ArticleOrphanet journal of rare diseases2021
Genetic syndromes screening by facial recognition technology: VGG-16 screening model construction and evaluation.
Article in Orphanet journal of rare diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review.Journal of medical Internet research · 2026Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- Automated Multi-Class Facial Syndrome Classification Using Transfer Learning Techniques.Bioengineering (Basel, Switzerland) · 2024Article
- [Research on the classification model of chronic sinusitis based on VGG].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2024Article
- Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition.Cancers · 2024Review
- Computer-aided diagnostic screen for Congenital Central Hypoventilation Syndrome with facial phenotype.Pediatric research · 2024Article
- Genetic Screening-Emerging Issues.Genes · 2024Review
- Enhancing Variant Prioritization in VarFish through On-Premise Computational Facial Analysis.Genes · 2024Article
- Article
- Facial features of lysosomal storage disorders.Expert review of endocrinology & metabolism · 2022Article
- Review on Facial-Recognition-Based Applications in Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2022Review
- A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer.Frontiers in oncology · 2022Article
- Facial artificial intelligence in ophthalmology and medicine: fundamental and transformative applications.Therapeutic advances in ophthalmologyReview
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12 authors.
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Abstract
backgroundMany 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.
resultsA 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 ± 0.0211, specificity of 0.9124 ± 0.0308, recall of 0.8597 ± 0.0190, F1-score of 0.8829 ± 0.0215 and an area under the receiver operating characteristic curve of 0.9443 ± 0.0276 (95% confidence interval: 0.9210-0.9620) for GS screening, which was significantly higher than that achieved by human experts.
conclusionsThis 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.
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