Mariotti Cesare, Mangoni Lorenzo, Muzi Alessio, Fella Michele, Mogetta Veronica, Bongiovanni Giacomo, Rizzo Clara, Chhablani Jay, Midena Edoardo, Lupidi Marco
Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche, Ancona, Italy.
Department of Ophthalmology, Humanitas Gradenigo, Turin, Italy.
Eur J Ophthalmol. 2025 Sep;35(5):1542-1553. doi: 10.1177/11206721251337139. Epub 2025 Apr 27.
PurposeThis study investigated the applicability of a validated AI-algorithm for analyzing different retinal biomarkers in eyes affected by epiretinal membranes (ERMs) before and after surgery.MethodsA retrospective study included 40 patients surgically treated for ERMs removal between November 2022 and January 2024. Pars plana vitrectomy with ERM/ILM peeling was performed by a single experienced surgeon. A validated AI algorithm was used to analyze OCT scans, focusing on intraretinal fluid (IRF) and subretinal fluid (SRF) volumes, external limiting membrane (ELM) and ellipsoid zone (EZ) interruption percentages and hyper-reflective foci (HRF) counts.ResultsPostoperative best corrected visual acuity (BCVA) significantly improved ( < 0.01), and central macular thickness (CMT) decreased from 483.61 ± 96.32 to 386.82 ± 94.86 µm ( = 0.001). IRF volume reduced from 0.283 ± 0.39 mm to 0.142 ± 0.27 mm ( = 0.036) particularly in the central 1 mm-circle. SRF, HRF and EZ/ELM interruption percentages exhibited no significant differences ( > 0.05). Significant correlations ( < 0.05) were found between preoperative BCVA and postoperative BCVA ( = 0.45); CMT reduction and postoperative BCVA ( = 0.42), preoperative IRF and Visual Recovery ( = -0.48), ELM and EZ interruption and visual recovery ( = -0.43 and = -0.47 respectively). Multivariate analysis demonstrated that fluid distribution, especially in the central subfield, correlated with BCVA recovery (R2 = 0.38; < 0.05; Pillai's Trace = 0.79).ConclusionThe study highlights AI's potential in quantifying OCT biomarkers in ERMs surgery. The findings suggest that improved BCVA is associated with reduced CMT, IRF, and redistribution of IRF towards the periphery. EZ and ELM integrities remain crucial prognostic factors, emphasizing the importance of the preoperative analysis.
目的
本研究调查了一种经过验证的人工智能算法在分析视网膜前膜（ERM）手术前后受影响眼睛中不同视网膜生物标志物的适用性。
方法
一项回顾性研究纳入了2022年11月至2024年1月期间接受ERM切除术的40例手术患者。由一位经验丰富的外科医生进行玻璃体视网膜切除术联合ERM/内界膜（ILM）剥除术。使用经过验证的人工智能算法分析光学相干断层扫描（OCT），重点关注视网膜内液（IRF）和视网膜下液（SRF）体积、外界膜（ELM）和椭圆体带（EZ）中断百分比以及高反射灶（HRF）计数。
结果
术后最佳矫正视力（BCVA）显著改善（<0.01），中心黄斑厚度（CMT）从483.61±96.32μm降至386.82±94.86μm（=0.001）。IRF体积从0.283±0.39mm降至0.142±0.27mm（=0.036），尤其是在中央1mm圆内。SRF、HRF以及EZ/ELM中断百分比无显著差异（>0.05）。术前BCVA与术后BCVA之间存在显著相关性（<0.05）（=0.45）；CMT降低与术后BCVA之间存在显著相关性（=0.42），术前IRF与视力恢复之间存在显著相关性（= -0.48），ELM和EZ中断与视力恢复之间存在显著相关性（分别为= -0.43和= -0.47）。多因素分析表明，液体分布，尤其是在中央子区域，与BCVA恢复相关（R2 = 0.38；<0.05；Pillai迹= 0.79）。
结论
该研究突出了人工智能在量化ERM手术中OCT生物标志物方面的潜力。研究结果表明，BCVA改善与CMT降低、IRF减少以及IRF向周边重新分布有关。EZ和ELM完整性仍然是关键的预后因素，强调了术前分析的重要性。
