Tang Xiaowu, Ye Weijie, Ou Yongkang, Ye Hongsheng, Zhu Xiran, Huang Dong, Liu Jinming, Zhao Fei, Deng Wenting, Li Chenlong, Cai Weiwei, Zheng Yiqing, Zeng Junbo, Cai Yuexin
Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Institute of Hearing and Speech-language Science, Sun Yat-sen University, Guangzhou, China.
Laryngoscope. 2025 May;135(5):1652-1660. doi: 10.1002/lary.31959. Epub 2024 Dec 19.
This study aims to investigate whether artificial intelligence can improve the diagnostic accuracy of vertigo related diseases.
Based on the clinical guidelines, clinical symptoms and laboratory test results were extracted from electronic medical records as variables. These variables were then input into a machine learning diagnostic model for classification and diagnosis. This study encompasses two primary objectives: Task 1 to distinguish between patients with Benign Paroxysmal Positional Vertigo (BPPV) and non-BPPV. In Task 2, further classifying non-BPPV patients into Ménière's Disease (MD), Vestibular Migraine (VM), and Sudden Sensorineural Hearing Loss accompanied by Vertigo (SSNHLV). The sensitivity, precision, and area under the curve (AUC) metric is primarily used to assess the performance of the machine learning model development phase in a prospective validation cohort.
In our study, 1789 patients were recruited as the training cohort and 1148 patients as the prospective validation cohort. The comprehensive diagnostic performance of the XGBoost model surpasses that of traditional models. The sensitivity, accuracy, and AUC in task 1 were 98.32%, 87.03%, and 0.947, respectively. In task 2, the sensitivity values for MD, SSNHLV, and VM were 89.00%, 100.0%, and 79.40%, respectively. The precision values were 88.80%, 100.0%, and 80.00%, respectively. The AUC values were 0.933, 1.000, and 0.931, respectively. The model can significantly improve the accuracy of diagnosing vertigo diseases.
This system may enhance the accuracy of classification and diagnosis of vertigo diseases. It offers initial therapy or referrals to clinical doctors, particularly in resource-limited settings.
NA Laryngoscope, 135:1652-1660, 2025.
本研究旨在探讨人工智能是否能提高眩晕相关疾病的诊断准确性。
根据临床指南，从电子病历中提取临床症状和实验室检查结果作为变量。然后将这些变量输入机器学习诊断模型进行分类和诊断。本研究包含两个主要目标：任务1区分良性阵发性位置性眩晕（BPPV）患者和非BPPV患者。在任务2中，将非BPPV患者进一步分为梅尼埃病（MD）、前庭性偏头痛（VM）和伴有眩晕的突发性感音神经性听力损失（SSNHLV）。敏感性、精确性和曲线下面积（AUC）指标主要用于在前瞻性验证队列中评估机器学习模型开发阶段的性能。
在我们的研究中，招募了1789名患者作为训练队列，1148名患者作为前瞻性验证队列。XGBoost模型的综合诊断性能优于传统模型。任务1中的敏感性、准确性和AUC分别为98.32%、87.03%和0.947。在任务2中，MD、SSNHLV和VM的敏感性值分别为89.00%、100.0%和79.40%。精确性值分别为88.80%、100.0%和80.00%。AUC值分别为0.933、1.000和0.931。该模型可显著提高眩晕疾病的诊断准确性。
该系统可能提高眩晕疾病分类和诊断的准确性。它为临床医生提供初始治疗建议或转诊建议，特别是在资源有限的环境中。
NA 喉镜，135:1652 - 1660，2025年。
