The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning.

Autor: Dai L; School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou, China. dailina27@163.com.; School of Graduate Studies, Management and Science University, Shah Alam, 40100, Selangor, Malaysia. dailina27@163.com., Md Johar MG; Software Engineering and Digital Innovation Center, Management and Science University, Shah Alam, 40100, Selangor, Malaysia., Alkawaz MH; Department of Computer Science, College of Education for Pure Science, University of Mosul, Mosul, Nineveh, Iraq.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Nov 21; Vol. 14 (1), pp. 28885. Date of Electronic Publication: 2024 Nov 21.
DOI: 10.1038/s41598-024-80441-y
Abstrakt: This work is to investigate the diagnostic value of a deep learning-based magnetic resonance imaging (MRI) image segmentation (IS) technique for shoulder joint injuries (SJIs) in swimmers. A novel multi-scale feature fusion network (MSFFN) is developed by optimizing and integrating the AlexNet and U-Net algorithms for the segmentation of MRI images of the shoulder joint. The model is evaluated using metrics such as the Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity (SE). A cohort of 52 swimmers with SJIs from Guangzhou Hospital serve as the subjects for this study, wherein the accuracy of the developed shoulder joint MRI IS model in diagnosing swimmers' SJIs is analyzed. The results reveal that the DSC for segmenting joint bones in MRI images based on the MSFFN algorithm is 92.65%, with PPV of 95.83% and SE of 96.30%. Similarly, the DSC for segmenting humerus bones in MRI images is 92.93%, with PPV of 95.56% and SE of 92.78%. The MRI IS algorithm exhibits an accuracy of 86.54% in diagnosing types of SJIs in swimmers, surpassing the conventional diagnostic accuracy of 71.15%. The consistency between the diagnostic results of complete tear, superior surface tear, inferior surface tear, and intratendinous tear of SJIs in swimmers and arthroscopic diagnostic results yield a Kappa value of 0.785 and an accuracy of 87.89%. These findings underscore the significant diagnostic value and potential of the MRI IS technique based on the MSFFN algorithm in diagnosing SJIs in swimmers.
Competing Interests: Competing interests: The authors declare no competing interests. Ethics statement: The studies involving human participants were reviewed and approved by School of Information Technology and Engineering, Guangzhou College of Commerce Ethics Committee (Approval Number: 2022.29384384). The participants provided their written informed consent to participate in this study. All methods were performed in accordance with relevant guidelines and regulations.
(© 2024. The Author(s).)
Databáze: MEDLINE
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