Automated Classification of the Tympanic Membrane Using a Convolutional Neural Network

Lee, Je Yeon; Choi, Seung-Ho; Chung, Jong Woo

doi:10.3390/app9091827

Cited by 48 publications

(49 citation statements)

References 22 publications

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“…Although the aim of such study is not conducted to support or help diagnose, the positive rate detection of structures of the tympanic membrane was the highest reported up to date: 93.1%. Recently, convolutional neural networks (CNNs) were used to otitis media diagnosis tasks [30,31]. The authors in [30] employed a transfer learning approach to classify between four classes: normal, acute otitis media, chronic suppurative otitis media, and earwax.…”

Section: Introductionmentioning

confidence: 99%

“…The two main drawbacks of this study were ensuring sufficient data to generate a reproducible model and model bias due to the image acquisition process -only one device was used to acquire the images. The authors in [31] developed a CNN model to binary classification between normal and otitis media cases, and also to analyze the presence or absence of perforation on the tympanic membrane achieving an accuracy of 91%.…”

Section: Introductionmentioning

confidence: 99%

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Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

et al. 2020

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In medicine, a misdiagnosis or the absence of specialists can affect the patient's health, leading to unnecessary tests and increasing the costs of healthcare. In particular, the lack of specialists in otolaryngology in third world countries forces patients to seek medical attention from general practitioners, whom might not have enough training and experience for making correct diagnosis in this field. To tackle this problem, we propose and test a computer-aided system based on machine learning models and image processing techniques for otoscopic examination, as a support for a more accurate diagnosis of ear conditions at primary care before specialist referral; in particular, for myringosclerosis, earwax plug, and chronic otitis media. To characterize the tympanic membrane and ear canal for each condition, we implemented three different feature extraction methods: color coherence vector, discrete cosine transform, and filter bank. We also considered three machine learning algorithms: support vector machine (SVM), k-nearest neighbor (k-NN) and decision trees to develop the ear condition predictor model. To conduct the research, our database included 160 images as testing set and 720 images as training and validation sets of 180 patients. We repeatedly trained the learning models using the training dataset and evaluated them using the validation dataset to thus obtain the best feature extraction method and learning model that produce the highest validation accuracy. The results showed that the SVM and k-NN presented the best performance followed by decision trees model. Finally, we performed a classification stage-i.e., diagnosis-using testing data, where the SVM model achieved an average classification accuracy of 93.9%, average sensitivity of 87.8%, average specificity of 95.9%, and average positive predictive value of 87.7%. The results show that this system might be used for general practitioners as a reference to make better decisions in the ear pathologies diagnosis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

et al. 2020

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“…Lee et al 52 developed a convolutional neural network capable of detecting the tympanic membrane side with 97.9% accuracy and presence of perforation with 91.0% accuracy. 52 Improvement of automatic image-processing technologies has become a vital variable in the AI growth equation and will continue to expand due to advancement in radiomics and computer vision ( Table 6 ).…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence Applications in Otology: A State of the Art Review

You

Lin

Mijović

et al. 2020

Otolaryngol.--head neck surg.

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Objective Recent advances in artificial intelligence (AI) are driving innovative new health care solutions. We aim to review the state of the art of AI in otology and provide a discussion of work underway, current limitations, and future directions. Data Sources Two comprehensive databases, MEDLINE and EMBASE, were mined using a directed search strategy to identify all articles that applied AI to otology. Review Methods An initial abstract and title screening was completed. Exclusion criteria included nonavailable abstract and full text, language, and nonrelevance. References of included studies and relevant review articles were cross-checked to identify additional studies. Conclusion The database search identified 1374 articles. Abstract and title screening resulted in full-text retrieval of 96 articles. A total of N = 38 articles were retained. Applications of AI technologies involved the optimization of hearing aid technology (n = 5; 13% of all articles), speech enhancement technologies (n = 4; 11%), diagnosis and management of vestibular disorders (n = 11; 29%), prediction of sensorineural hearing loss outcomes (n = 9; 24%), interpretation of automatic brainstem responses (n = 5; 13%), and imaging modalities and image-processing techniques (n = 4; 10%). Publication counts of the included articles from each decade demonstrated a marked increase in interest in AI in recent years. Implications for Practice This review highlights several applications of AI that otologists and otolaryngologists alike should be aware of given the possibility of implementation in mainstream clinical practice. Although there remain significant ethical and regulatory challenges, AI powered systems offer great potential to shape how healthcare systems of the future operate and clinicians are key stakeholders in this process.

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“…Deep learning studies how the networks emulate human behaviour so as to acquire knowledge or skills, reorganise existing knowledge structures and gradually improve their performance. With the rapid growth of data volume in recent years, deep learning based on deep neural networks has achieved indisputable success [9]. Among these technologies, CNN based models have achieved huge success in a extensive range of tasks.…”

Section: Introductionmentioning

confidence: 99%