Hafner Michael, Asani Ben, Eckardt Franziska, Siedlecki Jakob, Schworm Benedikt, Priglinger Siegfried G, Schiefelbein Johannes
Department of Ophthalmology, LMU University Hospital, LMU Munich, Mathildenstraße 8, 80336, Munich, Germany.
Ophthalmol Ther. 2025 May;14(5):1025-1037. doi: 10.1007/s40123-025-01125-y. Epub 2025 Mar 25.
Artificial intelligence (AI)-driven biomarker segmentation offers an objective approach to assessing neovascular age-related macular degeneration (nAMD). In addition, faricimab, a bispecific VEGF and Ang-2 inhibitor, presents new potential in disease management. This study applies an AI-based segmentation algorithm to quantify key optical coherence tomography (OCT) biomarkers and assess the short-term efficacy of intravitreal faricimab in treatment-naïve patients.
This retrospective analysis includes 40 eyes from 38 treatment-naïve patients with nAMD treated with faricimab at LMU University Hospital Munich between January 2023 and September 2024. Patients received 4-monthly intravitreal injections. Biomarkers of disease activity, including central retinal thickness (CRT), intraretinal fluid (IRF), subretinal fluid (SRF), subretinal hyperreflective material (SHRM) and fibrovascular pigment epithelium detachment (fvPED), were quantified using a deep learning-based semantic segmentation algorithm. Best-corrected visual acuity (BCVA) and OCT imaging data were analyzed at baseline (mo0) and after 1 (mo1), 2 (mo2) and 3 months (mo3).
AI-driven analysis revealed significant reductions in key biomarkers. CRT decreased from 433.6 (IQR: 306.6) µm at mo0 to 241.5 (IQR: 130.8) µm at mo3 (p < 0.0001). IRF and SRF volumes were reduced by > 99% from mo0 to mo3 (both p < 0.0001). BCVA improved from 0.60 (IQR: 0.30) logMAR at mo0 to 0.40 (IQR: 0.33) logMAR at mo3 (p < 0.0001). Correlation analysis identified IRF and SHRM reductions as the strongest predictors of visual improvement.
This study demonstrates the potential of AI-assisted biomarker analysis for precise disease monitoring in nAMD. Faricimab significantly reduced disease activity biomarkers and improved visual acuity in treatment-naïve patients, reinforcing its efficacy in early disease control. Future studies should explore long-term outcomes and further integrate AI-driven biomarker evaluation in clinical practice.
人工智能（AI）驱动的生物标志物分割为评估新生血管性年龄相关性黄斑变性（nAMD）提供了一种客观方法。此外，双特异性血管内皮生长因子（VEGF）和血管生成素-2（Ang-2）抑制剂faricimab在疾病管理方面展现了新的潜力。本研究应用基于AI的分割算法来量化关键光学相干断层扫描（OCT）生物标志物，并评估玻璃体内注射faricimab对初治患者的短期疗效。
本回顾性分析纳入了2023年1月至2024年9月期间在慕尼黑路德维希-马克西米利安大学医院接受faricimab治疗的38例初治nAMD患者的40只眼。患者每4个月接受一次玻璃体内注射。使用基于深度学习的语义分割算法对疾病活动生物标志物进行量化，包括中心视网膜厚度（CRT）、视网膜内液（IRF）、视网膜下液（SRF）、视网膜下高反射物质（SHRM）和纤维血管性色素上皮脱离（fvPED）。在基线（第0个月）以及第1个月（mo1）、第2个月（mo2）和第3个月（mo3）时分析最佳矫正视力（BCVA）和OCT成像数据。
AI驱动的分析显示关键生物标志物显著降低。CRT从第0个月时的433.6（四分位间距：306.6）µm降至第3个月时的241.5（四分位间距：130.8）µm（p<0.0001）。从第0个月到第3个月，IRF和SRF体积减少了>99%（两者p<0.0001）。BCVA从第0个月时的0.60（四分位间距：0.30）logMAR提高到第3个月时的0.40（四分位间距：0.33）logMAR（p<0.0001）。相关性分析确定IRF和SHRM的减少是视力改善的最强预测因素。
本研究证明了AI辅助生物标志物分析在nAMD精确疾病监测中的潜力。Faricimab显著降低了初治患者的疾病活动生物标志物并提高了视力，强化了其在疾病早期控制中的疗效。未来的研究应探索长期结果，并进一步将AI驱动的生物标志物评估整合到临床实践中。
