Artificial intelligence medical image-aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgery
The existing assessment of adjacent segment degeneration (ASD) risk after lumbar fusion surgery focuses on a single type of clinical information or imaging manifestations. In the early stages, it is difficult to show obvious degeneration characteristics, and the patients’ true risks cannot be fully...
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Elsevier
2025-06-01
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| Series: | SLAS Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S247263032500041X |
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| author | Bin Dai Xinyu Liang Yan Dai Xintian Ding |
| author_facet | Bin Dai Xinyu Liang Yan Dai Xintian Ding |
| author_sort | Bin Dai |
| collection | DOAJ |
| description | The existing assessment of adjacent segment degeneration (ASD) risk after lumbar fusion surgery focuses on a single type of clinical information or imaging manifestations. In the early stages, it is difficult to show obvious degeneration characteristics, and the patients’ true risks cannot be fully revealed. The evaluation results based on imaging ignore the clinical symptoms and changes in quality of life of patients, limiting the understanding of the natural process of ASD and the comprehensive assessment of its risk factors, and hindering the development of effective prevention strategies. To improve the quality of postoperative management and effectively identify the characteristics of ASD, this paper studies the risk assessment of ASD after lumbar fusion surgery by combining the artificial intelligence (AI) medical image-aided diagnosis system. First, the collaborative attention mechanism is adopted to start with the extraction of single-modal features and fuse the multi-modal features of computed tomography (CT) and magnetic resonance imaging (MRI) images. Then, the similarity matrix is weighted to achieve the complementarity of multi-modal information, and the stability of feature extraction is improved through the residual network structure. Finally, the fully connected network (FCN) is combined with the multi-task learning framework to provide a more comprehensive assessment of the risk of ASD. The experimental analysis results show that compared with three advanced models, three dimensional-convolutional neural networks (3D-CNN), U-Net++, and deep residual networks (DRN), the accuracy of the model in this paper is 3.82 %, 6.17 %, and 6.68 % higher respectively; the precision is 0.56 %, 1.09 %, and 4.01 % higher respectively; the recall is 3.41 %, 4.85 %, and 5.79 % higher respectively. The conclusion shows that the AI medical image-aided diagnosis system can help to accurately identify the characteristics of ASD and effectively assess the risks after lumbar fusion surgery. |
| format | Article |
| id | doaj-art-3878b1d7d54f4a6fae78a6e623f5d3f9 |
| institution | OA Journals |
| issn | 2472-6303 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SLAS Technology |
| spelling | doaj-art-3878b1d7d54f4a6fae78a6e623f5d3f92025-08-20T02:26:52ZengElsevierSLAS Technology2472-63032025-06-013210028310.1016/j.slast.2025.100283Artificial intelligence medical image-aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgeryBin Dai0Xinyu Liang1Yan Dai2Xintian Ding3Clinical College of Medicine, Wannan Medical College, Wuhu 241000, Anhui, PR ChinaAnhui Medical University, Hefei 230000, Anhui, PR ChinaChangzhou Tianning District Center for Disease Control and Prevention, Changzhou 213017, Jiangsu, PR ChinaAnhui Medical University, Hefei 230000, Anhui, PR China; Corresponding author.The existing assessment of adjacent segment degeneration (ASD) risk after lumbar fusion surgery focuses on a single type of clinical information or imaging manifestations. In the early stages, it is difficult to show obvious degeneration characteristics, and the patients’ true risks cannot be fully revealed. The evaluation results based on imaging ignore the clinical symptoms and changes in quality of life of patients, limiting the understanding of the natural process of ASD and the comprehensive assessment of its risk factors, and hindering the development of effective prevention strategies. To improve the quality of postoperative management and effectively identify the characteristics of ASD, this paper studies the risk assessment of ASD after lumbar fusion surgery by combining the artificial intelligence (AI) medical image-aided diagnosis system. First, the collaborative attention mechanism is adopted to start with the extraction of single-modal features and fuse the multi-modal features of computed tomography (CT) and magnetic resonance imaging (MRI) images. Then, the similarity matrix is weighted to achieve the complementarity of multi-modal information, and the stability of feature extraction is improved through the residual network structure. Finally, the fully connected network (FCN) is combined with the multi-task learning framework to provide a more comprehensive assessment of the risk of ASD. The experimental analysis results show that compared with three advanced models, three dimensional-convolutional neural networks (3D-CNN), U-Net++, and deep residual networks (DRN), the accuracy of the model in this paper is 3.82 %, 6.17 %, and 6.68 % higher respectively; the precision is 0.56 %, 1.09 %, and 4.01 % higher respectively; the recall is 3.41 %, 4.85 %, and 5.79 % higher respectively. The conclusion shows that the AI medical image-aided diagnosis system can help to accurately identify the characteristics of ASD and effectively assess the risks after lumbar fusion surgery.http://www.sciencedirect.com/science/article/pii/S247263032500041XAdjacent segment degenerationRisk assessmentArtificial intelligenceMedical Image-aided Diagnosis SystemLumbar Fusion |
| spellingShingle | Bin Dai Xinyu Liang Yan Dai Xintian Ding Artificial intelligence medical image-aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgery SLAS Technology Adjacent segment degeneration Risk assessment Artificial intelligence Medical Image-aided Diagnosis System Lumbar Fusion |
| title | Artificial intelligence medical image-aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgery |
| title_full | Artificial intelligence medical image-aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgery |
| title_fullStr | Artificial intelligence medical image-aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgery |
| title_full_unstemmed | Artificial intelligence medical image-aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgery |
| title_short | Artificial intelligence medical image-aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgery |
| title_sort | artificial intelligence medical image aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgery |
| topic | Adjacent segment degeneration Risk assessment Artificial intelligence Medical Image-aided Diagnosis System Lumbar Fusion |
| url | http://www.sciencedirect.com/science/article/pii/S247263032500041X |
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