study constructed an automatic facial recognition model for the identification of children with GSs. A total of 456 frontal facial photos were collected from 228 children with GSs and 228 healthy children in Guangdong Provincial People's Hospital from Jun 2016 to Jan 2021. Only one frontal facial image was selected for each participant. The VGG-16 network (named after its proposal lab, Visual Geometry Group from Oxford University) was pretrained by transfer learning methods, and a facial recognition model based on the VGG-16 architecture was constructed. The performance of the VGG-16 model was evaluated by five-fold cross-validation. Comparison of VGG-16 model to five physicians were also performed. The VGG-16 model achieved the highest accuracy of 0.8860 [+ or -] 0.0211, specificity of 0.9124 [+ or -] 0.0308, recall of 0.8597 [+ or -] 0.0190, F1-score of 0.8829 [+ or -] 0.0215 and an area under the receiver operating characteristic curve of 0.9443 [+ or -] 0.0276 (95% confidence interval: 0.9210-0.9620) for GS screening, which was significantly higher than that achieved by human experts. This study highlighted the feasibility of facial recognition technology for GSs identification. The VGG-16 recognition model can play a prominent role in GSs screening in clinical practice.