Suciu Corina Iuliana, Marginean Anca, Suciu Vlad-Ioan, Muntean George Adrian, Nicoară Simona Delia
Department of Ophthalmology, "Iuliu Haţieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.
Department of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Diagnostics (Basel). 2023 Dec 28;14(1):76. doi: 10.3390/diagnostics14010076.
(1) Background: Diabetes mellitus (DM) is a growing challenge, both for patients and physicians, in order to control the impact on health and prevent complications. Millions of patients with diabetes require medical attention, which generates problems regarding the limited time for screening but also addressability difficulties for consultation and management. As a result, screening programs for vision-threatening complications due to DM have to be more efficient in the future in order to cope with such a great healthcare burden. Diabetic macular edema (DME) is a severe complication of DM that can be prevented if it is timely screened with the help of optical coherence tomography (OCT) devices. Newly developing state-of-the-art artificial intelligence (AI) algorithms can assist physicians in analyzing large datasets and flag potential risks. By using AI algorithms in order to process OCT images of large populations, the screening capacity and speed can be increased so that patients can be timely treated. This quick response gives the physicians a chance to intervene and prevent disability. (2) Methods: This study evaluated ConvNeXt and EfficientNet architectures in correctly identifying DME patterns on real-life OCT images for screening purposes. (3) Results: Firstly, we obtained models that differentiate between diabetic retinopathy (DR) and healthy scans with an accuracy of 0.98. Secondly, we obtained a model that can indicate the presence of edema, detachment of the subfoveolar neurosensory retina, and hyperreflective foci (HF) without using pixel level annotation. Lastly, we analyzed the extent to which the pretrained weights on natural images "understand" OCT scans. (4) Conclusions: Pretrained networks such as ConvNeXt or EfficientNet correctly identify features relevant to the differentiation between healthy retinas and DR, even though they were pretrained on natural images. Another important aspect of our research is that the differentiation between biomarkers and their localization can be obtained even without pixel-level annotation. The "three biomarkers model" is able to identify obvious subfoveal neurosensory detachments, retinal edema, and hyperreflective foci, as well as very small subfoveal detachments. In conclusion, our study points out the possible usefulness of AI-assisted diagnosis of DME for lowering healthcare costs, increasing the quality of life of patients with diabetes, and reducing the waiting time until an appropriate ophthalmological consultation and treatment can be performed.
（1）背景：糖尿病（DM）对患者和医生来说都是一个日益严峻的挑战，目的是控制其对健康的影响并预防并发症。数以百万计的糖尿病患者需要医疗关注，这不仅在筛查时间有限方面产生问题，而且在咨询和管理的可及性方面也存在困难。因此，未来糖尿病所致威胁视力并发症的筛查项目必须更加高效，以应对如此巨大的医疗负担。糖尿病性黄斑水肿（DME）是糖尿病的一种严重并发症，如果借助光学相干断层扫描（OCT）设备及时进行筛查，是可以预防的。新开发的先进人工智能（AI）算法可以帮助医生分析大量数据集并标记潜在风险。通过使用AI算法处理大量人群的OCT图像，可以提高筛查能力和速度，以便患者能够得到及时治疗。这种快速反应使医生有机会进行干预并预防残疾。（2）方法：本研究评估了ConvNeXt和EfficientNet架构在为筛查目的而正确识别实际OCT图像上的DME模式方面的情况。（3）结果：首先，我们获得了区分糖尿病视网膜病变（DR）和健康扫描图像的模型，准确率为0.98。其次，我们获得了一个模型，该模型可以在不使用像素级注释的情况下指示水肿、黄斑下神经感觉视网膜脱离和高反射灶（HF）的存在。最后，我们分析了自然图像上的预训练权重在多大程度上“理解”OCT扫描。（4）结论：诸如ConvNeXt或EfficientNet等预训练网络能够正确识别与健康视网膜和DR之间区分相关的特征，尽管它们是在自然图像上进行预训练的。我们研究的另一个重要方面是，即使不进行像素级注释，也能够实现生物标志物与其定位之间的区分。“三种生物标志物模型”能够识别明显的黄斑下神经感觉脱离、视网膜水肿和高反射灶，以及非常小的黄斑下脱离。总之，我们的研究指出了AI辅助诊断DME在降低医疗成本、提高糖尿病患者生活质量以及减少直至能够进行适当眼科咨询和治疗的等待时间方面的潜在有用性。
