Development of an Automatic Diagnostic Algorithm for Pediatric Otitis Media

Tran, Thi Thao; Fang, Te Yung; Pham, Van Truong; Lin, Chen; Wang, Pa Chun; Lo, Men Tzung

doi:10.1097/mao.0000000000001897

Cited by 37 publications

(47 citation statements)

References 22 publications

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“…One of the important challenges of AI in otolaryngology is high‐quality and large quantities of patient data collection 13 . Based on a large amount of otoendoscopic images, several studies have developed the feature‐extractions‐based algorithms for automatic diagnosis of OM 15–18 . Recently, deep learning methods, which reduce the need for manual feature extraction are used to automated diagnosis of ear diseases 19,20 .…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Classification of Pediatric Otitis Media

Lin²,

et al. 2020

The Laryngoscope

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Objectives/Hypothesis To create a new strategy for monitoring pediatric otitis media (OM), we developed a brief, reliable, and objective method for automated classification using convolutional neural networks (CNNs) with images from otoscope. Study Design Prospective study. Methods An otoscopic image classifier for pediatric OM was built upon the idea of deep learning and transfer learning using the two most widely used CNN architectures named Xception and MobileNet‐V2. Otoscopic images, including acute otitis media (AOM), otitis media with effusion (OME), and normal ears were obtained from our institution. Among qualified otoendoscopic images, 10,703 images were used for training, and 1,500 images were used for testing. In addition, 102 images captured by smartphone with WI‐FI connected otoscope were used as a prospective test set to evaluate the model for home screening and monitoring. Results For all diagnoses combined in the test set, the Xception model and the MobileNet‐V2 model had similar overall accuracies of 97.45% (95% CI 96.81%–97.94%) and 95.72% (95% CI 95.12%–96.16%). The overall accuracies of two models with smartphone images were 90.66% (95% CI 90.21%–90.98%) and 88.56% (95% CI 87.86%–90.05%). The class activation map results showed that the extracted features of smartphone images were the same as those of otoendoscopic images. Conclusions We have developed deep learning algorithms for the successfully automated classification of pediatric AOM and OME with otoscopic images. With a smartphone‐enabled wireless otoscope, artificial intelligence may assist parents in early detection and continuous monitoring at home to decrease the visit frequencies. Level of Evidence NA Laryngoscope, 131:E2344–E2351, 2021

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Classification of Pediatric Otitis Media

Lin²,

et al. 2020

The Laryngoscope

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“…A diagnosis support system analyzing the color of the TM has been reported to achieve an accuracy of 73.11% [13]. Another diagnostic system using features that mimic an otologist's decision-making process for otitis media has been reported to achieve an accuracy of 89.9% [14], while an automated feature extraction and classification system for AOM and OME has achieved an accuracy of 91.41% [12].…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have applied deep learning for making diagnoses based on otoscopic images. Accuracies from 73.11% to 91.41% have been reported when applying deep learning to distinguish acute otitis media (AOM) and otitis media with effusion (OME) [12][13][14]. At the same time, chronic otitis media (COM) is challenging due to variability of the images and difficulty of locating lesions.…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of the Tympanic Membrane Using a Convolutional Neural Network

2019

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Precise evaluation of the tympanic membrane (TM) is required for accurate diagnosis of middle ear diseases. However, making an accurate assessment is sometimes difficult. Artificial intelligence is often employed for image processing, especially for performing high level analysis such as image classification, segmentation and matching. In particular, convolutional neural networks (CNNs) are increasingly used in medical image recognition. This study demonstrates the usefulness and reliability of CNNs in recognizing the side and perforation of TMs in medical images. CNN was constructed with typically six layers. After random assignment of the available images to the training, validation and test sets, training was performed. The accuracy of the CNN model was consequently evaluated using a new dataset. A class activation map (CAM) was used to evaluate feature extraction. The CNN model accuracy of detecting the TM side in the test dataset was 97.9%, whereas that of detecting the presence of perforation was 91.0%. The side of the TM and the presence of a perforation affect the activation sites. The results show that CNNs can be a useful tool for classifying TM lesions and identifying TM sides. Further research is required to consider real-time analysis and to improve classification accuracy.

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“…Moreover, research in the area of 3D tissue printing and bioengineering is likely to open a whole new arena of pediatric airway surgical reconstruction . Advances in genomics, precision medicine, deep machine learning and artificial intelligence are likely to influence the practice of pediatric otolaryngology from otitis media diagnosis to hearing loss treatment . Integration of simulation into surgical training and competency assessment will predictably increase over time .…”

Section: Futurementioning

confidence: 99%

“…18 Advances in genomics, precision medicine, deep machine learning and artificial intelligence are likely to influence the practice of pediatric otolaryngology from otitis media diagnosis to hearing loss treatment. 19,20 Integration of simulation into surgical training and competency assessment will predictably increase over time. 21,22 In order to stay at the forefront of these advances and to meet the demand of increasingly complex health systems, pediatric otolaryngology practices will likely continue to increasingly move towards hospital-based multidisciplinary team medicine models.…”

Section: Futurementioning

confidence: 99%