Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning

Abstract

We sought to predict whether central serous chorioretinopathy (CSC) will persist after 6 months using multiple optical coherence tomography (OCT) images by deep convolutional neural network (CNN). This was a multicenter, retrospective, cohort study. Multiple OCT images, including B-scan and en face images of retinal thickness (RT), mid-retina, ellipsoid zone (EZ) layer, and choroidal layer, were collected from 832 eyes of 832 CSC patients (593 self-resolving and 239 persistent). Each image set and concatenated set were divided into training (70%), validation (15%), and test (15%) sets. Training and validation were performed using ResNet50 CNN architecture for predicting CSC requiring treatment. Model performance was analyzed using the test set. The accuracy of prediction was 0.8072, 0.9200, 0.6480, and 0.9200 for B-scan, RT, mid-retina, EZ, and choroid modalities, respectively. When image sets with high accuracy were concatenated, the accuracy was 0.9520, 0.8800, and 0.9280 for B-scan + RT, B-scan + EZ, and EZ + RT, respectively. OCT B-scan, RT, and EZ en face images demonstrated good performances for predicting the prognosis of CSC using CNN. The performance improved when these sets were concatenated. The results of this study can serve as a reference for choosing an optimal treatment for CSC patients.

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“…Research by Devarakonda et al [119] and Srivastava et al [121] demonstrates that incorporating choroidal features significantly enhances model performance in classification tasks. Studies by Jee et al [129] and Mirshahi et al [131] exemplify the importance of choroidal features in AI analysis, aligning with our current understanding of choroidal involvement in various diseases.…”

The impact of artificial intelligence on retinal disease management: Vision Academy retinal expert consensus

“…AI could also identify responders and nonresponders to treatment, and predict individual treatment needs [ 44 , 45 ] as well as the prognosis of patients affected by central serous chorioretinopathy, in order to individualize the treatment scheme. New biomarkers may be identified using heatmaps provided by the AI medical device [ 46 ], while other devices may enable us to better understand the role that the same biomarker, such as central retinal thickness or intraretinal fluid, has in different retinal pathologies [ 47 ].…”