Effect of manual OCTA segmentation correction to improve image quality and visibility of choroidal neovascularization in AMD.

In this retrospective case series on neovascular age-related macular degeneration (nAMD), we aimed to improve Choroidal Neovascularization (CNV) visualization in Optical Coherence Tomography Angiography (OCTA) scans by addressing segmentation errors. Out of 198 eyes, 73 OCTA scans required manual segmentation correction. We compared uncorrected scans to those with minimal (2 corrections), moderate (10 corrections), and detailed (50 corrections) efforts targeting falsely segmented Bruch's Membrane (BM). Results showed that 55% of corrected OCTAs exhibited improved quality after manual correction. Notably, minimal correction (2 scans) already led to significant improvements, with additional corrections (10 or 50) not further enhancing expert grading. Reduced background noise and improved CNV identification were observed, with the most substantial improvement after two corrections compared to baseline uncorrected images. In conclusion, our approach of correcting segmentation errors effectively enhances image quality in OCTA scans of nAMD. This study demonstrates the efficacy of the method, with 55% of resegmented OCTA images exhibiting enhanced quality, leading to a notable increase in the proportion of high-quality images from 63 to 83%.

ARTIFICIAL INTELLIGENCE FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY-BASED DISEASE ACTIVITY PREDICTION IN AGE-RELATED MACULAR DEGENERATION.

PURPOSE: The authors hypothesize that optical coherence tomography angiography (OCTA)-visualized vascular morphology may be a predictor of choroidal neovascularization status in age-related macular degeneration (AMD). The authors thus evaluated the use of artificial intelligence (AI) to predict different stages of AMD disease based on OCTA en face 2D projections scans. METHODS: Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active, and wet AMD in remission with no signs of activity. Two human experts graded the same test set, and a consensus grading between two experts was used for the prediction of four categories. RESULTS: The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was 0.4286 active choroidal neovascularization, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, and 0.9762, respectively. The overall AI classification prediction was significantly better than the human (odds ratio = 1.95, P = 0.0021). CONCLUSION: These data show that choroidal neovascularization morphology can be used to predict disease activity by AI; longitudinal studies are needed to better understand the evolution of choroidal neovascularization and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.

Retinal tissue and microvasculature loss in COVID-19 infection.

This cross-sectional study aimed to investigate the hypothesis that permanent capillary damage may underlie the long-term COVID-19 sequela by quantifying the retinal vessel integrity. Participants were divided into three subgroups; Normal controls who had not been affected by COVID-19, mild COVID-19 cases who received out-patient care, and severe COVID-19 cases requiring intensive care unit (ICU) admission and respiratory support. Patients with systemic conditions that may affect the retinal vasculature before the diagnosis of COVID-19 infection were excluded. Participants underwent comprehensive ophthalmologic examination and retinal imaging obtained from Spectral-Domain Optical Coherence Tomography (SD-OCT), and vessel density using OCT Angiography. Sixty-one eyes from 31 individuals were studied. Retinal volume was significantly decreased in the outer 3 mm of the macula in the severe COVID-19 group (p = 0.02). Total retinal vessel density was significantly lower in the severe COVID-19 group compared to the normal and mild COVID-19 groups (p = 0.004 and 0.0057, respectively). The intermediate and deep capillary plexuses in the severe COVID-19 group were significantly lower compared to other groups (p < 0.05). Retinal tissue and microvascular loss may be a biomarker of COVID-19 severity. Further monitoring of the retina in COVID-19-recovered patients may help further understand the COVID-19 sequela.

JOINT MOTION CORRECTION AND 3D SEGMENTATION WITH GRAPH-ASSISTED NEURAL NETWORKS FOR RETINAL OCT.

Optical Coherence Tomography (OCT) is a widely used non-invasive high resolution 3D imaging technique for biological tissues and plays an important role in ophthalmology. OCT retinal layer segmentation is a fundamental image processing step for OCT-Angiography projection, and disease analysis. A major problem in retinal imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose neural networks that jointly correct eye motion and retinal layer segmentation utilizing 3D OCT information, so that the segmentation among neighboring B-scans would be consistent. The experimental results show both visual and quantitative improvements by combining motion correction and 3D OCT layer segmentation comparing to conventional and deep-learning based 2D OCT layer segmentation.