Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study
| Autor: | Antonio R. Porras, Carlos Tor-Díez, Marshall L. Summar, Kenneth N. Rosenbaum, Marius George Linguraru |
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| Rok vydání: | 2021 |
| Předmět: |
Male
Down syndrome Asia Internationality Cornelia de Lange Syndrome Genetic syndromes Point-of-Care Systems Ethnic group MEDLINE Medicine (miscellaneous) Health Informatics Machine learning computer.software_genre Risk Assessment Sensitivity and Specificity White People Machine Learning Health Information Management Photography medicine Humans Decision Sciences (miscellaneous) Retrospective Studies Point of care business.industry Genetic Diseases Inborn Infant Reproducibility of Results Retrospective cohort study Hispanic or Latino medicine.disease Facial Expression Phenotype Face Africa Noonan syndrome Female Artificial intelligence business computer |
| Zdroj: | The Lancet Digital Health. 3:e635-e643 |
| ISSN: | 2589-7500 |
| DOI: | 10.1016/s2589-7500(21)00137-0 |
| Popis: | BACKGROUND Delays in the diagnosis of genetic syndromes are common, particularly in low and middle-income countries with limited access to genetic screening services. We, therefore, aimed to develop and evaluate a machine learning-based screening technology using facial photographs to evaluate a child's risk of presenting with a genetic syndrome for use at the point of care. METHODS In this retrospective study, we developed a facial deep phenotyping technology based on deep neural networks and facial statistical shape models to screen children for genetic syndromes. We trained the machine learning models on facial photographs from children (aged |
| Databáze: | OpenAIRE |
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