Classification of Different Tympanic Membrane Conditions Using Fused Deep Hypercolumn Features and Bidirectional LSTM

Uçar, Murat; Akyol, Kemal; Atila, Ümit; Uçar, Emine

doi:10.1016/j.irbm.2021.01.001

Cited by 7 publications

(9 citation statements)

References 30 publications

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“…Figure 1 shows the study selection process in a PRISMA flow diagram. Sixteen studies 3,6,9,[21][22][23][24][25][26][27][28][29][30][31][32][33] were eventually included for the quality assessment and meta-analysis with the first reported in 2016. 22 The key characteristics of the included studies are summarized in Table I.…”

Section: Resultsmentioning

confidence: 99%

“…To evaluate the performance of ML in diagnosing MED, apart from accuracy, different performance evaluation metrics were used. These included; sensitivity, 21,22 specificity, 21,22 F ‐score, 23,25 AUC‐ROC 23,25 and in some studies PPV 22,27,30 . As there is no guideline on reporting these diagnostic test accuracy studies using ML approaches, not all studies reported the other evaluation parameters, such as sensitivity, specificity, F‐score, or AUC.…”

Section: Resultsmentioning

confidence: 99%

“…These included; sensitivity, 21,22 specificity, 21,22 F-score, 23,25 AUC-ROC 23,25 and in some studies PPV. 22,27,30 As there is no guideline on reporting these diagnostic test accuracy studies using ML approaches, not all studies reported the other evaluation parameters, such as sensitivity, specificity, F-score, or AUC. Therefore, in this review, sensitivity, specificity, and AUC for individual studies without these evaluation metrics were re-calculated on the basis of TP, FP, FN, and TN.…”

Section: Approach Performance Evaluation and Meta-analysismentioning

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: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Approach Performance Evaluation and Meta-analysismentioning

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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“…SVM tries to minimize the upper bound of the error with the hyperplane. It is also a classification algorithm that maximizes the boundary separation between training data [25]. Unlike other machine learning methods, the performance of SVM, having a strong learning and generalization ability, is quite better [26].…”

Section: Support Vector Machinesmentioning

confidence: 99%

“…The size of the AUC area indicates the success of the machine learning model in distinguishing the classes. The ideal value for AUC is 1 [25].…”

Section: Performance Metricsmentioning

confidence: 99%

Classification of Tympanic Membrane Images based on VGG16 Model

Çalışkan

2022

Kocaeli Journal of Science and Engineering

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Otitis Media (OM) is a type of infectious disease caused by viruses and/or bacteria in the middle ear cavity. In the current study, it was aimed to detect the eardrum region in middle ear images and thus to diagnose OM disease by using artificial intelligence methods. The Convolution Neural Networks (CNN) model and the deep features of this model and the images obtained with the otoscope device were used. In order to separate these images as Normal and Anomalous, the end-to-end VGG16 model was directly used in the first stage of the experimental work. In the second stage of the experimental study, the activation maps of the fc6 and fc7 layers consisting of 4096 features and the Fc8 layer consisting of 1000 features of the VGG16 CNN model were obtained. Then, it was given as input to Support Vector Machines (SVM). Then, the deep features obtained from all activation maps were combined and a new feature set was obtained. In the last stage, this feature set is given as an input to SVM. Thus, the effect of the VGG16 model and the features obtained from the layers of this model on the success of distinguishing images of the eardrum was investigated. As a result of the experimental study, the best performance results were obtained with the fc6 layer. The results showed that OM disease could be accurately detected by using a deep CNN architecture. The proposed deep learning-based classification system promises highly accurate results for disease detection.

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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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Classification of Different Tympanic Membrane Conditions Using Fused Deep Hypercolumn Features and Bidirectional LSTM

Cited by 7 publications

References 30 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

Classification of Tympanic Membrane Images based on VGG16 Model

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

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