Optical coherence tomography angiography in neovascular age-related macular degeneration: comprehensive review of advancements and future perspective

被引:0
作者
Tillmann, Anne [1 ]
Turgut, Ferhat [1 ,2 ,3 ]
Munk, Marion R. [1 ,4 ,5 ]
机构
[1] Augenarzt Praxisgemeinschaft Gutblick AG, Pfaffikon, Switzerland
[2] Stadtspital Zurich, Dept Ophthalmol, Zurich, Switzerland
[3] Semmelwe Univ, Dept Ophthalmol, Budapest, Hungary
[4] Univ Bern, Bern Univ Hosp, Dept Ophthalmol, Inselspital, CH-3010 Bern, Switzerland
[5] Northwestern Univ, Feinberg Sch Med, Dept Ophthalmol, Chicago, IL 60208 USA
来源
关键词
QUIESCENT CHOROIDAL NEOVASCULARIZATION; OCT-ANGIOGRAPHY; TYPE-2; NEOVASCULARIZATION; QUANTITATIVE-ANALYSIS; VESSEL SEGMENTATION; ARTIFACTS; SECONDARY; THERAPY; AMD; STANDARDIZATION;
D O I
10.1038/s41433-024-03295-8
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Optical coherence tomography angiography (OCTA) holds promise in enhancing the care of various retinal vascular diseases, including neovascular age-related macular degeneration (nAMD). Given nAMD's vascular nature and the distinct vasculature of macular neovascularization (MNV), detailed analysis is expected to gain significance. Research in artificial intelligence (AI) indicates that en-face OCTA views may offer superior predictive capabilities than spectral domain optical coherence tomography (SD-OCT) images, highlighting the necessity to identify key vascular parameters. Analyzing vasculature could facilitate distinguishing MNV subtypes and refining diagnosis. Future studies correlating OCTA parameters with clinical data might prompt a revised classification system. However, the combined utilization of qualitative and quantitative OCTA biomarkers to enhance the accuracy of diagnosing disease activity remains underdeveloped. Discrepancies persist regarding the optimal biomarker for indicating an active lesion, warranting comprehensive prospective studies for validation. AI holds potential in extracting valuable insights from the vast datasets within OCTA, enabling researchers and clinicians to fully exploit its OCTA imaging capabilities. Nevertheless, challenges pertaining to data quantity and quality pose significant obstacles to AI advancement in this field. As OCTA gains traction in clinical practice and data volume increases, AI-driven analysis is expected to further augment diagnostic capabilities.
引用
收藏
页码:835 / 844
页数:10
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