Maunz Andreas, Benmansour Fethallah, Li Yvonna, Albrecht Thomas, Zhang Yan-Ping, Arcadu Filippo, Zheng Yalin, Madhusudhan Savita, Sahni Jayashree
Pharma Research and Early Development, Roche Innovation Center, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.
Department of Eye and Vision Science, University of Liverpool, Liverpool L7 8XP, UK.
J Pers Med. 2021 Jun 8;11(6):524. doi: 10.3390/jpm11060524.
To evaluate the performance of a machine-learning (ML) algorithm to detect and classify choroidal neovascularization (CNV), secondary to age-related macular degeneration (AMD) on spectral-domain optical coherence tomography (SD-OCT) images.
Baseline fluorescein angiography (FA) and SD-OCT images from 1037 treatment-naive study eyes and 531 fellow eyes, without advanced AMD from the phase 3 HARBOR trial (NCT00891735), were used to develop, train, and cross-validate an ML pipeline combining deep-learning-based segmentation of SD-OCT B-scans and CNV classification, based on features derived from the segmentations, in a five-fold setting. FA classification of the CNV phenotypes from HARBOR was used for generating the ground truth for model development. SD-OCT scans from the phase 2 AVENUE trial (NCT02484690) were used to externally validate the ML model.
The ML algorithm discriminated CNV absence from CNV presence, with a very high accuracy (area under the receiver operating characteristic [AUROC] = 0.99), and classified occult versus predominantly classic CNV types, per FA assessment, with a high accuracy (AUROC = 0.91) on HARBOR SD-OCT images. Minimally classic CNV was discriminated with significantly lower performance. Occult and predominantly classic CNV types could be discriminated with AUROC = 0.88 on baseline SD-OCT images of 165 study eyes, with CNV from AVENUE.
Our ML model was able to detect CNV presence and CNV subtypes on SD-OCT images with high accuracy in patients with neovascular AMD.
评估一种机器学习（ML）算法在光谱域光学相干断层扫描（SD-OCT）图像上检测和分类年龄相关性黄斑变性（AMD）继发的脉络膜新生血管（CNV）的性能。
来自3期HARBOR试验（NCT00891735）的1037只未经治疗的研究眼和531只对侧眼的基线荧光素血管造影（FA）和SD-OCT图像，这些眼睛无晚期AMD，用于开发、训练和交叉验证一个ML流程，该流程结合基于深度学习的SD-OCT B扫描分割和基于分割衍生特征的CNV分类，采用五折设置。来自HARBOR的CNV表型的FA分类用于为模型开发生成真值。来自2期AVENUE试验（NCT02484690）的SD-OCT扫描用于对ML模型进行外部验证。
ML算法能够以非常高的准确率（受试者操作特征曲线下面积[AUROC]=0.99）区分有无CNV，并根据FA评估，在HARBOR SD-OCT图像上以高准确率（AUROC=0.91）对隐匿性与主要为典型性CNV类型进行分类。对最小经典型CNV的区分性能显著较低。在165只研究眼的基线SD-OCT图像上，隐匿性和主要为经典型CNV类型的区分AUROC=0.88，这些研究眼来自AVENUE试验且存在CNV。
我们的ML模型能够在新生血管性AMD患者的SD-OCT图像上高精度地检测CNV的存在和CNV亚型。
