视神经萎缩的鉴别诊断可能具有挑战性,并且需要昂贵的费用,耗时的辅助测试来确定原因。虽然Leber的遗传性视神经病变(LHON)和视神经炎(ON)都是视神经萎缩的临床重要原因,两者在普通人群中相对罕见,导致获取大型成像数据集的局限性。因此,这项研究旨在开发一种基于小数据集的深度学习(DL)模型,该模型可以仅使用眼底摄影来区分视盘萎缩的原因。我们回顾了120只正常眼的眼底照片,30眼(15例)的基因证实的LHON,和30只眼(26例)。将图像分为训练数据集和测试数据集,并用于ResNet-18的模型训练。为了可视化视网膜照片中与疾病预测高度相关的关键区域,梯度加权类激活图(Grad-CAM)用于生成图像级注意力热图并增强DL系统的可解释性。在正常的3类分类中,LHON,ON,正常受试者工作特征曲线下面积(AUROC)为1.0,0.988forLHON,和0.990为ON,清楚地将每个类与其他类区分开来,总精度为0.93。具体来说,区分正常病例和疾病病例时,精度,召回,F1成绩完美,为1.0。此外,在LHON与其他条件的区别中,从别人,在LHON和ON之间,我们一直观察到精度,召回,F1评分为0.8。保持模型性能,直到图像的像素值只有10%,被Grad-CAM认定为重要,被保存下来,其余的都是蒙面的,其次是再培训和评估。
The differential diagnosis for optic atrophy can be challenging and requires expensive, time-consuming ancillary testing to determine the cause. While Leber\'s hereditary optic neuropathy (LHON) and optic neuritis (ON) are both clinically significant causes for optic atrophy, both relatively rare in the general population, contributing to limitations in obtaining large imaging datasets. This study therefore aims to develop a deep learning (DL) model based on small datasets that could distinguish the cause of optic disc atrophy using only fundus photography. We retrospectively reviewed fundus photographs of 120 normal eyes, 30 eyes (15 patients) with genetically-confirmed LHON, and 30 eyes (26 patients) with ON. Images were split into a training dataset and a test dataset and used for model training with ResNet-18. To visualize the critical regions in retinal photographs that are highly associated with disease prediction, Gradient-Weighted Class Activation Map (Grad-CAM) was used to generate image-level attention heat maps and to enhance the interpretability of the DL system. In the 3-class classification of normal, LHON, and ON, the area under the receiver operating characteristic curve (AUROC) was 1.0 for normal, 0.988 for LHON, and 0.990 for ON, clearly differentiating each class from the others with an overall total accuracy of 0.93. Specifically, when distinguishing between normal and disease cases, the precision, recall, and F1 scores were perfect at 1.0. Furthermore, in the differentiation of LHON from other conditions, ON from others, and between LHON and ON, we consistently observed precision, recall, and F1 scores of 0.8. The model performance was maintained until only 10% of the pixel values of the image, identified as important by Grad-CAM, were preserved and the rest were masked, followed by retraining and evaluation.