Autoimmune and autoinflammatory inner ear diseases (AIED/AID) are characterized by the symptom of sensorineural hearing loss (SNHL). To date, standardized diagnostic tools for AIED/AID are lacking, and clinically differentiating AIED/AID from chronic otitis media (COM) with SNHL is challenging. This retrospective study aimed to construct a magnetic resonance imaging (MRI)-based decision tree using classification and regression tree (CART) analysis to distinguish AIED/AID from COM. In total, 67 patients were enrolled between January 2004 and October 2019, comprising AIED/AID (n = 18), COM (n = 24), and control groups (n = 25). All patients underwent 3 T temporal bone MRI, including post-contrast T1-weighted images (postT1WI) and post-contrast FLAIR images (postFLAIR). Two radiologists evaluated the presence of otomastoid effusion and inner ear contrast-enhancement on MRI. A CART decision tree model was constructed using MRI features to differentiate AIED/AID from COM and control groups, and diagnostic performance was analyzed. High-intensity bilateral effusion (61.1%) and inner ear enhancement (postFLAIR, 93.8%; postT1WI, 61.1%) were the most common findings in the AIED/AID group. We constructed two CART decision tree models; the first used effusion amount as the first partitioning node and postT1WI-inner ear enhancement as the second node, whereas the second comprised two partitioning nodes with the degree of postFLAIR-enhancement of the inner ear. The first and second models enabled distinction of AIED/AID from COM with high specificity (100% and 94.3%, respectively). The amount of effusion and the degree of inner ear enhancement on MRI may facilitate the distinction between AIED/AID and COM with SNHL using decision tree models, thereby contributing to early diagnosis and intervention.

自身免疫性和自身炎症性内耳疾病 (AIED/AID) 的特征在于感觉神经性听力损失 (SNHL) 的症状。迄今为止，缺乏针对 AIED/AID 的标准化诊断工具，并且在临床上将 AIED/AID 与慢性中耳炎 (COM) 与 SNHL 区分开来具有挑战性。这项回顾性研究旨在使用分类和回归树 (CART) 分析构建基于磁共振成像 (MRI) 的决策树，以区分 AIED/AID 和 COM。2004 年 1 月至 2019 年 10 月期间共招募了 67 名患者，包括 AIED/AID（n = 18）、COM（n = 24）和对照组（n = 25）。所有患者均接受了 3 T 颞骨 MRI，包括对比后 T1 加权图像（postT1WI）和对比后 FLAIR 图像（postFLAIR）。两名放射科医生评估了 MRI 上耳乳突积液和内耳对比增强的存在。使用MRI特征构建CART决策树模型以区分AIED/AID与COM和对照组，并分析诊断性能。高强度双侧积液 (61.1%) 和内耳增强（postFLAIR，93.8%；postT1WI，61.1%）是 AIED/AID 组中最常见的发现。我们构建了两个CART决策树模型；第一个使用积液量作为第一个分区节点，T1WI-内耳增强后作为第二个节点，而第二个包含两个分区节点，内耳后FLAIR-增强的程度。第一个和第二个模型能够以高特异性（分别为 100% 和 94.3%）将 AIED/AID 与 COM 区分开来。