Han Ju-Hyuck, Kim Yong-Suk, Lee Jong Bin, Kim Hantai, Kim Jong-Yeup, Cho Yongseok
Department of Artificial Intelligence, Konyang University, Daejeon 32992, Republic of Korea.
Department of Otolaryngology, Konyang University Hospital, Daejeon 32992, Republic of Korea.
Sensors (Basel). 2025 Jun 28;25(13):4039. doi: 10.3390/s25134039.
Benign paroxysmal positional vertigo (BPPV) is characterized by torsional nystagmus induced by changes in head position, where accurate quantitative assessment of subtle torsional eye movements is essential for precise diagnosis. Conventional videonystagmography (VNG) techniques face challenges in accurately capturing the rotational components of pupil movements, and existing automated methods typically exhibit limited performance in identifying torsional nystagmus. The objective of this study was to develop an automated system capable of accurately and quantitatively detecting torsional nystagmus. We introduce the Torsion Transformer model, designed to directly estimate torsion angles from iris images. This model employs a self-supervised learning framework comprising two main components: a Decoder module, which learns rotational transformations from image data, and a Finder module, which subsequently estimates the torsion angle. The resulting torsion angle data, represented as time-series, are then analyzed using a 1-dimensional convolutional neural network (1D-CNN) classifier to detect the presence of nystagmus. The performance of the proposed method was evaluated using video recordings from 127 patients diagnosed with BPPV. Our Torsion Transformer model demonstrated robust performance, achieving a sensitivity of 89.99%, specificity of 86.36%, an F1-score of 88.82%, and an area under the receiver operating characteristic curve (AUROC) of 87.93%. These results indicate that the proposed model effectively quantifies torsional nystagmus, with performance levels comparable to established methods for detecting horizontal and vertical nystagmus. Thus, the Torsion Transformer shows considerable promise as a clinical decision support tool in the diagnosis of BPPV. Technical performance improvement in torsional nystagmus detection; System to support clinical decision-making for healthcare professionals.
良性阵发性位置性眩晕（BPPV）的特征是头部位置变化诱发扭转性眼球震颤，其中对细微扭转性眼球运动进行准确的定量评估对于精确诊断至关重要。传统的视频眼震图（VNG）技术在准确捕捉瞳孔运动的旋转成分方面面临挑战，并且现有的自动化方法在识别扭转性眼球震颤方面通常表现出有限的性能。本研究的目的是开发一种能够准确、定量检测扭转性眼球震颤的自动化系统。我们引入了扭转变压器模型，旨在直接从虹膜图像估计扭转角度。该模型采用了一个自监督学习框架，包括两个主要组件：一个解码器模块，从图像数据中学习旋转变换；一个查找器模块，随后估计扭转角度。然后，将表示为时间序列的所得扭转角度数据使用一维卷积神经网络（1D-CNN）分类器进行分析，以检测眼球震颤的存在。使用来自127例被诊断为BPPV患者的视频记录对所提出方法的性能进行了评估。我们的扭转变压器模型表现出强大的性能，灵敏度达到89.99%，特异性为86.36%，F1分数为88.82%，受试者工作特征曲线下面积（AUROC）为87.93%。这些结果表明，所提出的模型有效地量化了扭转性眼球震颤，其性能水平与检测水平和垂直眼球震颤的既定方法相当。因此，扭转变压器作为BPPV诊断中的临床决策支持工具显示出相当大的前景。扭转性眼球震颤检测的技术性能改进；为医疗保健专业人员提供临床决策支持的系统。
