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

Cao, Zuwei; Chen, Feifan; Grais, Emad M.; Yue, Fengjuan; Cai, Yixi; Swanepoel, De Wet; Zhao, Fei

doi:10.1002/lary.30291

Cited by 14 publications

(20 citation statements)

References 44 publications

(124 reference statements)

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“…First, we enrolled a representative cross-sectional sample of young children presenting for primary care visits. Many studies to date have been conducted in children referred to specialty clinics who could presumably have more severe and easier to diagnose findings . Other studies have included only clearcut cases and controls, which can lead to overestimation of diagnostic accuracy .…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children

Shaikh,

Conway,

Kovačević

et al. 2024

JAMA Pediatr

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ImportanceAcute otitis media (AOM) is a frequently diagnosed illness in children, yet the accuracy of diagnosis has been consistently low. Multiple neural networks have been developed to recognize the presence of AOM with limited clinical application.ObjectiveTo develop and internally validate an artificial intelligence decision-support tool to interpret videos of the tympanic membrane and enhance accuracy in the diagnosis of AOM.Design, Setting, and ParticipantsThis diagnostic study analyzed otoscopic videos of the tympanic membrane captured using a smartphone during outpatient clinic visits at 2 sites in Pennsylvania between 2018 and 2023. Eligible participants included children who presented for sick visits or wellness visits.ExposureOtoscopic examination.Main Outcomes and MeasuresUsing the otoscopic videos that were annotated by validated otoscopists, a deep residual-recurrent neural network was trained to predict both features of the tympanic membrane and the diagnosis of AOM vs no AOM. The accuracy of this network was compared with a second network trained using a decision tree approach. A noise quality filter was also trained to prompt users that the video segment acquired may not be adequate for diagnostic purposes.ResultsUsing 1151 videos from 635 children (majority younger than 3 years of age), the deep residual-recurrent neural network had almost identical diagnostic accuracy as the decision tree network. The finalized deep residual-recurrent neural network algorithm classified tympanic membrane videos into AOM vs no AOM categories with a sensitivity of 93.8% (95% CI, 92.6%-95.0%) and specificity of 93.5% (95% CI, 92.8%-94.3%) and the decision tree model had a sensitivity of 93.7% (95% CI, 92.4%-94.9%) and specificity of 93.3% (92.5%-94.1%). Of the tympanic membrane features outputted, bulging of the TM most closely aligned with the predicted diagnosis; bulging was present in 230 of 230 cases (100%) in which the diagnosis was predicted to be AOM in the test set.Conclusions and RelevanceThese findings suggest that given its high accuracy, the algorithm and medical-grade application that facilitates image acquisition and quality filtering could reasonably be used in primary care or acute care settings to aid with automated diagnosis of AOM and decisions regarding treatment.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children

Shaikh,

Conway,

Kovačević

et al. 2024

JAMA Pediatr

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“…19 A variety of ML models with different objectives have been built in the field of audiology, including the prediction of middle ear disorders, detection of auditory brain stem response and prediction of ISSNHL outcomes. 20,21 Although several ML models have been built to predict the prognosis of ISSNHL, their performances have not been reviewed. Furthermore, the quality and applicability of these models in hospital settings are yet to be discussed.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review

Aghakhani,

Yousefi,

Yekaninejad

2023

Ann Otol Rhinol Laryngol

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Background: Machine Learning models have been applied in various healthcare fields, including Audiology, to predict disease outcomes. The prognosis of sudden sensorineural hearing loss is difficult to predict due to the variable course of the disease. Hence, researchers have attempted to utilize ML models to predict the outcome of patients with sudden sensorineural hearing loss. The objectives of this study were to review the performance of these machine learning models and assess their applicability in real-world settings. Methods: A systematic search was conducted in PubMed, Web of Science and Scopus. Only studies that built machine learning prediction models were included, and studies that used algorithms such as logistic regression only for the purpose of adjusting for confounding variables were excluded. The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Results: After screening, a total of 7 papers were eligible for synthesis. In total, these studies built 48 ML models. The most common utilized algorithms were Logistic Regression, Support Vector Machine (SVM) and boosting. The area under the curve of the receiver operating characteristic curve ranged between 0.59 and 0.915. All of the included studies had a high risk of bias; hence there are concerns regarding their applicability. Conclusion: Although these models showed great performance and promising results, future studies are still needed before these models can be applied in a real-world setting. Future studies should employ multiple cohorts, different feature selection methods, and external validation to further validate the models’ applicability.

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“…For instance, AI has demonstrated potential for broad applications in otolaryngology, ranging from the diagnosis of laryngeal cancer 3 to ear disease using otoscopic images. 4,5 While several reviews have examined AI and its applications in otolaryngology, 2,[6][7][8] rhinology, 9,10 otological images, 4,5,11 laryngeal cancer, 3,12 and head and neck cancer diagnosis, 13 some of them were based on the data from a few years ago and focused on only some diseases or specialties. With the emergence of new algorithms, it is important to update the literature and provide otolaryngologists with an overview of AI applications in otolaryngology.…”

mentioning

confidence: 99%

“…This motivated the clinical application of AI to automate numerous medical tasks. For instance, AI has demonstrated potential for broad applications in otolaryngology, ranging from the diagnosis of laryngeal cancer 3 to ear disease using otoscopic images 4,5 …”

mentioning

confidence: 99%

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

Wang

Liang

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveTo update the literature and provide a systematic review of image‐based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges.Data SourcesWeb of Science, Embase, PubMed, and Cochrane Library.Review MethodsStudies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies.ResultsOverall, 686 studies were identified. After screening titles and abstracts, 325 full‐text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively.ConclusionThis state‐of‐the‐art review aimed to highlight the increasing applications of image‐based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real‐world clinical practice. Future studies should consider 3‐dimensional (3D)‐based AI, such as 3D surgical AI.

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

Cited by 14 publications

References 44 publications

Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children

Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children

Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

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