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Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence

ABSTRACT: Refined understanding of the association of retinal microstructure with current and future (post-treatment) function in chronic central serous chorioretinopathy (cCSC) may help to identify patients that would benefit most from treatment. In this post-hoc analysis of data from the prospective, randomized PLACE trial (NCT01797861), we aimed to determine the accuracy of AI-based inference of retinal function from retinal morphology in cCSC. Longitudinal spectral-domain optical coherence tomography (SD-OCT) data from 57 eyes of 57 patients from baseline, week 6–8 and month 7–8 post-treatment were segmented using deep-learning software. Fundus-controlled perimetry data were aligned to the SD-OCT data to extract layer thickness and reflectivity values for each test point. Point-wise retinal sensitivity could be inferred with a (leave-one-out) cross-validated mean absolute error (MAE) [95% CI] of 2.93 dB [2.40–3.46] (scenario 1) using random forest regression. With addition of patient-specific baseline data (scenario 2), retinal sensitivity at remaining follow-up visits was estimated even more accurately with a MAE of 1.07 dB [1.06–1.08]. In scenario 3, month 7–8 post-treatment retinal sensitivity was predicted from baseline SD-OCT data with a MAE of 3.38 dB [2.82–3.94]. Our study shows that localized retinal sensitivity can be inferred from retinal structure in cCSC using machine-learning. Especially, prediction of month 7–8 post-treatment sensitivity with consideration of the treatment as explanatory variable constitutes an important step toward personalized treatment decisions in cCSC.

SUBMITTER: Pfau M

PROVIDER: S-EPMC8516921 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

Project description:Chronic central serous chorioretinopathy (cCSC) is a multifactorial eye disease characterized by subretinal fluid accumulation that leads to vision loss. Clinically, cCSC is associated with stress, hypercortisolism and corticosteroid use, and is more frequent in males (80%) than in females (20%). Current genetic studies on cCSC have thus far focussed on common variants, but familial occurrence of cCSC also suggests a role for rare variants in the disease susceptibility. Therefore, in this study, we performed exome sequencing of cCSC patients to elucidate the role of rare (protein-altering) variants in the disease. Exome sequencing was performed on 269 cCSC patients and 1,586 controls. Data were processed according to the Genome-Analysis-Toolkit (GATK) best practices. Principal component analysis was performed to check for genetic ancestry and only unrelated subjects of European descent were retained. Burden, SKAT and SKAT-O tests were performed using 2 different grouping criteria. One group included protein-altering variants only, while the other contained synonymous and splice site variants as well. The gene-based analyses were performed using the SKAT R-package correcting for two principal components using two approaches; (1) on the entire cohort correcting for sex and (2) on males and females separately. Additionally, the gene-based associations of genes at previously reported cCSC loci were investigated. After filtering, the dataset contained 263 cCSC patients (208 males [79%]) and 1352 controls (671 males [50%]) carrying 197,915 protein-altering variants in 16,370 genes and 330,689 exonic variants in 18,173 genes. Analysis stratified by sex identified significant associations with the PIGZ (PSKAT?=?9.19?×?10-7 & PSKAT-O?=?2.48?×?10-6), DUOX1 (PSKAT?=?1.03?×?10-6), RSAD1 (PSKAT?=?1.92?×?10-7 & PSKAT-O?=?8.57?×?10-8) and LAMB3 (PBurden?=?1.40?×?10-6 & PSKAT-O?=?1.14?×?10-6) genes in female cCSC patients, after correction for multiple testing. The number of rare variant carriers in these genes was significantly higher in the female cCSC cohort compared to female controls (45,5% vs. 18.5%, P?=?1.92?×?10-6, OR?=?3.67 [95% CI?=?2.09-6.46]). No significant associations were identified in the entire cohort nor in the male patients. In this exome study on cCSC patients, we have identified PIGZ, DUOX1, RSAD1 and LAMB3 as potential new candidate genes for cCSC in females. The sex-specific associations identified here suggest a possible interaction between rare genetic factors and sex for cCSC, but replication of these findings in additional cohorts of cCSC patients is necessary.

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Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence

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