Artificial intelligence supported facial feature analysis in medical genetics

Karen W. Gripp

doi:10.1097/MOP.0000000000001487

Artificial intelligence supported facial feature analysis in medical genetics

Karen W. Gripp

Research output: Contribution to journal › Article › peer-review

Abstract

PURPOSE OF REVIEW: This review highlights recent developments in the use of machine learning supported facial feature analysis in the context of genetic and syndromic conditions. The review focusses on 2D image-based tools. The 2D images are easily obtained using handheld devices such as mobile phones and are more relevant to general practice than 3D image based systems. RECENT FINDINGS: Different algorithms are used widely in pediatric clinics, medical genetics clinics, for gene variant analysis and in research. Integrated systems combining next-generation phenotyping with next-generation genotyping support a shortened diagnostic odyssey for patients. SUMMARY: Future integration of phenotyping using data available in electronic medical records with genotyping data will likely result in earlier identification of possible genetic conditions.

Original language	English
Pages (from-to)	533-537
Number of pages	5
Journal	Current Opinion in Pediatrics
Volume	37
Issue number	6
DOIs	https://doi.org/10.1097/MOP.0000000000001487
State	Published - 1 Dec 2025

Keywords

artificial intelligence tools
machine learning supported facial feature analysis
next-generation phenotyping
Face/diagnostic imaging
Humans
Artificial Intelligence
Machine Learning
Phenotype
Algorithms
Genetic Testing/methods
Child
Electronic Health Records
Genetics, Medical/methods

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10.1097/MOP.0000000000001487

Cite this

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title = "Artificial intelligence supported facial feature analysis in medical genetics",

abstract = "PURPOSE OF REVIEW: This review highlights recent developments in the use of machine learning supported facial feature analysis in the context of genetic and syndromic conditions. The review focusses on 2D image-based tools. The 2D images are easily obtained using handheld devices such as mobile phones and are more relevant to general practice than 3D image based systems. RECENT FINDINGS: Different algorithms are used widely in pediatric clinics, medical genetics clinics, for gene variant analysis and in research. Integrated systems combining next-generation phenotyping with next-generation genotyping support a shortened diagnostic odyssey for patients. SUMMARY: Future integration of phenotyping using data available in electronic medical records with genotyping data will likely result in earlier identification of possible genetic conditions.",

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doi = "10.1097/MOP.0000000000001487",

language = "English",

volume = "37",

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AB - PURPOSE OF REVIEW: This review highlights recent developments in the use of machine learning supported facial feature analysis in the context of genetic and syndromic conditions. The review focusses on 2D image-based tools. The 2D images are easily obtained using handheld devices such as mobile phones and are more relevant to general practice than 3D image based systems. RECENT FINDINGS: Different algorithms are used widely in pediatric clinics, medical genetics clinics, for gene variant analysis and in research. Integrated systems combining next-generation phenotyping with next-generation genotyping support a shortened diagnostic odyssey for patients. SUMMARY: Future integration of phenotyping using data available in electronic medical records with genotyping data will likely result in earlier identification of possible genetic conditions.

KW - artificial intelligence tools

KW - machine learning supported facial feature analysis

KW - next-generation phenotyping

KW - Face/diagnostic imaging

KW - Humans

KW - Artificial Intelligence

KW - Machine Learning

KW - Phenotype

KW - Algorithms

KW - Genetic Testing/methods

KW - Child

KW - Electronic Health Records

KW - Genetics, Medical/methods

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JO - Current Opinion in Pediatrics

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Artificial intelligence supported facial feature analysis in medical genetics

Abstract

Keywords

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