Callejas Pastor Cecilia A, Ryu Hyun Tae, Joo Jung Sook, Ku Yunseo, Suh Myung-Whan
Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon, Republic of Korea.
NPJ Digit Med. 2025 Jul 31;8(1):487. doi: 10.1038/s41746-025-01880-z.
Diagnosing vestibular disorders remains challenging due to complex symptoms and extensive history-taking required. While machine learning approaches have shown promise in medical diagnostics, their application to vestibular disorder classification has been limited. We developed a CatBoost machine learning model to classify six common vestibular disorders using a retrospective dataset of patients. The model incorporates 50 clinical features, selected through a hybrid approach combining algorithmic methods (RFE-SVM and SKB score) and expert clinical knowledge. We designed the system to achieve high sensitivity for common vestibular disorders (BPPV and VM) and high specificity for conditions requiring intensive interventions (MD and HOD) or careful differential diagnosis (PPPD and VEST) to minimize unnecessary invasive treatments. When applied to test data, reaches 88.4% accuracy, with 60.9% correct classifications, 27.5% partially correct, and 11.6% incorrect classifications. Results suggest that machine learning can support clinical decision-making in vestibular disorder diagnosis when combining algorithmic capabilities with clinical expertise.
由于症状复杂且需要进行广泛的病史采集，前庭疾病的诊断仍然具有挑战性。虽然机器学习方法在医学诊断中已显示出前景，但其在眩晕疾病分类中的应用却很有限。我们开发了一种CatBoost机器学习模型，使用患者的回顾性数据集对六种常见的前庭疾病进行分类。该模型纳入了50个临床特征，这些特征是通过结合算法方法（RFE-SVM和SKB评分）和专家临床知识的混合方法选择出来的。我们设计该系统，以实现对常见前庭疾病（耳石症和梅尼埃病）的高敏感性，以及对需要强化干预的疾病（梅尼埃病和上半规管裂综合征）或需要仔细鉴别诊断的疾病（持续性姿势-知觉性头晕和双侧前庭病）的高特异性，以尽量减少不必要的侵入性治疗。当应用于测试数据时，准确率达到88.4%，其中60.9%分类正确，27.5%部分正确，11.6%分类错误。结果表明，将算法能力与临床专业知识相结合时，机器学习可以为前庭疾病诊断中的临床决策提供支持。
