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

• Published in: Scientific reports 2021-10, Vol.11 (1), p.20446-9, Article 20446
• Main Authors: Pfau, Maximilian, van Dijk, Elon H. C., van Rijssen, Thomas J., Schmitz-Valckenberg, Steffen, Holz, Frank G., Fleckenstein, Monika, Boon, Camiel J. F.
• Format: Article
• Language: English
• Subjects: 692/1807/1482; 692/308/409; 692/308/53/2423; Accuracy; Adult; Artificial Intelligence; Central Serous Chorioretinopathy - diagnostic imaging; Central Serous Chorioretinopathy - physiopathology; Central Serous Chorioretinopathy - therapy; Deep learning; Female; Fundus Oculi; Humanities and Social Sciences; Humans; Lasers; Learning algorithms; Machine learning; Male; Middle Aged; multidisciplinary; Ophthalmology; Patients; Photodynamic therapy; Retina; Retina - diagnostic imaging; Retina - physiology; Science; Science (multidisciplinary); Sensitivity analysis; Tomography, Optical Coherence; Treatment Outcome
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-99977-4

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.