Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21999, Republic of Korea.
Gachon Biomedical & Convergence Institute, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea.
Sensors (Basel). 2024 May 26;24(11):3417. doi: 10.3390/s24113417.
In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystagmus detection models. Among the four deep learning architectures used in this study, the CNN1D model proposed as a nystagmus detection model demonstrated the best performance, exhibiting a sensitivity of 94.06 ± 0.78%, specificity of 86.39 ± 1.31%, precision of 91.34 ± 0.84%, accuracy of 91.02 ± 0.66%, and an -score of 92.68 ± 0.55%. These results indicate the high accuracy and generalizability of the proposed nystagmus diagnosis algorithm. In conclusion, this study validates the practicality of deep learning in diagnosing BPPV and offers avenues for numerous potential applications of deep learning in the medical diagnostic sector. The findings of this research underscore its importance in enhancing diagnostic accuracy and efficiency in healthcare.
在这项研究中，我们提出了一种基于深度学习的眼球震颤检测算法，使用视频眼动图（VOG）数据来诊断良性阵发性位置性眩晕（BPPV）。我们利用各种深度学习架构来开发和评估眼球震颤检测模型。在本研究中使用的四个深度学习架构中，作为眼球震颤检测模型提出的 CNN1D 模型表现出最佳性能，其灵敏度为 94.06 ± 0.78%，特异性为 86.39 ± 1.31%，精度为 91.34 ± 0.84%，准确率为 91.02 ± 0.66%，AUC 为 92.68 ± 0.55%。这些结果表明，所提出的眼球震颤诊断算法具有较高的准确性和泛化性。总之，本研究验证了深度学习在诊断 BPPV 中的实用性，并为深度学习在医学诊断领域的众多潜在应用提供了途径。这项研究的结果强调了其在提高医疗保健诊断准确性和效率方面的重要性。
