A Web-Based Deep Learning Model for Automated Diagnosis of Otoscopic Images

Abstract

Objectives: To develop a multiclass-classifier deep learning model and website for distinguishing tympanic membrane (TM) pathologies based on otoscopic images. Methods: An otoscopic image database developed by utilizing publicly available online images and open databases was assessed by convolutional neural network (CNN) models including ResNet-50, Inception-V3, Inception-Resnet-V2, and MobileNetV2. Training and testing were conducted with a 75:25 breakdown. Area under the curve of receiver operating characteristics (AUC-ROC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to compare different CNN models’ performances in classifying TM images. Results: Our database included 400 images, organized into normal (n\u200a=\u200a196) and abnormal classes (n\u200a=\u200a204), including acute otitis media (n\u200a=\u200a116), otitis externa (n\u200a=\u200a44), chronic suppurative otitis media (n\u200a=\u200a23), and cerumen impaction (n\u200a=\u200a21). For binary classification between normal versus abnormal TM, the best performing model had average AUC-ROC of 0.902 (MobileNetV2), followed by 0.745 (Inception-Resnet-V2), 0.731 (ResNet-50), and 0.636 (Inception-V3). Accuracy ranged between 0.73–0.77, sensitivity 0.72–0.88, specificity 0.58–0.84, PPV 0.68–0.81, and NPV 0.73–0.83. Macro-AUC-ROC for MobileNetV2 based multiclass-classifier was 0.91, with accuracy of 66%. Binary and multiclass-classifier models based on MobileNetV2 were loaded onto a publicly accessible and user-friendly website (https://headneckml.com/tympanic). This allows the readership to upload TM images for real-time predictions using the developed algorithms. Conclusions: Novel CNN algorithms were developed with high AUC-ROCs for differentiating between various TM pathologies. This was further deployed as a proof-of-concept publicly accessible website for real-time predictions.