Dai Bin, Liang Xinyu, Dai Yan, Ding Xintian
Clinical College of Medicine, Wannan Medical College, Wuhu 241000, Anhui, PR China.
Anhui Medical University, Hefei 230000, Anhui, PR China.
SLAS Technol. 2025 Jun;32:100283. doi: 10.1016/j.slast.2025.100283. Epub 2025 Apr 10.
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.
目前对腰椎融合术后相邻节段退变（ASD）风险的评估主要集中在单一类型的临床信息或影像表现上。在早期阶段，很难呈现出明显的退变特征，无法充分揭示患者的真实风险。基于影像的评估结果忽略了患者的临床症状和生活质量变化，限制了对ASD自然进程的理解及其危险因素的综合评估，阻碍了有效预防策略的发展。为提高术后管理质量并有效识别ASD的特征，本文通过结合人工智能（AI）医学图像辅助诊断系统研究腰椎融合术后ASD的风险评估。首先，采用协同注意力机制从单模态特征提取入手，融合计算机断层扫描（CT）和磁共振成像（MRI）图像的多模态特征。然后，对相似性矩阵进行加权以实现多模态信息的互补性，并通过残差网络结构提高特征提取的稳定性。最后，将全连接网络（FCN）与多任务学习框架相结合，对ASD风险进行更全面的评估。实验分析结果表明，与三种先进模型三维卷积神经网络（3D-CNN）、U-Net++和深度残差网络（DRN）相比，本文模型的准确率分别高出3.82%、6.17%和6.68%；精确率分别高出0.56%、1.09%和4.01%；召回率分别高出3.41%、4.85%和5.79%。结论表明，AI医学图像辅助诊断系统有助于准确识别ASD的特征，并有效评估腰椎融合术后的风险。
