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Войти в исследования Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
The Use of Artificial Intelligence in Idiopathic Clubfoot: A Scoping Review.
Scoping ReviewСтатьяНазвание и аннотация: Перевод пока не подготовлен
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El Hachem Rony Imad, Rizk Jean-Paul, Nahle Imad
Аннотация
BACKGROUND: The past decade has been marked by the progressive integration of artificial intelligence (AI), especially in the form of machine learning and deep learning. Pediatric orthopaedics remains an underexplored area in AI, especially in the context of congenital deformities such as clubfoot.
OBJECTIVE: A scoping review would help organize the various uses of AI in this domain by identifying studies and categorizing them based on the technique used and their applications, exposing gaps, and informing future studies to support AI integration in pediatric care.
METHODS: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-based analysis was conducted, and several databases were used to scope for articles (MEDLINE, PubMed, Scopus, Google Scholar) using a set of keywords and mapped-out terms.
RESULTS: A total of 16 studies met all eligibility criteria and were included in this review. These studies covered the use of AI, machine learning, or computational modeling over a broad range of objectives such as diagnosis, treatment, gait analysis, and prognostic modeling.
CONCLUSION: AI is moving from promise to practice in clubfoot diagnosis, decision making, and even relapse detection. Indeed, across 16 studies, AI applied as different forms, such as deep learning classifiers and finite element models, shows potential to offer solutions to long-standing clinical challenges such as subjective assessments and variability in treatment.
OBJECTIVE: A scoping review would help organize the various uses of AI in this domain by identifying studies and categorizing them based on the technique used and their applications, exposing gaps, and informing future studies to support AI integration in pediatric care.
METHODS: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-based analysis was conducted, and several databases were used to scope for articles (MEDLINE, PubMed, Scopus, Google Scholar) using a set of keywords and mapped-out terms.
RESULTS: A total of 16 studies met all eligibility criteria and were included in this review. These studies covered the use of AI, machine learning, or computational modeling over a broad range of objectives such as diagnosis, treatment, gait analysis, and prognostic modeling.
CONCLUSION: AI is moving from promise to practice in clubfoot diagnosis, decision making, and even relapse detection. Indeed, across 16 studies, AI applied as different forms, such as deep learning classifiers and finite element models, shows potential to offer solutions to long-standing clinical challenges such as subjective assessments and variability in treatment.