Xie Zhuoyao, Chen Zefeiyun, Yang Qinmei, Ye Qiang, Li Xin, Xie Qiuxia, Liu Caolin, Lin Bomiao, Han Xinai, He Yi, Wang Xiaohong, Yang Wei, Zhao Yinghua
Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong Province), Guangzhou, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Insights Imaging. 2025 Apr 25;16(1):91. doi: 10.1186/s13244-025-01967-x.
OBJECTIVES: To develop a machine learning (ML)-based model using MRI and clinical risk factors to enhance diagnostic accuracy for axial spondyloarthritis (axSpA). METHODS: We retrospectively analyzed datasets from four centers (A-D), focusing on patients with chronic low back pain. A subset from center A was used for prospective validation. A deep learning (DL) model based on ResNet50 was constructed using sacroiliac joint MRI. Clinical variables were integrated with DL scores in ML algorithms to distinguish axSpA from non-axSpA patients. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. RESULTS: The study included 1294 patients (median age 31 years [interquartile range 24-42]; 35.5% females). Clinical risk factors identified were age, sex, and human leukocyte antigen-B27 status. The MRI-based DL model demonstrated an AUC of 0.837, 0.636, 0.724, 0.710, and 0.812 on the internal test set, three external test sets, and the prospective validation set, respectively. The combined model, particularly the K-nearest-neighbors-11 algorithm, demonstrated superior performance across multiple test sets with AUCs ranging from 0.853 to 0.912. It surpassed the Assessment of SpondyloArthritis International Society criteria with better AUC (0.858 vs. 0.650, p < 0.001), sensitivity (87.8% vs. 42.4%, p < 0.001), and accuracy (78.7% vs. 56.9%, p < 0.001). CONCLUSION: The ML method integrating MRI and clinical risk factors effectively identified axSpA, representing a promising tool for the diagnosis and management of axSpA. CLINICAL RELEVANCE STATEMENT: The machine learning model combining MRI and clinical risk factors potentially enables earlier diagnosis and intervention for axial spondyloarthritis patients, reducing the delays commonly associated with traditional diagnostic approaches. KEY POINTS: Axial spondyloarthritis (AxSpA) lacks definitive diagnostic criteria or markers, leading to diagnostic delay. MRI-based deep learning provided quantitative analysis of sacroiliac joint changes indicative of axSpA. A machine learning model combining sacroiliac joint MRI and clinical risk factors enhanced axSpA identification.
目的：开发一种基于机器学习（ML）的模型，利用磁共振成像（MRI）和临床风险因素提高轴向型脊柱关节炎（axSpA）的诊断准确性。 方法：我们回顾性分析了来自四个中心（A - D）的数据集，重点关注慢性下腰痛患者。中心A的一个子集用于前瞻性验证。使用骶髂关节MRI构建基于ResNet50的深度学习（DL）模型。将临床变量与DL评分整合到ML算法中，以区分axSpA患者和非axSpA患者。通过受试者操作特征曲线（AUC）下的面积、敏感性、特异性和准确性评估模型性能。 结果：该研究纳入了1294例患者（中位年龄31岁[四分位间距24 - 42]；35.5%为女性）。确定的临床风险因素为年龄、性别和人类白细胞抗原B27状态。基于MRI的DL模型在内部测试集、三个外部测试集和前瞻性验证集上的AUC分别为0.837、0.636、0.724、0.710和0.812。联合模型，特别是K近邻 - 11算法，在多个测试集上表现出卓越性能，AUC范围为0.853至0.912。它在AUC（0.858对0.650，p < 0.001）、敏感性（87.8%对42.4%，p < 0.001）和准确性（78.7%对56.9%，p < 0.001）方面超过了国际脊柱关节炎评估协会标准。 结论：整合MRI和临床风险因素的ML方法有效识别了axSpA，是一种用于axSpA诊断和管理的有前景的工具。 临床相关性声明：结合MRI和临床风险因素的机器学习模型可能使轴向型脊柱关节炎患者能够更早地诊断和干预，减少与传统诊断方法通常相关的延误。 关键点：轴向型脊柱关节炎（AxSpA）缺乏明确的诊断标准或标志物，导致诊断延迟。基于MRI的深度学习对指示axSpA的骶髂关节变化进行了定量分析。结合骶髂关节MRI和临床风险因素的机器学习模型提高了axSpA的识别能力。
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