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, indocyanine green angiography, 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 were compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074–0.098 logMAR (within four to five letters), and the root mean square errors were 0.096–0.127 logMAR (within five to seven letters) for the 1-, 3-, and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5 of 97) of synthetic OCT images could be accurately identified as synthetic images. The MAEs of central macular thickness of synthetic OCT images were 30.15 ± 13.28 μm and 22.46 ± 9.71 μm 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 6 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.
Highlights
Central serous chorioretinopathy (CSC) is an idiopathic ophthalmopathy in which the neurosensory retina is often detached in the central macular region due to serous leakage from defects of the retinal pigment epithelium (RPE), causing damage to central vision (Saperstein et al, 2002; Piccolino et al, 2005; Maruko et al, 2010; Wong et al, 2016)
Previous studies have shown that artificial intelligence (AI) can be applied to predict post-therapeutic visual acuity (VA) and optical coherence tomography (OCT) images based on automatic analysis of OCT imaging in patients with age-related macular degeneration (AMD) (Bogunovic et al, 2017; Rohm et al, 2018; Liu et al, 2020)
To generate and evaluate individualized posttherapeutic OCT images that could predict the short-term response of laser therapies based on pre-therapeutic images using a generative adversarial network (GAN), a total of 416 pre- and post-therapeutic OCT images of patients with CSC obtained from Zhongshan Ophthalmic Center (ZOC) were included in the training set, whereas 64 pre-therapeutic OCT images obtained from Xiamen Eye Center (XEC) were included in the test set retrospectively, and the corresponding posttherapeutic OCT images were used to evaluate the synthetic images
Summary
Central serous chorioretinopathy (CSC) is an idiopathic ophthalmopathy in which the neurosensory retina is often detached in the central macular region due to serous leakage from defects of the retinal pigment epithelium (RPE), causing damage to central vision (Saperstein et al, 2002; Piccolino et al, 2005; Maruko et al, 2010; Wong et al, 2016). CSC ranks fourth in incidence after age-related macular degeneration (AMD), diabetic retinopathy (DR), and retinal vein occlusion (RVO), and it is second only to AMD as the presumed cause of subretinal neovascularization (Wang et al, 2008; Manayath et al, 2018). Because of the increasing number of patients with DR and the lack of a sufficient number of ophthalmologists to perform screenings, efforts have been made to detect early forms of DR using AI. These AI programs show high efficiency and sensitivity (Oliveira et al, 2011; Haritoglou et al, 2014; Sim et al, 2015). Previous studies have shown that AI can be applied to predict post-therapeutic visual acuity (VA) and OCT images based on automatic analysis of OCT imaging in patients with AMD (Bogunovic et al, 2017; Rohm et al, 2018; Liu et al, 2020)
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