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.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.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.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.
Crincoli, E., Savastano, M. C., Savastano, A., Caporossi, T., Bacherini, D., Miere, A., Gambini, G., De Vico, U., Baldascino, A., Minnella, A. M., Scupola, A., Damico, G., Molle, F., Bernardinelli, P., De Filippis, A., Kilian, R., Rizzo, C., Ripa, M., Ferrara, S., Scampoli, A., Brando, D., Molle, A., Souied, E. H., Rizzo, S., NEW ARTIFICIAL INTELLIGENCE ANALYSIS for PREDICTION of LONG-TERM VISUAL IMPROVEMENT after EPIRETINAL MEMBRANE SURGERY, <<RETINA>>, 2023; 43 (2): 173-181. [doi:10.1097/IAE.0000000000003646] [https://hdl.handle.net/10807/232090]
NEW ARTIFICIAL INTELLIGENCE ANALYSIS for PREDICTION of LONG-TERM VISUAL IMPROVEMENT after EPIRETINAL MEMBRANE SURGERY
Crincoli, Emanuele;Savastano, Maria Cristina;Savastano, Alfonso;Caporossi, Tomaso;Gambini, Gloria;De Vico, Umberto;Baldascino, Antonio;Minnella, Angelo Maria;Scupola, Andrea;Molle, Fernando;Bernardinelli, Patrizio;De Filippis, Alessandro;Ripa, Matteo;Ferrara, Silvia;Scampoli, Alessandra;Brando, Davide;Molle, Andrea;Rizzo, Stanislao
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.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.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.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.
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