Ge Qianmin, Lin Jinyan, Zhang YeYe, Wei Hong, Kang Min, Zou Jie, Ling Qian, Huang Hui, Xu Sanhua, Chen Xu, Shao Yi
Department of Ophthalmology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China.
School of Science and Center for Theoretical Physics, Hainan University, Haikou, Hainan, 570288, China.
J Imaging Inform Med. 2025 Jan 27. doi: 10.1007/s10278-024-01174-y.
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169. The results of MGD classifications by three ophthalmologists were used to calculate the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, true negative rate (TNR), true positive rate (TPR), and false positive rate (FPR) of the model. The deep learning (DL) was used to build the model to identify the IVCM images. Model accuracy and loss tests showed that the DCNN model had high accuracy, low loss, and no large fluctuations at an epoch of 175, indicating that DenseNet-169 could enable the dichotomization to proceed stably. The accuracy of each classification of the test set was above 90%, which was highly consistent with the ophthalmologists' diagnosis. The precision of the groups in each classification was more than 90%, or even close to 100%, except for the meibomian gland atrophy with obstruction group in the fifth classification. The recall ranged from 0.8728 to 0.9981, and the FPR was low in the screening and classification diagnoses. The application of DCNN can achieve accurate classification and diagnosis of MGD through IVCM images and has great potential during medical procedures.
本研究的目的是构建一个深度卷积神经网络（DCNN）模型，用于基于活体共聚焦显微镜（IVCM）图像诊断睑板腺功能障碍（MGD）并进行分类，评估DCNN模型的性能及其对临床诊断和治疗的辅助意义。我们从三家医院的IVCM数据库中提取了6643张IVCM图像作为DCNN模型的训练集，并从另外两家医院的IVCM数据库中提取了1661张IVCM图像作为测试集，以检验该模型的性能。使用DenseNet-169构建DCNN模型。三位眼科医生对MGD的分类结果用于计算该模型的受试者操作特征曲线下面积（AUROC）、准确率、精确率、召回率、真阴性率（TNR）、真阳性率（TPR）和假阳性率（FPR）。使用深度学习（DL）构建模型以识别IVCM图像。模型准确率和损失测试表明，DCNN模型在第175个训练轮次时具有较高的准确率、较低的损失且无大幅波动，这表明DenseNet-169能够使二分法稳定进行。测试集的各项分类准确率均高于90%，与眼科医生的诊断高度一致。除第五类睑板腺萎缩伴阻塞组外，各分类中各组的精确率均超过90%，甚至接近100%。召回率在0.8728至0.9981之间，在筛查和分类诊断中FPR较低。DCNN的应用能够通过IVCM图像实现对MGD的准确分类和诊断，在医疗过程中具有巨大潜力。
