Abstract
Shoulder pathologies, particularly soft tissue injuries, are a common problem requiring accurate and timely diagnosis. Currently, MRI is the gold standard for visualizing shoulder injuries. However, the interpretation of MRI often relies on the subjective assessment of the examiner and can vary depending on their experience. Texture analysis (TA), based on the quantitative evaluation of MRI images, offers a solution by providing objective data that complements and refines expert evaluation. This review examines current research on the application of TA in diagnosing shoulder pathologies. We analyze various TA methods and their application for evaluating different shoulder structures. We compare the performance of automated, machine learning-based approaches with expert interpretation of MRI images. The advantages and limitations of TA are also discussed. The integration of artificial intelligence, and specifically image texture analysis, into clinical practice represents a significant advancement in medical diagnostics. This approach promises substantial benefits by augmenting physicians' expertise with objective assessments derived from mathematical algorithms and machine learning. This integration is expected to not only enhance diagnostic accuracy but also contribute to the development of more precise treatment strategies and preventative measures for various pathologies.
For citation: Shabarova NS, Zhilyakov AA, Zhilyakov AV, Volokitina EA. Application of texture analysis in the diagnosis of shoulder pathologies: A literature review. USMU Medical Bulletin. 2025;10(1):e00133. (In Russ.). DOI: https://doi.org/10.52420/usmumb.10.1.e00133. EDN: https://elibrary.ru/YKCESE.
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Copyright © 2025 Shabarova N. S., Zhilyakov A. A., Zhilyakov A. V., Volokitina E. A.