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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Developing a Diagnostic Decision Support System for Benign Paroxysmal Positional Vertigo Using a Deep-Learning Model.

Eun-Cheon Lim^1,2, Jeong Hye Park^3,4, Han Jae Jeon⁵

¹Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang 14068, Korea. abysslover@gmail.com.

Journal of Clinical Medicine

|May 11, 2019

Summary

A new deep-learning model accurately interprets nystagmus, aiding benign paroxysmal positional vertigo (BPPV) diagnosis. This machine learning approach shows high sensitivity and specificity for identifying eye movements and affected canals in BPPV patients.

Keywords:

artificial intelligence benign paroxysmal positional vertigo deep learning vertigo

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Area of Science:

Ophthalmology
Neurology
Artificial Intelligence

Background:

Diagnosis of benign paroxysmal positional vertigo (BPPV) relies on interpreting positional nystagmus.
Interpretation challenges exist in primary care and emergency settings.
Machine learning may improve diagnostic accuracy.

Purpose of the Study:

To develop and validate a deep-learning model for interpreting nystagmus in BPPV.
To assess the model's accuracy in classifying nystagmus types and localizing the affected canal.

Main Methods:

Utilized a large dataset of 91,778 nystagmus videos from 3,467 patients.
Annotated 3D nystagmus movements by otologic experts.
Trained a neural network using 255 grid images derived from video features.

Main Results:

Achieved high sensitivity and specificity for horizontal (0.910/0.919), vertical (0.879/0.894), and torsional (0.783/0.799) nystagmus.
Successfully predicted the affected canal with 0.806 sensitivity and 0.971 specificity.
Demonstrated robust performance across different nystagmus types.

Conclusions:

The deep-learning model exhibits high accuracy in classifying nystagmus and localizing the affected canal in BPPV.
This AI tool shows significant potential for widespread clinical application in BPPV diagnosis.