The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning
Abstract 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 Alex...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-80441-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846158551489183744 |
|---|---|
| author | Lina Dai Md Gapar Md Johar Mohammed Hazim Alkawaz |
| author_facet | Lina Dai Md Gapar Md Johar Mohammed Hazim Alkawaz |
| author_sort | Lina Dai |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-c43a81869e004d1faa97b0e14f99ef5a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c43a81869e004d1faa97b0e14f99ef5a2024-11-24T12:27:12ZengNature PortfolioScientific Reports2045-23222024-11-0114111410.1038/s41598-024-80441-yThe diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learningLina Dai0Md Gapar Md Johar1Mohammed Hazim Alkawaz2School of Information Technology and Engineering, Guangzhou College of CommerceSoftware Engineering and Digital Innovation Center, Management and Science UniversityDepartment of Computer Science, College of Education for Pure Science, University of MosulAbstract 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.https://doi.org/10.1038/s41598-024-80441-yDeep learningMSFFN algorithmMRI image segmentationSwimmersShoulder joint injuriesDiagnosis |
| spellingShingle | Lina Dai Md Gapar Md Johar Mohammed Hazim Alkawaz The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning Scientific Reports Deep learning MSFFN algorithm MRI image segmentation Swimmers Shoulder joint injuries Diagnosis |
| title | The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning |
| title_full | The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning |
| title_fullStr | The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning |
| title_full_unstemmed | The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning |
| title_short | The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning |
| title_sort | diagnostic value of mri segmentation technique for shoulder joint injuries based on deep learning |
| topic | Deep learning MSFFN algorithm MRI image segmentation Swimmers Shoulder joint injuries Diagnosis |
| url | https://doi.org/10.1038/s41598-024-80441-y |
| work_keys_str_mv | AT linadai thediagnosticvalueofmrisegmentationtechniqueforshoulderjointinjuriesbasedondeeplearning AT mdgaparmdjohar thediagnosticvalueofmrisegmentationtechniqueforshoulderjointinjuriesbasedondeeplearning AT mohammedhazimalkawaz thediagnosticvalueofmrisegmentationtechniqueforshoulderjointinjuriesbasedondeeplearning AT linadai diagnosticvalueofmrisegmentationtechniqueforshoulderjointinjuriesbasedondeeplearning AT mdgaparmdjohar diagnosticvalueofmrisegmentationtechniqueforshoulderjointinjuriesbasedondeeplearning AT mohammedhazimalkawaz diagnosticvalueofmrisegmentationtechniqueforshoulderjointinjuriesbasedondeeplearning |