■轴性脊柱关节炎(axSpA)经常被诊断为晚期,特别是在人类白细胞抗原(HLA)-B27阴性患者中,导致错过最佳治疗的机会。这项研究旨在开发一种人工智能(AI)工具,被称为NegSpA-AI,使用骶髂关节(SIJ)磁共振成像(MRI)和临床SpA特征来提高HLA-B27阴性患者对axSpA的诊断。
■我们回顾性纳入了2010年1月至2021年8月南方医科大学第三附属医院和南海医院的454名HLA-B27阴性风湿病专家诊断的axSpA或其他疾病(非axSpA)患者。他们被分成一个训练集(n=328)进行5倍交叉验证,内部测试集(n=72),和独立的外部测试集(n=54)。要构建一个预期的测试集,我们进一步纳入了2021年9月至2023年8月来自南方医科大学第三附属医院的87例患者.使用的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)网络开发了NegSpA-AI,以在入院时区分axSpA和非axSpA。此外,我们进行了一项包括4名放射科医师和2名风湿病医师的读者研究,以评估和比较独立和AI辅助临床医师的表现.
■NegSpA-AI与独立的初级风湿病学家(≤5年的经验)相比表现出众,曲线下面积(AUC)为0.878[95%置信区间(CI):0.786-0.971],0.870(95%CI:0.771-0.970),和内部0.815(95%CI:0.714-0.915),外部,和预期的测试集,分别。NegSpA-AI的帮助提高了辨别的准确性,灵敏度,独立初级放射科医生的特异性为7.4-11.5%,1.0-13.3%,在3个测试集中为7.4-20.6%(均P<0.05)。在前瞻性测试集上,人工智能辅助还提高了诊断的准确性,灵敏度,独立初级风湿病学家的特异性下降了7.7%,7.7%,和6.9%,(均P<0.01)。
■提出的NegSpA-AI有效地改善了放射科医师对SIJMRI的解释和风湿病学家对HLA-B27阴性axSpA的诊断。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: The proposed NegSpA-AI effectively improves radiologists\' interpretations of SIJ MRI and rheumatologists\' diagnoses of HLA-B27-negative axSpA.