AUTHOR=Xu Fabao , Wan Cheng , Zhao Lanqin , Liu Shaopeng , Hong Jiaming , Xiang Yifan , You Qijing , Zhou Lijun , Li Zhongwen , Gong Songjian , Zhu Yi , Chen Chuan , Zhang Li , Gong Yajun , Li Longhui , Li Cong , Zhang Xiayin , Guo Chong , Lai Kunbei , Huang Chuangxin , Ting Daniel , Lin Haotian , Jin Chenjin 

TITLE=Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2021.649221

DOI=10.3389/fbioe.2021.649221

ISSN=2296-4185

ABSTRACT=To predict visual acuity (VA) and post-therapeutic optical coherence tomography (OCT) images 1, 3 and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC) by artificial intelligence (AI). Real-world clinical and imaging data were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data obtained from ZOC (416 eyes of 401 patients) were used as the training set; the data obtained from XEC (64 eyes of 60 patients) were used as the test set. Six different machine learning algorithms and a blending algorithm were used to predict VA and a pix2pixHD method was adopted to predict post-therapeutic OCT images in patients after laser treatment. The data for VA predictions included clinical features obtained from electronic medical records (20 features) and measured features obtained from fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), and OCT (145 features). The data for OCT predictions included 480 pairs of pre- and post-therapeutic OCT images. The VA and OCT images predicted by AI was compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074-0.098 logMAR (within 4-5 letters), and the root mean square errors (RMSEs) were 0.096-0.127 logMAR (within 5-7 letters) for the 1-, 3- and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5/97) of synthetic OCT images could be accurately identified as synthetic images. The MAEs of central macular thickness (CMT) of synthetic OCT images were 30.15 ± 13.28μm, 22.46 ± 9.71μm for the 1- and 3-month predictions, respectively. This is the first study to apply AI to predict VA and post-therapeutic OCT of patients with CSC. This work establishes a reliable method of predicting prognosis six months in advance; the application of AI has the potential to help reduce patient anxiety and serve as a reference for ophthalmologists when choosing optimal laser treatments.