Chng Seo Yi, Tern Paul Jie Wen, Kan Matthew Rui Xian, Cheng Lionel Tim-Ee
Department of Paediatrics, National University of Singapore, Singapore.
Department of Cardiology, National Heart Centre, Singapore.
Healthc Inform Res. 2024 Jan;30(1):42-48. doi: 10.4258/hir.2024.30.1.42. Epub 2024 Jan 31.
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.
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.
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).
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.
远程医疗在许多国家的医疗保健领域已牢固确立。急性呼吸道感染是远程医疗咨询最常见的原因。咽喉检查对于诊断细菌性咽炎很重要，但在远程医疗咨询过程中对医生来说具有挑战性。一种解决方案可能是让患者将其咽喉图像上传到网络应用程序。本研究旨在开发一种用于渗出性咽炎自动诊断的深度学习模型。此后，该模型将在线部署。
我们在研究中使用了343张咽喉图像（139张有渗出性咽炎，204张无咽炎）。使用ImageDataGenerator对训练数据进行增强。实施了MobileNetV3、ResNet50和EfficientNetB0的卷积神经网络模型来训练数据集，并进行超参数调整。
所有三个模型均成功训练；随着轮次的增加，损失和训练损失降低，准确率和训练准确率提高。与MobileNetV3（82.1%）和ResNet50（88.1%）相比，EfficientNetB0模型达到了最高准确率（95.5%）。EfficientNetB0模型还实现了高精度（1.00）、召回率（0.89）和F1分数（0.94）。
我们基于EfficientNetB0训练了一种能够诊断渗出性咽炎的深度学习模型。在所有先前使用机器学习诊断渗出性咽炎的研究中，我们的模型能够达到最高准确率，为95.5%。我们已将该模型部署到一个网络应用程序上，可用于辅助医生对渗出性咽炎的诊断。
