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

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


Journalthe Laryngoscope
Article typeJournal research article – Systematic review
Publication date – Jul – 2022
Authors – Zuwei Cao, Feifan Chen, Emad M. Grais, Fengjuan Yue, Yuexin Cai, De Wet Swanepoel,Fei Zhao
Keywordsmachine learning (ML), otoendoscopic, Tympanic Membrane (TM)
Open access – Yes
SpecialityDigital health, ENT surgery
World region Global

Language – English
Submitted to the One Surgery Index on August 4, 2022 at 12:22 am
Abstract:

Objective
To systematically evaluate the development of Machine Learning (ML) models and compare their diagnostic accuracy for the classification of Middle Ear Disorders (MED) using Tympanic Membrane (TM) images.

Methods
PubMed, EMBASE, CINAHL, and CENTRAL were searched up until November 30, 2021. Studies on the development of ML approaches for diagnosing MED using TM images were selected according to the inclusion criteria. PRISMA guidelines were followed with study design, analysis method, and outcomes extracted. Sensitivity, specificity, and area under the curve (AUC) were used to summarize the performance metrics of the meta-analysis. Risk of Bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool in combination with the Prediction Model Risk of Bias Assessment Tool.

Results
Sixteen studies were included, encompassing 20254 TM images (7025 normal TM and 13229 MED). The sample size ranged from 45 to 6066 per study. The accuracy of the 25 included ML approaches ranged from 76.00% to 98.26%. Eleven studies (68.8%) were rated as having a low risk of bias, with the reference standard as the major domain of high risk of bias (37.5%). Sensitivity and specificity were 93% (95% CI, 90%–95%) and 85% (95% CI, 82%–88%), respectively. The AUC of total TM images was 94% (95% CI, 91%–96%). The greater AUC was found using otoendoscopic images than otoscopic images.

Conclusions
ML approaches perform robustly in distinguishing between normal ears and MED, however, it is proposed that a standardized TM image acquisition and annotation protocol should be developed.

OSI Number – 21703

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