Enhanced diagnosis of axial spondyloarthritis using machine learning with sacroiliac joint MRI: a multicenter study.

IF 4.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Insights into Imaging Pub Date : 2025-04-25 DOI:10.1186/s13244-025-01967-x

Zhuoyao Xie, Zefeiyun Chen, Qinmei Yang, Qiang Ye, Xin Li, Qiuxia Xie, Caolin Liu, Bomiao Lin, Xinai Han, Yi He, Xiaohong Wang, Wei Yang, Yinghua Zhao

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引用次数: 0

Abstract

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.

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骶髂关节MRI机器学习增强轴型脊柱炎诊断：一项多中心研究。

目的：建立基于MRI和临床危险因素的机器学习（ML）模型，以提高轴性脊柱炎（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，重点是慢性腰痛患者。来自中心A的一个子集用于前瞻性验证。基于ResNet50构建骶髂关节MRI深度学习（DL）模型。临床变量与ML算法中的DL评分相结合，以区分axSpA患者和非axSpA患者。通过受试者工作特征曲线下面积（AUC）、灵敏度、特异性和准确性来评估模型的性能。结果：研究纳入1294例患者(中位年龄31岁[四分位数间距24-42]；35.5%的女性)。确定的临床危险因素有年龄、性别和人白细胞抗原b27状态。基于mri的深度学习模型在内部测试集、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。组合模型，特别是K-nearest-neighbors-11算法，在多个测试集上表现出优异的性能，auc范围为0.853 ~ 0.912。以更好的AUC （0.858 vs. 0.650, p）超过了国际脊椎关节炎协会（Assessment of SpondyloArthritis International Society）的标准。结论：结合MRI和临床危险因素的ML方法能有效识别出axSpA，是一种很有前景的诊断和治疗axSpA的工具。临床相关性声明：结合MRI和临床危险因素的机器学习模型可能使轴型脊柱炎患者的早期诊断和干预成为可能，减少了传统诊断方法通常相关的延迟。重点：轴性脊柱炎（AxSpA）缺乏明确的诊断标准或标志物，导致诊断延迟。基于mri的深度学习提供了指示axSpA的骶髂关节变化的定量分析。结合骶髂关节MRI和临床危险因素的机器学习模型增强了axSpA的识别。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

7.30

自引率

4.30%

发文量

182

审稿时长

13 weeks

期刊介绍： Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.