Deep Learning Model and its Application for the Diagnosis of Exudative Pharyngitis

• Published in: Healthcare informatics research Vol. 30; no. 1; pp. 42 - 48
• Main Authors: Chng, Seo Yi, Tern, Paul Jie Wen, Kan, Matthew Rui Xian, Cheng, Lionel Tim-Ee
• Format: Journal Article
• Language: English
• Published: Korea (South) Korean Society of Medical Informatics 01.01.2024; The Korean Society of Medical Informatics; 대한의료정보학회
• Subjects: artificial intelligence; deep learning; diagnosis; Original; pharyngitis; telemedicine; 예방의학; Diagnosis; Pharyngitis; Telemedicine; Artificial Intelligence; Deep Learning
• ISSN: 2093-369X; 2093-3681; 2093-369X
• DOI: 10.4258/hir.2024.30.1.42

Objectives: Telemedicine is firmly established in the healthcare landscape of many countries. Acute respiratory infections are the most common reason for telemedicine consultations. A throat examination is important for diagnosing bacterial pharyngitis, but this is challenging for doctors during a telemedicine consultation. A solution could be for patients to upload images of their throat to a web application. This study aimed to develop a deep learning model for the automated diagnosis of exudative pharyngitis. Thereafter, the model will be deployed online.Methods: We used 343 throat images (139 with exudative pharyngitis and 204 without pharyngitis) in the study. ImageDataGenerator was used to augment the training data. The convolutional neural network models of MobileNetV3, ResNet50, and EfficientNetB0 were implemented to train the dataset, with hyperparameter tuning.Results: All three models were trained successfully; with successive epochs, the loss and training loss decreased, and accuracy and training accuracy increased. The EfficientNetB0 model achieved the highest accuracy (95.5%), compared to MobileNetV3 (82.1%) and ResNet50 (88.1%). The EfficientNetB0 model also achieved high precision (1.00), recall (0.89) and F1-score (0.94).Conclusions: We trained a deep learning model based on EfficientNetB0 that can diagnose exudative pharyngitis. Our model was able to achieve the highest accuracy, at 95.5%, out of all previous studies that used machine learning for the diagnosis of exudative pharyngitis. We have deployed the model on a web application that can be used to augment the doctor’s diagnosis of exudative pharyngitis.