AUTHOR=Lu Wen , Li Zhuangzhuang , Li Yini , Li Jie , Chen Zhengnong , Feng Yanmei , Wang Hui , Luo Qiong , Wang Yiqing , Pan Jun , Gu Lingyun , Yu Dongzhen , Zhang Yudong , Shi Haibo , Yin Shankai TITLE=RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application JOURNAL=Frontiers in Neuroscience VOLUME=16 YEAR=2022 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.930028 DOI=10.3389/fnins.2022.930028 ISSN=1662-453X ABSTRACT=

Symptoms of vertigo are frequently reported and are usually accompanied by eye-movements called nystagmus. In this article, we designed a three-dimensional nystagmus recognition model and a benign paroxysmal positional vertigo automatic diagnosis system based on deep neural network architectures (Chinese Clinical Trials Registry ChiCTR-IOR-17010506). An object detection model was constructed to track the movement of the pupil centre. Convolutional neural network-based models were trained to detect nystagmus patterns in three dimensions. Our nystagmus detection models obtained high areas under the curve; 0.982 in horizontal tests, 0.893 in vertical tests, and 0.957 in torsional tests. Moreover, our automatic benign paroxysmal positional vertigo diagnosis system achieved a sensitivity of 0.8848, specificity of 0.8841, accuracy of 0.8845, and an F1 score of 0.8914. Compared with previous studies, our system provides a clinical reference, facilitates nystagmus detection and diagnosis, and it can be applied in real-world medical practices.