Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study

Cai, Yixi; Yu, Jin-Gang; Chen, Yuebo; Chu, Liu; Xiao, Lichao; Grais, Emad M.; Zhao, Fei; Lan, Liping; Zeng, Shengxin; Zeng, Junbo; Wu, Minjian; Su, Yuejia; Li, Yuanqing; Zheng, Yue

doi:10.1136/bmjopen-2020-041139

Cited by 25 publications

(49 citation statements)

References 27 publications

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“…This is often affected by using different otoscopy systems, such as a standard otoscope 26,27,29 and endo-otoscope. [3][4][5] A further challenge is that the diagnostic accuracy of the ML approaches is affected by the degree and type of pathological changes in the middle ear. For example, Cai et al 3 indicated that it is more difficult to distinguish between normal and OME, whereas there was difficulty in classifying the cases of cerumen and tympanostomy tube in the study by Livingstone et al 24 Therefore, to minimize this challenge, multi-label classifiers trained Not having access to big data is a common challenge when analyzing medical images.…”

Section: Discussionmentioning

confidence: 99%

“…2 MED has a number of forms, for example, tympanic membrane (TM) perforation, acute otitis media (AOM), otitis media with effusion (OME), and chronic otitis media (COM). 3 Without timely diagnosis and treatment, severe and persistent MED can lead to permanent hearing loss, developmental delay in children, and even lifethreatening complications. 4 Otoscopy or otoendoscopy is a clinical examination routinely used by healthcare pro.fessionals, including otologists, audiologists, pediatricians, family practitioners, and those who work in urgent and emergency care services.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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. Level of Evidence NA Laryngoscope, 133:732–741, 2023

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Five studies [25][26][27][28][29] compared the performance of image classification algorithms to human assessors. Of these, three studies compared the performance of the AI algorithm to human assessors using the same test set.…”

Section: Aiversushumanclassificationmentioning

confidence: 99%

Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta‐analysis

Habib

Kajbafzadeh

Hasan

et al. 2022

Clinical Otolaryngology

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Objectives:To summarise the accuracy of artificial intelligence (AI) computer vision algorithms to classify ear disease from otoscopy.Design:Systematic review and meta-analysis.Methods:Using the PRISMA guidelines, nine online databases were searched for articles that used AI computer vision algorithms developed from various methods (convolutional neural networks, artificial neural networks, support vector machines, decision trees and k-nearest neighbours) to classify otoscopic images. Diagnostic classes of interest: normal tympanic membrane, acute otitis media (AOM), otitis media with effusion (OME), chronic otitis media (COM) with or without perforation, cholesteatoma and canal obstruction.Mainoutcomemeasures:Accuracy to correctly classify otoscopic images compared to otolaryngologists (ground truth). The Quality Assessment of Diagnostic Accuracy Studies Version 2 tool was used to assess the quality of methodology and risk of bias.Results: Thirty-nine articles were included. Algorithms achieved 90.7% (95%CI: 90.1-91.3%) accuracy to difference between normal or abnormal otoscopy images in 14 studies. The most common multiclassification algorithm (3 or more diagnostic classes) achieved 97.6% (95%CI: 97.3-97.9%) accuracy to differentiate between normal, AOM and OME in three studies. AI algorithms outperformed human assessors to classify otoscopy images achieving 93.4% (95%CI: 90.5-96.4%) versus 73.2% (95%CI: 67.9-78.5%) accuracy in three studies. Convolutional neural networks achieved the highest accuracy compared to other classification methods. Conclusion:AI can classify ear disease from otoscopy. A concerted effort is requiredto establish a comprehensive and reliable otoscopy database for algorithm training.An AI-supported otoscopy system may assist health care workers, trainees and primary care practitioners with less otology experience identify ear disease.

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“…The binary classification method of still color images of the eardrum has been used to identify the normal ear and otitis media, with accuracy rates of 73.1 and 68.3%, respectively (Tran et al, 2018;Cai et al, 2021). In both cases, color information was used to train the learning models.…”

Section: Discussionmentioning

confidence: 99%

Efficient and accurate diagnosis of otomycosis using an ensemble deep-learning model

Mao

et al. 2022

Front. Mol. Biosci.

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Otomycosis accounts for over 15% of cases of external otitis worldwide. It is common in humid regions and Chinese cultures with ear-cleaning custom. Aspergillus and Candida are the major pathogens causing long-term infection. Early endoscopic and microbiological examinations, performed by otologists and microbiologists, respectively, are important for the appropriate medical treatment of otomycosis. The deep-learning model is a novel automatic diagnostic program that provides quick and accurate diagnoses using a large database of images acquired in clinical settings. The aim of the present study was to introduce a machine-learning model to accurately and quickly diagnose otomycosis caused by Aspergillus and Candida. We propose a computer-aided decision-making system based on a deep-learning model comprising two subsystems: Java web application and image classification. The web application subsystem provides a user-friendly webpage to collect consulted images and display the calculation results. The image classification subsystem mainly trained neural network models for end-to-end data inference. The end user uploads a few images obtained with the ear endoscope, and the system returns the classification results to the user in the form of category probability values. To accurately diagnose otomycosis, we used otoendoscopic images and fungal culture secretion. Fungal fluorescence, culture, and DNA sequencing were performed to confirm the pathogens Aspergillus or Candida spp. In addition, impacted cerumen, external otitis, and normal external auditory canal endoscopic images were retained for reference. We merged these four types of images into an otoendoscopic image gallery. To achieve better accuracy and generalization abilities after model-training, we selected 2,182 of approximately 4,000 ear endoscopic images as training samples and 475 as validation samples. After selecting the deep neural network models, we tested the ResNet, SENet, and EfficientNet neural network models with different numbers of layers. Considering the accuracy and operation speed, we finally chose the EfficientNetB6 model, and the probability values of the four categories of otomycosis, impacted cerumen, external otitis, and normal cases were outputted. After multiple model training iterations, the average accuracy of the overall validation sample reached 92.42%. The results suggest that the system could be used as a reference for general practitioners to obtain more accurate diagnoses of otomycosis.

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Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study

Cited by 25 publications

References 27 publications

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

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

Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta‐analysis

Efficient and accurate diagnosis of otomycosis using an ensemble deep-learning model

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