ArticlePediatrics2017
Computer-Aided Recognition of Facial Attributes for Fetal Alcohol Spectrum Disorders.
Article in Pediatrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 61 citations in OpenAlex.
- Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review.Alcohol, clinical & experimental research · 2024Review
- Multifaceted case management during pregnancy is associated with better child outcomes and less fetal alcohol syndrome.Annals of medicine · 2023Article
- A Deep-Learning-Based Method Can Detect Both Common and Rare Genetic Disorders in Fetal Ultrasound.Biomedicines · 2023Article
- Screening Children's Intellectual Disabilities with Phonetic Features, Facial Phenotype and Craniofacial Variability Index.Brain sciences · 2023Article
- A systematic review: facial, dental and orthodontic findings and orofacial diagnostics in patients with FASD.Frontiers in pediatrics · 2023Review
- Identifying Prenatal Alcohol Exposure and Children Affected by It: A Review of Biomarkers and Screening Tools.Alcohol research : current reviews · 2023Review
- The prevalence of fetal alcohol spectrum disorders in rural communities in South Africa: A third regional sample of child characteristics and maternal risk factors.Alcoholism, clinical and experimental research · 2022Article
- Review on Facial-Recognition-Based Applications in Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2022Review
- Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis.Scientific reports · 2022Article
- Correlational research on facial and clinical characteristics of adolescents with obsessive-compulsive disorder.BMC psychiatry · 2021Article
- Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition.Journal of personalized medicine · 2021Article
- Pain Management in Children: NSAID Use in the Perioperative and Emergency Department Settings.Paediatric drugs · 2021Review
- Simplifying Diagnosis of Fetal Alcohol Syndrome Using Machine Learning Methods.Frontiers in pediatrics · 2021Article
- Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study.Journal of medical Internet research · 2020Article
- Ocular measurements in fetal alcohol spectrum disorders.American journal of medical genetics. Part A · 2020Article
- Differentiating molecular etiologies of Angelman syndrome through facial phenotyping using deep learning.American journal of medical genetics. Part A · 2020Article
- Asymmetry-index and orthodontic facial analysis of children with foetal alcohol syndrome using 3D-facial scans.Pediatric research · 2020Observational
- Assessing the Big Five personality traits using real-life static facial images.Scientific reports · 2020Article
- Fetal Alcohol Spectrum Disorder: Can We Change the Future?Alcoholism, clinical and experimental research · 2020Article
- Differentiation of MISSLA and Fanconi anaemia by computer-aided image analysis and presentation of two novel MISSLA siblings.European journal of human genetics : EJHG · 2019Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
objectivesTo compare the detection of facial attributes by computer-based facial recognition software of 2-D images against standard, manual examination in fetal alcohol spectrum disorders (FASD).
methodsParticipants were gathered from the Fetal Alcohol Syndrome Epidemiology Research database. Standard frontal and oblique photographs of children were obtained during a manual, in-person dysmorphology assessment. Images were submitted for facial analysis conducted by the facial dysmorphology novel analysis technology (an automated system), which assesses ratios of measurements between various facial landmarks to determine the presence of dysmorphic features. Manual blinded dysmorphology assessments were compared with those obtained via the computer-aided system.
resultsAreas under the curve values for individual receiver-operating characteristic curves revealed the computer-aided system (0.88 ± 0.02) to be comparable to the manual method (0.86 ± 0.03) in detecting patients with FASD. Interestingly, cases of alcohol-related neurodevelopmental disorder (ARND) were identified more efficiently by the computer-aided system (0.84 ± 0.07) in comparison to the manual method (0.74 ± 0.04). A facial gestalt analysis of patients with ARND also identified more generalized facial findings compared to the cardinal facial features seen in more severe forms of FASD.
conclusionsWe found there was an increased diagnostic accuracy for ARND via our computer-aided method. As this category has been historically difficult to diagnose, we believe our experiment demonstrates that facial dysmorphology novel analysis technology can potentially improve ARND diagnosis by introducing a standardized metric for recognizing FASD-associated facial anomalies. Earlier recognition of these patients will lead to earlier intervention with improved patient outcomes.
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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.