Update on the management of idiopathic scoliosis.

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Abstract

[Indexed for MEDLINE] 19. Spine Deform. 2024 Nov;12(6):1545-1570. doi: 10.1007/s43390-024-00940-w. Epub 2024 Aug 17. Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence. Goldman SN(#)(1), Hui AT(#)(2), Choi S(1), Mbamalu EK(1), Tirabady P(1), Eleswarapu AS(3), Gomez JA(3), Alvandi LM(3), Fornari ED(3). Author information: (1)Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA. (2)Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA. Aaron.Hui@einsteinmed.edu. (3)Department of Orthopaedics, Montefiore Medical Center, Bronx, NY, 10461, USA. (#)Contributed equally PURPOSE: Adolescent idiopathic scoliosis (AIS) is a common spinal deformity with varying progression, complicating treatment decisions. Artificial intelligence (AI) and machine learning (ML) are increasingly prominent in orthopedic care, aiding in diagnosis, risk-stratification, and treatment guidance. This scoping review outlines AI applications in AIS. METHODS: This study followed PRISMA-ScR guidelines and included articles that reported the development, use, or validation of AI models for treating, diagnosing, or predicting clinical outcomes in AIS. RESULTS: 40 full-text articles were included, with most studies published in the last 5 years (77.5%). Common ML techniques were convolutional neural networks (55%), decision trees and random forests (15%), and artificial neural networks (15%). Most AI applications in AIS were for imaging analysis (25/40; 62.5%), focusing on automatic measurement of Cobb angle, and axial vertebral rotation (13/25; 52%) and curve classification/severity (13/25; 52%). Prediction was the second most common application (15/40; 37.5%), with studies predicting curve progression (9/15; 60%), and Cobb angles (9/15; 60%). Only 15 studies (37.5%) reported clinical implementation guidelines for AI in AIS management. 52.5% of studies reported model accuracy, with an average of 85.4%. CONCLUSION: This review highlights the applications of AI in AIS care, notably including automatic radiographic analysis, curve type classification, prediction of curve progression, and AIS diagnosis. However, the current lack of clear clinical implementation guidelines, model transparency, and external validation of studied models limits clinician trust and the generalizability and applicability of AI in AIS management. © 2024. The Author(s). DOI: 10.1007/s43390-024-00940-w PMCID: PMC11499369