Machine learning–based prediction of rotator cuff tears using anatomical parameters: a retrospective cohort study

Abstract Background To apply machine learning to improve risk assessment for rotator cuff tears by developing an integrated predictive model that combines acromial morphology (structural factors) and patient characteristics (systemic factors). Methods A total of 342 patients who underwent shoulder r...

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Detalhes bibliográficos

Principais autores:	Zhipeng Hou, Quqiao Wan, Ligui Wan
Formato:	Artigo
Idioma:	Inglês
Publicado em:	BMC 2026-03-01
coleção:	BMC Musculoskeletal Disorders
Assuntos:	Rotator cuff tear Machine learning Novel acromial angle Acromial morphology Predictive model
Acesso em linha:	https://doi.org/10.1186/s12891-026-09765-2
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