Efüzyonlu Otitis Media Olan Hastalarda Derin Öğrenme İle İletim Tipi İK Tespiti

A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images

Junbo Zeng, MD1; Weibiao Kang, MD2; Suijun Chen, MD1; et al

Key Points

Question How effective are deep learning (DL) models in predicting conductive hearing loss (CHL) from otoscopic images of ears with otitis media with effusion?

Findings In this diagnostic/prognostic study including 2790 otoscopic images from 1239 patients, a DL model was developed using otoscopic images and validated in multiple centers. The DL model predicted CHL with an accuracy of 81%.

Meaning This research to predict CHL using a DL model revealed great potential for the quick and accurate prediction of degree of conductive hearing loss, and offers opportunities to reduce patient waiting time and avoid unnecessary referral.

Abstract

Importance Otitis media with effusion (OME) is one of the most common causes of acquired conductive hearing loss (CHL). Persistent hearing loss is associated with poor childhood speech and language development and other adverse consequence. However, to obtain accurate and reliable hearing thresholds largely requires a high degree of cooperation from the patients.

Objective To predict CHL from otoscopic images using deep learning (DL) techniques and a logistic regression model based on tympanic membrane features.

Design, Setting, and Participants A retrospective diagnostic/prognostic study was conducted using 2790 otoscopic images obtained from multiple centers between January 2015 and November 2020. Participants were aged between 4 and 89 years. Of 1239 participants, there were 209 ears from children and adolescents (aged 4-18 years [16.87%]), 804 ears from adults (aged 18-60 years [64.89%]), and 226 ears from older people (aged >60 years, [18.24%]). Overall, 679 ears (54.8%) were from men. The 2790 otoscopic images were randomly assigned into a training set (2232 [80%]), and validation set (558 [20%]). The DL model was developed to predict an average air-bone gap greater than 10 dB. A logistic regression model was also developed based on otoscopic features.

Main Outcomes and Measures The performance of the DL model in predicting CHL was measured using the area under the receiver operating curve (AUC), accuracy, and F1 score (a measure of the quality of a classifier, which is the harmonic mean of precision and recall; a higher F1 score means better performance). In addition, these evaluation parameters were compared to results obtained from the logistic regression model and predictions made by three otologists.

Results The performance of the DL model in predicting CHL showed the AUC of 0.74, accuracy of 81%, and F1 score of 0.89. This was better than the results from the logistic regression model (ie, AUC of 0.60, accuracy of 76%, and F1 score of 0.82), and much improved on the performance of the 3 otologists; accuracy of 16%, 30%, 39%, and F1 scores of 0.09, 0.18, and 0.25, respectively. Furthermore, the DL model took 2.5 seconds to predict from 205 otoscopic images, whereas the 3 otologists spent 633 seconds, 645 seconds, and 692 seconds, respectively.

Conclusions and Relevance The model in this diagnostic/prognostic study provided greater accuracy in prediction of CHL in ears with OME than those obtained from the logistic regression model and otologists. This indicates great potential for the use of artificial intelligence tools to facilitate CHL evaluation when CHL is unable to be measured.

Makalenin tamamına erişmek için lütfen: https://jamanetwork.com/journals/jamaotolaryngology/article-abstract/2792681 adresini ziyaret ediniz.