Department of Ophthalmology, Bucheon St Mary's Hospital, College of Medicine, The Catholic University of Korea, Gyeonggi-do, Republic of Korea.
Department of Ophthalmology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Br J Ophthalmol. 2021 Jun;105(6):856-861. doi: 10.1136/bjophthalmol-2020-316108. Epub 2020 Jul 3.
Automatic identification of pachychoroid maybe used as an adjunctive method to confirm the condition and be of help in treatment for macular diseases. This study investigated the feasibility of classifying pachychoroid disease on ultra-widefield indocyanine green angiography (UWF ICGA) images using an automated machine-learning platform.
Two models were trained with a set including 783 UWF ICGA images of patients with pachychoroid (n=376) and non-pachychoroid (n=349) diseases using the AutoML Vision (Google). Pachychoroid was confirmed using quantitative and qualitative choroidal morphology on multimodal imaging by two retina specialists. Model 1 used the original and Model 2 used images of the left eye horizontally flipped to the orientation of the right eye to increase accuracy by equalising the mirror image of the right eye and left eye. The performances were compared with those of human experts.
In total, 284, 279 and 220 images of central serous chorioretinopathy, polypoidal choroidal vasculopathy and neovascular age-related maculopathy were included. The precision and recall were 87.84% and 87.84% for Model 1 and 89.19% and 89.19% for Model 2, which were comparable to the results of the retinal specialists (90.91% and 95.24%) and superior to those of ophthalmic residents (68.18% and 92.50%).
Auto machine-learning platform can be used in the classification of pachychoroid on UWF ICGA images after careful consideration for pachychoroid definition and limitation of the platform including unstable performance on the medical image.
自动识别肥厚脉络膜可作为一种辅助方法来确认疾病，并有助于黄斑疾病的治疗。本研究利用自动机器学习平台，探讨在超广角吲哚青绿血管造影（UWF ICGA）图像上对肥厚脉络膜疾病进行分类的可行性。
使用 AutoML Vision（谷歌）对包括 783 例肥厚脉络膜（n=376）和非肥厚脉络膜（n=349）疾病患者的 UWF ICGA 图像的数据集，训练了两个模型。通过两名视网膜专家在多模态成像上对脉络膜形态进行定量和定性评估，确认肥厚脉络膜。模型 1 使用原始图像，模型 2 将左眼图像水平翻转到右眼的方向，以通过使右眼和左眼的镜像相等来提高准确性。比较了这些模型与人类专家的表现。
共纳入了 284 例中心性浆液性脉络膜视网膜病变、息肉样脉络膜血管病变和新生血管性年龄相关性黄斑病变的图像。模型 1 的精确率和召回率分别为 87.84%和 87.84%，模型 2 分别为 89.19%和 89.19%，这与视网膜专家的结果（90.91%和 95.24%）相当，优于眼科住院医师的结果（68.18%和 92.50%）。
在仔细考虑肥厚脉络膜定义和平台的局限性（包括在医学图像上性能不稳定）后，自动机器学习平台可用于超广角吲哚青绿血管造影图像上的肥厚脉络膜分类。
