Lu Zixiao, Zou Qingqing, Wang Menghong, Han Xinai, Shi Xingliang, Wu Shufan, Xie Zhuoyao, Ye Qiang, Song Liwen, He Yi, Feng Qianjin, Zhao Yinghua
Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, China.
School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Quant Imaging Med Surg. 2024 Aug 1;14(8):5845-5860. doi: 10.21037/qims-24-729. Epub 2024 Jul 30.
Axial spondyloarthritis (axSpA) is frequently diagnosed late, particularly in human leukocyte antigen (HLA)-B27-negative patients, resulting in a missed opportunity for optimal treatment. This study aimed to develop an artificial intelligence (AI) tool, termed NegSpA-AI, using sacroiliac joint (SIJ) magnetic resonance imaging (MRI) and clinical SpA features to improve the diagnosis of axSpA in HLA-B27-negative patients.
We retrospectively included 454 HLA-B27-negative patients with rheumatologist-diagnosed axSpA or other diseases (non-axSpA) from the Third Affiliated Hospital of Southern Medical University and Nanhai Hospital between January 2010 and August 2021. They were divided into a training set (n=328) for 5-fold cross-validation, an internal test set (n=72), and an independent external test set (n=54). To construct a prospective test set, we further enrolled 87 patients between September 2021 and August 2023 from the Third Affiliated Hospital of Southern Medical University. MRI techniques employed included T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed (FS) sequences. We developed NegSpA-AI using a deep learning (DL) network to differentiate between axSpA and non-axSpA at admission. Furthermore, we conducted a reader study involving 4 radiologists and 2 rheumatologists to evaluate and compare the performance of independent and AI-assisted clinicians.
NegSpA-AI demonstrated superior performance compared to the independent junior rheumatologist (≤5 years of experience), achieving areas under the curve (AUCs) of 0.878 [95% confidence interval (CI): 0.786-0.971], 0.870 (95% CI: 0.771-0.970), and 0.815 (95% CI: 0.714-0.915) on the internal, external, and prospective test sets, respectively. The assistance of NegSpA-AI promoted discriminating accuracy, sensitivity, and specificity of independent junior radiologists by 7.4-11.5%, 1.0-13.3%, and 7.4-20.6% across the 3 test sets (all P<0.05). On the prospective test set, AI assistance also improved the diagnostic accuracy, sensitivity, and specificity of independent junior rheumatologists by 7.7%, 7.7%, and 6.9%, respectively (all P<0.01).
The proposed NegSpA-AI effectively improves radiologists' interpretations of SIJ MRI and rheumatologists' diagnoses of HLA-B27-negative axSpA.
轴性脊柱关节炎（axSpA）常常在晚期才得以诊断，尤其是在人类白细胞抗原（HLA）-B27阴性的患者中，这导致错失了最佳治疗时机。本研究旨在开发一种人工智能（AI）工具，即NegSpA-AI，利用骶髂关节（SIJ）磁共振成像（MRI）和临床SpA特征来改善对HLA-B27阴性患者的axSpA诊断。
我们回顾性纳入了2010年1月至2021年8月期间来自南方医科大学第三附属医院和南海医院的454例经风湿病学家诊断为axSpA或其他疾病（非axSpA）的HLA-B27阴性患者。他们被分为用于五折交叉验证的训练集（n = 328）、内部测试集（n = 72）和独立外部测试集（n = 54）。为构建一个前瞻性测试集，我们于2021年9月至2023年8月从南方医科大学第三附属医院又纳入了87例患者。所采用的MRI技术包括T1加权（T1W）、T2加权（T2W）和脂肪抑制（FS）序列。我们使用深度学习（DL）网络开发NegSpA-AI，以在入院时区分axSpA和非axSpA。此外，我们开展了一项由4名放射科医生和2名风湿病学家参与的阅片者研究评估并比较独立临床医生和AI辅助临床医生的表现。
与独立的初级风湿病学家（经验≤5年）相比，NegSpA-AI表现出更优性能，在内部测试集、外部测试集和前瞻性测试集上的曲线下面积（AUC）分别达到0.878 [95%置信区间（CI）：0.786 - 0.971]、0.870（95% CI：0.771 - 0.970）和0.815（95% CI：0.714 - 0.915）。在3个测试集中，NegSpA-AI的辅助使独立初级放射科医生的鉴别准确性、敏感性和特异性分别提高了7.4 - 11.5%、1.0 - 13.3%和7.4 - 20.6%（所有P < 0.05）。在前瞻性测试集中，AI辅助还使独立初级风湿病学家的诊断准确性、敏感性和特异性分别提高了7.7%、7.7%和6.9%（所有P < 0.01）。
所提出的NegSpA-AI有效改善了放射科医生对SIJ MRI的解读以及风湿病学家对HLA-B27阴性axSpA的诊断。
