Wongchaisuwat Nida, Wang Jie, White Elizabeth S, Hwang Thomas S, Jia Yali, Bailey Steven T
Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon; Department of Ophthalmology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon.
Ophthalmol Retina. 2025 Apr;9(4):378-385. doi: 10.1016/j.oret.2024.10.017. Epub 2024 Oct 24.
To test the diagnostic performance of an artificial intelligence algorithm for detecting and segmenting macular neovascularization (MNV) with OCT and OCT angiography (OCTA) in eyes with macular edema from various diagnoses.
Prospective cross-sectional study.
Study participants with macular edema due to either treatment-naïve exudative age-related macular degeneration (AMD), diabetic macular edema (DME), or retinal vein occlusion (RVO).
Study participants were imaged with macular 3 × 3-mm and 6 × 6-mm spectral-domain OCTA. Eyes with exudative AMD were required to have MNV in the central 3 × 3-mm area. A previously developed hybrid multitask convolutional neural network for MNV detection (aiMNV), and segmentation was applied to all images, regardless of image quality.
Sensitivity, specificity, positive predictive value, and negative predictive value of detecting MNV and intersection over union (IoU) score and F1 score for segmentation.
Of 114 eyes from 112 study participants, 56 eyes had MNV due to exudative AMD and 58 eyes with macular edema due to either DME or RVO. The 3 × 3-mm OCTA scans with aiMNV detected MNV with 96.4% sensitivity, 98.3% specificity, 98.2% positive predictive value, and 96.6% negative predictive value. For segmentation, the average IoU score was 0.947, and the F1 score was 0.973. The 6 × 6-mm scans performed well; however, sensitivity for MNV detection was lower than 3 × 3-mm scans due to lower scan sampling density.
This novel aiMNV algorithm can accurately detect and segment MNV in eyes with exudative AMD from a control group of eyes that present with macular edema from either DME or RVO. Higher scan sampling density improved the aiMNV sensitivity for MNV detection.
FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
测试一种人工智能算法在检测和分割各种诊断所致黄斑水肿眼中黄斑新生血管（MNV）方面的诊断性能，使用光学相干断层扫描（OCT）和光学相干断层扫描血管造影（OCTA）。
前瞻性横断面研究。
因初治渗出性年龄相关性黄斑变性（AMD）、糖尿病性黄斑水肿（DME）或视网膜静脉阻塞（RVO）导致黄斑水肿的研究参与者。
对研究参与者进行黄斑3×3毫米和6×6毫米光谱域OCTA成像。渗出性AMD患者的眼睛要求在中央3×3毫米区域有MNV。将先前开发的用于MNV检测（aiMNV）的混合多任务卷积神经网络应用于所有图像，无论图像质量如何。
检测MNV的灵敏度、特异度、阳性预测值和阴性预测值，以及分割的交并比（IoU）分数和F1分数。
在112名研究参与者的114只眼中，56只眼因渗出性AMD有MNV，58只眼因DME或RVO有黄斑水肿。使用aiMNV的3×3毫米OCTA扫描检测MNV的灵敏度为96.4%，特异度为98.3%，阳性预测值为98.2%，阴性预测值为96.6%。对于分割，平均IoU分数为0.947，F1分数为0.973。6×6毫米扫描表现良好；然而，由于扫描采样密度较低，MNV检测的灵敏度低于3×3毫米扫描。
这种新型aiMNV算法可以准确地在渗出性AMD眼中检测和分割MNV，这些眼睛来自于因DME或RVO出现黄斑水肿的对照组眼睛。更高的扫描采样密度提高了aiMNV检测MNV的灵敏度。
在本文末尾的脚注和披露中可能会发现专有或商业披露信息。
