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NEW ARTIFICIAL INTELLIGENCE ANALYSIS FOR PREDICTION OF LONG-TERM VISUAL IMPROVEMENT AFTER EPIRETINAL MEMBRANE SURGERY.
- Source :
-
Retina (Philadelphia, Pa.) [Retina] 2023 Feb 01; Vol. 43 (2), pp. 173-181. - Publication Year :
- 2023
-
Abstract
- Purpose: To predict improvement of best-corrected visual acuity (BCVA) 1 year after pars plana vitrectomy for epiretinal membrane (ERM) using artificial intelligence methods on optical coherence tomography B-scan images.<br />Methods: Four hundred and eleven (411) patients with Stage II ERM were divided in a group improvement (IM) (≥15 ETDRS letters of VA recovery) and a group no improvement (N-IM) (<15 letters) according to 1-year VA improvement after 25-G pars plana vitrectomy with internal limiting membrane peeling. Primary outcome was the creation of a deep learning classifier (DLC) based on optical coherence tomography B-scan images for prediction. Secondary outcome was assessment of the influence of various clinical and imaging predictors on BCVA improvement. Inception-ResNet-V2 was trained using standard augmentation techniques. Testing was performed on an external data set. For secondary outcome, B-scan acquisitions were analyzed by graders both before and after fibrillary change processing enhancement.<br />Results: The overall performance of the DLC showed a sensitivity of 87.3% and a specificity of 86.2%. Regression analysis showed a difference in preoperative images prevalence of ectopic inner foveal layer, foveal detachment, ellipsoid zone interruption, cotton wool sign, unprocessed fibrillary changes (odds ratio = 2.75 [confidence interval: 2.49-2.96]), and processed fibrillary changes (odds ratio = 5.42 [confidence interval: 4.81-6.08]), whereas preoperative BCVA and central macular thickness did not differ between groups.<br />Conclusion: The DLC showed high performances in predicting 1-year visual outcome in ERM surgery patients. Fibrillary changes should also be considered as relevant predictors.
Details
- Language :
- English
- ISSN :
- 1539-2864
- Volume :
- 43
- Issue :
- 2
- Database :
- MEDLINE
- Journal :
- Retina (Philadelphia, Pa.)
- Publication Type :
- Academic Journal
- Accession number :
- 36228144
- Full Text :
- https://doi.org/10.1097/IAE.0000000000003646