Le Boité Hugo, Bonnin Sophie, Gallardo Mathias, Lamard Mathieu, Couturier Aude, Quellec Gwenolé
Université Paris Cité, Paris, France.
Ophthalmology Department, AP-HP, Hôpital Lariboisière, 75010, Paris, France.
Sci Rep. 2025 Aug 18;15(1):30225. doi: 10.1038/s41598-025-15712-3.
We developed an automated framework for segmenting low-quality and non-perfusion areas in widefield OCTA images to obtain two key metrics useful for diabetic retinopathy (DR) monitoring: the retinal non-perfusion index (NPI) and foveal avascular zone (FAZ) area. Using 170 images from 88 patients in the EVIRED cohort, we trained two models: Q-NET, which segments low-quality areas, and NPA-NET, which detects non-perfusion areas and the FAZ. Their combined outputs created a 4-class map to calculate NPI and FAZ area. Ground truth segmentations were established by a single expert (for non-perfusion and FAZ areas) or a consensus of four annotators (for low-quality areas). NPA-NET and Q-NET, tested on 29 images, achieved strong segmentation performances (Dice coefficients of 0.714 (low-quality), 0.781 (non-perfusion), and 0.879 (FAZ)). Some inter-annotator variability was found (mean Dice: 0.85 for low-quality, 0.683 for non-perfusion areas). Predictive accuracy for NPI and FAZ area was high, with R² coefficients of 0.97 and 0.63, respectively, with minimal underestimation and no overestimation. This AI tool provides reliable biomarkers for DR monitoring, supporting treatment decisions and medical decision-making by automatically analyzing OCTA images, and could be integrated into clinical practice.
我们开发了一个自动化框架，用于分割广角光学相干断层扫描血管造影（OCTA）图像中的低质量区域和无灌注区域，以获得两个对糖尿病视网膜病变（DR）监测有用的关键指标：视网膜无灌注指数（NPI）和黄斑无血管区（FAZ）面积。我们使用了EVIRED队列中88名患者的170张图像，训练了两个模型：用于分割低质量区域的Q-NET和用于检测无灌注区域及FAZ的NPA-NET。它们的联合输出创建了一个4类地图，以计算NPI和FAZ面积。通过一名专家（针对无灌注和FAZ区域）或四名注释者的共识（针对低质量区域）建立了地面真值分割。在29张图像上进行测试时，NPA-NET和Q-NET取得了强大的分割性能（低质量区域的Dice系数为0.714，无灌注区域为0.781，FAZ为0.879）。发现注释者之间存在一些变异性（低质量区域的平均Dice系数：0.85，无灌注区域为0.683）。NPI和FAZ面积的预测准确性很高，R²系数分别为0.97和0.63，低估最小且无高估。这种人工智能工具为DR监测提供了可靠的生物标志物，通过自动分析OCTA图像支持治疗决策和医疗决策制定，并且可以整合到临床实践中。
