Quantitative assessment of retinal fluid in neovascular age-related macular degeneration under anti-VEGF therapy.
Gregor Sebastian ReiterUrsula Schmidt-ErfurthPublished in: Therapeutic advances in ophthalmology (2022)
The retinal world has been revolutionized by optical coherence tomography (OCT) and anti-vascular endothelial growth factor (VEGF) therapy. The numbers of intravitreal injections are on a constant rise and management in neovascular age-related macular degeneration (nAMD) is mainly driven by the qualitative assessment of macular fluid as detected on OCT scans. The presence of macular fluid, particularly subretinal fluid (SRF) and intraretinal fluid (IRF), has been used to trigger re-treatments in clinical trials and the real world. However, large discrepancies can be found between the evaluations of different readers or experts and especially small amounts of macular fluid might be missed during this process. Pixel-wise detection of macular fluid uses an entire OCT volume to calculate exact volumes of retinal fluid. While manual annotations of such pixel-wise fluid detection are unfeasible in a clinical setting, artificial intelligence (AI) is able to overcome this hurdle by providing real-time results of macular fluid in different retinal compartments. Quantitative fluid assessments have been used for various post hoc analyses of randomized controlled trials, providing novel insights into anti-VEGF treatment regimens. Nonetheless, the application of AI-algorithms in a prospective patient care setting is still limited. In this review, we discuss the use of quantitative fluid assessment in nAMD during anti-VEGF therapy and provide an outlook to novel forms of patient care with the support of AI quantifications.
Keyphrases
- optical coherence tomography
- age related macular degeneration
- diabetic retinopathy
- vascular endothelial growth factor
- artificial intelligence
- clinical trial
- optic nerve
- endothelial cells
- machine learning
- magnetic resonance imaging
- computed tomography
- high resolution
- systematic review
- big data
- randomized controlled trial
- quantum dots
- magnetic resonance
- bone marrow
- stem cells
- ultrasound guided