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

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.

SUBMITTER: Xu F

PROVIDER: S-EPMC8650495 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo evaluate visual acuity (VA) and factors influencing VA using new multimodal imaging-based classification of central serous chorioretinopathy (CSCR).MethodsRetrospective, observational and cross-sectional study on 229 naïve eyes diagnosed as CSCR with available baseline data and multimodal imaging. Each case was classified into (i) simple/complex/atypical; (ii) primary/recurrent/resolved; (iii) persistent or not; (iv) outer retinal atrophy(ORA) present/absent; (v) foveal involvement present/absent; and (vi) macular neovascularization(MNV) present/absent. Best corrected visual acuity (BCVA) was correlated to the classification as well as every parameter of the classification.ResultsMedian BCVA was 0.18 logMAR [95% Confidence Interval (CI)0.16-0.18] with median duration of complaints of one month (95% CI,6.14-13.0 months). Age of the patient (r = -0.24, p = 0.002) and duration of the disease (r = -0.32, p < 0.001) correlated significantly with BCVA. Logistic regression model showed that older age [odds ratio (OR) = 0.96, p = 0.05], female gender (OR = 2.45, p = 0.046), presence of ORA(OR = 0.34, p = 0.012),and foveal involvement(OR = 0.18, p = 0.007) were statistically significantly associated with poorer BCVA. Eyes classified as complex, persistent CSCR, with ORA or foveal involvement demonstrated lower BCVA compared to those with simple, non-persistent CSCR, without ORA or without foveal involvement (p < 0.05). Eyes with complex CSCR (p < 0.001), atypical CSCR(p = 0.025), persistent subretinal fluid (SRF) (p = 0.001) and those with ORA (p < 0.001) demonstrated a trend towards severe visual loss. Prevalence of persistent SRF, recurrent episodes and ORA was significantly higher among eyes with complex CSCR (p < 0.001) while there was no difference in prevalence of resolved cases (p = 0.07), foveal involvement (p = 0.28) and MNV (p = 0.45) between simple and complex cases.ConclusionThere is a strong correlation between VA and foveal involvement and ORA using the new classification. Thus, the objective parameters of the classification can be incorporated in establishing the treatment guidelines for CSCR.

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

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