Deep learning based torsional nystagmus detection for dizziness and vertigo diagnosis

Zhang, Wanlu; Wu, Haiyan; Liu, Yang; Zheng, Shuai; Liu, Zhizhe; Li, Youru; Zhao, Yao; Zhu, Zhenfeng

doi:10.1016/j.bspc.2021.102616

Cited by 19 publications

(16 citation statements)

References 30 publications

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“…The improvement of our torsional movement detection method, compared with previous work, ( Ong and Haslwanter, 2010 ; Jin et al, 2020 ; Zhang et al, 2021 ) can be attributed to the implementation of several image processing techniques. We first adopted the log-polar transformation to extract iris features, then applied phase correlation techniques to measure the shift between each frame.…”

Section: Discussionmentioning

confidence: 86%

“…Previous studies have investigated the automatic detection of nystagmus, while an entire AI-based BPPV diagnosis system has not been implemented. Zhang et al (2021) proposed a model for torsional BPPV nystagmus based on optical flow techniques which could effectively avoid the disturbance due to eyelash occlusion and pupil deformation. However, this model only supplied a basal framework for torsional nystagmus detection and could not be directly applied in disease diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“…Techniques mentioned in previous studies ( Ong and Haslwanter, 2010 ; Jin et al, 2020 ; Zhang et al, 2021 ) belong to two main categories. The first type of technique involves extraction of iris feature points and then using them to calculate the displacement of the iris based on the correspondence of the feature points between the two frames of images, such as the optical flow method.…”

Section: Discussionmentioning

confidence: 99%

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RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application

et al. 2022

Front. Neurosci.

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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.

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Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application

et al. 2022

Front. Neurosci.

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“…et al [35] used a deep learning model trained on extracted image data from nystagmus videos induced by positional tests to classify the affected canal in BPPV patients. More recently, a novel deep learning based framework involving convolutional neural networks was introduced for automatic detection of torsional up beating nystagmus of PC BPV from nystagmus videos [38]. When tested on a clinically collected torsional nystagmus video dataset, the method showed promising results in frame-level identification of torsional motion and final torsional nystagmus segment localization, which can help clinicians improve their diagnostic accuracy.…”

Section: Machine Learning Applications To Nystagmus and Vestibulo-ocular Reflex (Vor) Testsmentioning

confidence: 99%

“…These models process the patients' data, find the correlations and associations of presenting symptoms, familiar antecedents, habits, and background medical history with a view to predicting vertigo aetiology. The machine learning models most commonly used in vertigo diagnosis include decision trees [22][23][24][25], support vector machines (SVM) [22,[25][26][27][28][29][30][31][32][33][34], k-Nearest neighbors (KNN) [20,23,[25][26][27]30,35,36], and deep learning techniques [35,37,38]. Some researchers have also used novel ML algorithms and ensemble learning to improve diagnostic accuracy [28,33,[39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review

Kabade

Hooda

Raj

et al. 2021

Sensors

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Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis.

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SGT: Scene Graph-Guided Transformer for Surgical Report Generation

Lin¹,

Zheng²,

Liu³

et al. 2022

Lecture Notes in Computer Science

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Deep learning based torsional nystagmus detection for dizziness and vertigo diagnosis

Cited by 19 publications

References 30 publications

RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application

RETRACTED: A Deep Learning Model for Three-Dimensional Nystagmus Detection and Its Preliminary Application

Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review

SGT: Scene Graph-Guided Transformer for Surgical Report Generation

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