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Assessing Body Temperature - Tympanic membrane

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Machine Learning in Diagnosing Middle Ear Disorders Using Tympanic Membrane Images: A Meta-Analysis.

Zuwei Cao¹, Feifan Chen², Emad M Grais²

¹Center for Rehabilitative Auditory Research, Guizhou Provincial People's Hospital, Guiyang City, China.

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|July 18, 2022

Summary

This summary is machine-generated.

Machine learning models show strong diagnostic accuracy for middle ear disorders using tympanic membrane images. Developing standardized image protocols is recommended for future advancements in this field.

Keywords:

artificial intelligence deep learning hearing healthcare machine learning middle ear disorders otitis media otoscopy tympanic membrane

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Area of Science:

Otolaryngology
Medical Imaging
Artificial Intelligence

Background:

Middle Ear Disorders (MED) diagnosis relies on tympanic membrane (TM) imaging.
Machine Learning (ML) offers potential for automated diagnostic accuracy.

Approach:

Systematic review and meta-analysis of 16 studies (up to Nov 2021) evaluating ML models for MED classification.
Included 20,254 TM images; assessed diagnostic accuracy using sensitivity, specificity, and AUC.
Evaluated risk of bias using QUADAS-2 and PROBA tools.

Key Points:

ML model accuracy for MED classification ranged from 76.00% to 98.26%.
Overall sensitivity was 93% and specificity was 85%, with an AUC of 94%.
Otoendoscopic images yielded higher AUC than otoscopic images.

Conclusions:

ML models demonstrate robust performance in differentiating normal TM from MED.
Standardized TM image acquisition and annotation protocols are crucial for further development.