Evidence map›Paper›PMID 42778693›Full record

ArticlePediatric research2026

Early identification of Prader-Willi syndrome in newborns using facial phenotype-based machine learning.

Yunqian Zhu, Huihui Chen, Jieyi Chen, Lin Yang, Xinran Dong, Qi Ni, Wenhao Zhou

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Article in Pediatric research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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7 authors.

Yunqian Zhu *Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Huihui Chen *Key Laboratory of Birth Defects, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Jieyi ChenKey Laboratory of Birth Defects, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Lin YangDepartment of Pediatric Endocrinology and Inherited Metabolic Diseases, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Xinran DongKey Laboratory of Birth Defects, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Qi NiKey Laboratory of Birth Defects, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Wenhao ZhouKey Laboratory of Birth Defects, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China. zhouwenhao@fudan.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExisting facial models for Prader-Willi syndrome (PWS) demonstrate limited accuracy in newborns with subtle facial dysmorphism.

methodsPhotographs of children from a public platform (training cohort) and of newborns from Children's Hospital of Fudan University (validation cohort) were used for feature comparison. Models were developed using six algorithms with three feature sets (Complete-, Top-10, and ClinSel-) in the training cohort. Model performance, calibration, feature importance, and sensitivity analysis were assessed. External validation via threshold re-estimation was compared with Face2Gene.

resultsOf 113 children (21 [19%] PWS), RF and AdaBoost gained the highest Area Under the Precision-Recall Curve across all feature sets. RF Complete-set model achieved highest sensitivity (mean [95% CI], 0.97 [0.84-1.00]) with 0.95 specificity (95% CI, 0.83-1.00) and was well calibrated. Age, face width, and upper lip-related features were important features. Four previously unreported features (jaw symmetry, chin, nose, and jaw widths) were identified. Sensitivity analysis showed 26 facial features contribute more than age. In external validation (10 PWS newborns and 13 controls), RF Complete-set model achieved comparable sensitivity (0.80 vs. 0.70) and significantly higher specificity (0.77 vs. 0.15) at a false positive rate ≤0.25 compared with Face2Gene.

conclusionsRF Complete-set model demonstrates potential for neonatal PWS identification. IMPACT: Facial phenotype-based machine learning models for identifying Prader-Willi syndrome (PWS) in newborns facilitate early detection of PWS before genetic testing. Models achieved comparable sensitivity but higher specificity than Face2Gene, providing a favorable tool for PWS. Four previously unreported facial features-jaw symmetry, chin, nose, and jaw widths-expand the PWS facial phenotype. Models trained on children successfully identified PWS newborns after threshold adjustment, showing the value of reusing pediatric data when newborn samples are limited. Pilot validation in newborns with 0.80 sensitivity and 0.77 specificity supports the feasibility of machine learning-based screening for genetic syndromes.

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PMID42778693

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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.