糖尿病视网膜病变(DR),视网膜静脉阻塞(RVO),年龄相关性黄斑变性(AMD)构成了重大的全球健康挑战,常导致视力障碍和失明。自动检测这些条件至关重要,特别是在服务不足的农村地区,获得眼科服务的机会有限。尽管人工智能取得了显著进步,尤其是卷积神经网络(CNN),它们的复杂性会使解释变得困难。在这项研究中,我们收集了一个数据集,该数据集包含从接受荧光素眼底血管造影(FFA)检查的8110例患者中获得的15,089张彩色眼底照片(CFP).主要目标是构建将CNN与注意力机制合并的集成模型。这些模型是为分层多标签分类任务设计的,专注于DR的检测,RVO,AMD,和其他眼底条件。此外,我们的方法扩展到DR的详细分类,RVO,和AMD根据各自的子类。我们采用了一种方法,该方法需要将从FFA结果获得的诊断信息转换为CFP。我们的研究重点是评估模型仅基于CFP实现精确诊断的能力。值得注意的是,我们的模型展示了不同眼底状况的改善,ConvNeXt-base+注意模型因其卓越的性能而脱颖而出。ConvNeXt-base+注意力模型取得了显著的指标,包括0.943的受试者工作特征曲线下面积(AUC)、0.870的相关F1评分和0.778的Cohenκ用于DR检测。对于RVO,AUC为0.960,F1评分为0.854,Cohenκ为0.819。此外,在AMD检测中,该模型的AUC为0.959,F1评分为0.727,Cohen'sκ为0.686.令人印象深刻的是,该模型展示了对RVO和AMD进行亚分类的熟练程度,展示了值得称赞的灵敏度和特异性。此外,我们的模型通过可视化眼底图像上的注意力权重来增强可解释性,帮助识别疾病发现。这些结果强调了我们的模型在推进DR检测方面的重大影响,RVO,和AMD,提供改善患者预后和积极影响医疗保健前景的潜力。
Diabetic retinopathy (DR), retinal vein occlusion (RVO), and age-related macular degeneration (AMD) pose significant global health challenges, often resulting in vision impairment and blindness. Automatic detection of these conditions is crucial, particularly in underserved rural areas with limited access to ophthalmic services. Despite remarkable advancements in artificial intelligence, especially convolutional neural networks (CNNs), their complexity can make interpretation difficult. In this study, we curated a dataset consisting of 15,089 color fundus photographs (CFPs) obtained from 8110 patients who underwent fundus fluorescein angiography (FFA) examination. The primary objective was to construct integrated models that merge CNNs with an attention mechanism. These models were designed for a hierarchical multilabel classification task, focusing on the detection of DR, RVO, AMD, and other fundus conditions. Furthermore, our approach extended to the detailed classification of DR, RVO, and AMD according to their respective subclasses. We employed a methodology that entails the translation of diagnostic information obtained from FFA results into CFPs. Our investigation focused on evaluating the models\' ability to achieve precise diagnoses solely based on CFPs. Remarkably, our models showcased improvements across diverse fundus conditions, with the ConvNeXt-base + attention model standing out for its exceptional performance. The ConvNeXt-base + attention model achieved remarkable metrics, including an area under the receiver operating characteristic curve (AUC) of 0.943, a referable F1 score of 0.870, and a Cohen\'s kappa of 0.778 for DR detection. For RVO, it attained an AUC of 0.960, a referable F1 score of 0.854, and a Cohen\'s kappa of 0.819. Furthermore, in AMD detection, the model achieved an AUC of 0.959, an F1 score of 0.727, and a Cohen\'s kappa of 0.686. Impressively, the model demonstrated proficiency in subclassifying RVO and AMD, showcasing commendable sensitivity and specificity. Moreover, our models enhanced interpretability by visualizing attention weights on fundus images, aiding in the identification of disease findings. These outcomes underscore the substantial impact of our models in advancing the detection of DR, RVO, and AMD, offering the potential for improved patient outcomes and positively influencing the healthcare landscape.