ArticleIEEE journal of biomedical and health informatics2022
A Deep Invertible 3-D Facial Shape Model for Interpretable Genetic Syndrome Diagnosis.
Article in IEEE journal of biomedical and health informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- BabyFlow: 3D modeling of realistic and expressive infant faces.Scientific reports · 2026Article
- Objective pulse wave amplitude as a potential systemic correlate in childhood myopia: a digital TCM-based study.Frontiers in medicine · 2026Article
- Using a Disentangled Neural Network to Objectively Assess the Outcomes of Midfacial Surgery in Syndromic Craniosynostosis.Plastic and reconstructive surgery · 2025Article
- An explainable dataset linking facial phenotypes and genes to rare genetic diseases.Scientific data · 2025Article
- A 3D Clinical Face Phenotype Space of Genetic Syndromes Using a Triplet-Based Singular Geometric Autoencoder.IEEE access : practical innovations, open solutions · 2025Article
- An interactive atlas of three-dimensional syndromic facial morphology.American journal of human genetics · 2024Article
- Privacy, bias and the clinical use of facial recognition technology: A survey of genetics professionals.American journal of medical genetics. Part C, Seminars in medical genetics · 2023Article
- Comparing 2D and 3D representations for face-based genetic syndrome diagnosis.European journal of human genetics : EJHG · 2023Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
One of the primary difficulties in treating patients with genetic syndromes is diagnosing their condition. Many syndromes are associated with characteristic facial features that can be imaged and utilized by computer-assisted diagnosis systems. In this work, we develop a novel 3D facial surface modeling approach with the objective of maximizing diagnostic model interpretability within a flexible deep learning framework. Therefore, an invertible normalizing flow architecture is introduced to enable both inferential and generative tasks in a unified and efficient manner. The proposed model can be used (1) to infer syndrome diagnosis and other demographic variables given a 3D facial surface scan and (2) to explain model inferences to non-technical users via multiple interpretability mechanisms. The model was trained and evaluated on more than 4700 facial surface scans from subjects with 47 different syndromes. For the challenging task of predicting syndrome diagnosis given a new 3D facial surface scan, age, and sex of a subject, the model achieves a competitive overall top-1 accuracy of 71%, and a mean sensitivity of 43% across all syndrome classes. We believe that invertible models such as the one presented in this work can achieve competitive inferential performance while greatly increasing model interpretability in the domain of medical diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.