NEW ARTIFICIAL INTELLIGENCE ANALYSIS FOR PREDICTION OF LONG-TERM VISUAL IMPROVEMENT AFTER EPIRETINAL MEMBRANE SURGERY. Issue 2 (February 2023)

• Record Type: Journal Article
• Authors: Crincoli, Emanuele; Savastano, Maria Cristina; Savastano, Alfonso; Caporossi, Tomaso; Bacherini, Daniela; Miere, Alexandra; Gambini, Gloria; De Vico, Umberto; Baldascino, Antonio; Minnella, Angelo Maria; Scupola, Andrea; DAmico, Guglielmo; Molle, Fernando; Bernardinelli, Patrizio; De Filippis, Alessandro; Kilian, Raphael; Rizzo, Clara; Ripa, Matteo; Ferrara, Silvia; Scampoli, Alessandra; Brando, Davide; Molle, Andrea; Souied, Eric H.; Rizzo, Stanislao
• Is Part Of: Retina. Volume 43:Issue 2(2023)
• Page Start: 173
• Page End: 181
• Publication Date: 2023-02
• Subjects: artificial intelligence -- deep learning -- epiretinal membrane -- fibrillary changes -- optical coherence tomography; Retina -- Diseases -- Periodicals; Retinal Diseases; Vitreous Body; 617.735
• DOI: 10.1097/IAE.0000000000003646
• Languages: English
• ISSNs: 0275-004X

Abstract

Abstract : Our multicentric prospective interventional study led to the creation of a deep learning classifier accurately predicting visual improvement > 15 ETDRS letters 1 year after epiretinal membrane peeling. Image processing enhanced a significantly higher prevalence of fibrillary changes in preoperative images of patients with <15-letter improvement at 1 year postoperatively. 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.