Advancements in the diagnosis and management of rotator cuff tears. The role of artificial intelligence

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dc.coverageDOI: 10.1016/j.jor.2023.11.011
dc.creatorVelasquez Garcia, Ausberto
dc.creatorHsu, Kai Lan
dc.creatorMarinakis, Katina
dc.date2024
dc.date.accessioned2025-11-18T19:45:58Z
dc.date.available2025-11-18T19:45:58Z
dc.description<p>Background: This review examined the role of artificial intelligence (AI) in the diagnosis and management of rotator cuff tears (RCTs). Methods: A literature search was conducted in October 2023 using PubMed (MEDLINE), SCOPUS, and EMBASE databases, included only peer-reviewed studies. Relevant articles on AI technology in RCTs. A critical analysis of the relevant literature was conducted. Results: AI is transforming RCTs management through faster and more precise identification and assessment using algorithms that facilitate segmentation, quantification, and classification of the RCTs across various imaging modalities. Precise algorithms focusing on preoperative factors to assess RCTs reparability have been developed for personalized treatment planning and outcome prediction. AI also aids in exercise classification and promotes patient adherence during at-home physiotherapy. Despite promising advancements, challenges in data quality and symptom integration persist. Future research should include refining AI algorithms, expanding their integration into various imaging techniques, and exploring their roles in postoperative care and surgical decision-making. Conclusions: AI-driven solutions improve diagnostic accuracy and have the potential to influence treatment planning and postoperative outcomes through the automated RCTs analysis of medical imaging. Integration of high-quality datasets and clinical symptoms into AI models can enhance their reliability. Current AI algorithms can also be refined, integrated into other imaging techniques, and explored further in surgical decision-making and postoperative care.</p>eng
dc.identifierhttps://investigadores.uandes.cl/en/publications/09e7ab3c-5f4d-4351-b0c7-d02e23045a52
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/54249
dc.languageeng
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourcevol.47 (2024) p.87-93
dc.subjectArtificial intelligence
dc.subjectDeep learning
dc.subjectDiagnostic imaging
dc.subjectMachine learning
dc.subjectPersonalized medicine
dc.subjectRotator cuff tears
dc.subjectSDG 3 - Good Health and Well-being
dc.titleAdvancements in the diagnosis and management of rotator cuff tears. The role of artificial intelligenceeng
dc.typeReview articleeng
dc.typeArtículo de revisiónspa
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