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dc.contributor.author | Cao, Zuwei![]() |
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dc.contributor.author | Chen, Feifan![]() |
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dc.contributor.author | Grais, Emad M.![]() |
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dc.contributor.author | Yue, Fengjuan![]() |
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dc.contributor.author | Cai, Yuexin![]() |
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dc.contributor.author | Swanepoel, De Wet![]() |
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dc.contributor.author | Zhao, Fei![]() |
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dc.date.accessioned | 2024-08-20T10:32:39Z | |
dc.date.available | 2024-08-20T10:32:39Z | |
dc.date.issued | 2023-04 | |
dc.description.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. | en_US |
dc.description.department | Electrical, Electronic and Computer Engineering | en_US |
dc.description.department | Speech-Language Pathology and Audiology | en_US |
dc.description.librarian | am2024 | en_US |
dc.description.sdg | SDG-03:Good heatlh and well-being | en_US |
dc.description.sdg | SDG-09: Industry, innovation and infrastructure | en_US |
dc.description.sponsorship | NIHR, Sêr Cymru III Enhancing Competitiveness Infrastructure Award, Great Britain Sasakawa Foundation, Cardiff Metropolitan University Research Innovation Award, and The Global Academies Research and Innovation Development Fund, National Natural Science Foundation of China, Guizhou Provincial Science and Technology Projects and Global Academies and Santandar 2021 Fellowship Award. | en_US |
dc.description.uri | https://onlinelibrary.wiley.com/journal/15314995 | en_US |
dc.identifier.citation | Cao, Z., Chen, F., Grais, E.M. et al. 2023, 'Machine learning in diagnosing middle ear disorders using tympanic membrane images : a meta-analysis', Laryngoscope, vol. 133, no. 4., pp. 732-741. DOI: 10.1002/lary.30291. | en_US |
dc.identifier.issn | 0023-852X (print) | |
dc.identifier.issn | 1531-4995 (online) | |
dc.identifier.other | 10.1002/lary.30291 | |
dc.identifier.uri | http://hdl.handle.net/2263/97742 | |
dc.language.iso | en | en_US |
dc.publisher | Wiley | en_US |
dc.rights | © 2022 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License. | en_US |
dc.subject | Otoscopy | en_US |
dc.subject | Tympanic membrane | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Otitis media | en_US |
dc.subject | Hearing healthcare | en_US |
dc.subject | Artificial intelligence (AI) | en_US |
dc.subject | Middle ear disorders (MED) | en_US |
dc.subject | Area under the curve (AUC) | en_US |
dc.subject | SDG-09: Industry, innovation and infrastructure | en_US |
dc.subject | SDG-03: Good health and well-being | en_US |
dc.title | Machine learning in diagnosing middle ear disorders using tympanic membrane images : a meta-analysis | en_US |
dc.type | Article | en_US |