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Importance of OCT-derived biomarkers for the recurrence of central serous chorioretinopathy using statistics and predictive modelling. | LitMetric

AI Article Synopsis

  • * The study analyzed 5211 OCT scans from 344 eyes of 255 patients with CSCR, identifying 178 recurrent and 109 non-recurrent cases, using advanced AI techniques to extract relevant parameters.
  • * Nine predictive biomarkers for CSCR recurrence were found, including age, various fluid types, and specific retinal layer thicknesses, which could enhance automatic detection and improve clinical outcomes in the future.

Article Abstract

Central serous chorioretinopathy (CSCR) is a retinal disease characterised by the accumulation of subretinal fluid, which often resolves spontaneously in acute cases. However, approximately one-third of patients experience recurrences that may cause severe and irreversible vision. This study aimed to identify parameters derived from optical coherence tomography (OCT) that are associated with CSCR recurrence. Our dataset included 5211 OCT scans from 344 eyes of 255 patients diagnosed with CSCR. 178 eyes were identified as recurrent, 109 as non-recurrent, and 57 were excluded. We extracted parameters using artificial intelligence algorithms based on U-Nets, convolutional kernels, and morphological operators. We applied inferential statistics to evaluate differences between the recurrent and non-recurrent groups, and we used a logistic regression predictive model, reporting the coefficients as a measure of biomarker importance. We identified nine predictive biomarkers for CSCR recurrence: age, intraretinal fluid, subretinal fluid, pigment epithelial detachments, choroidal vascularity index, integrity of photoreceptors and retinal pigment epithelium layer, choriocapillaris and choroidal stroma thickness, and thinning of the outer nuclear layer, and of the inner nuclear layer combined with the outer plexiform layer. These results could enable future developments in the automatic detection of CSCR recurrence, paving the way for translational medical applications.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471761PMC
http://dx.doi.org/10.1038/s41598-024-75275-7DOI Listing

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