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Objectives: Various imaging features on optical coherence tomography (OCT) are crucial for identifying and defining disease progression. Establishing a consensus on these imaging features is essential, particularly for training deep learning models for disease classification. This study aims to analyze the inter-rater reliability in labeling the quality and common imaging signatures of retinal OCT scans. Methods: 500 OCT scans obtained from CIRRUS HD-OCT 5000 devices were displayed at 512x1024x128 resolution on a customizable, in-house annotation software. Each patient's eye was represented by 16 random scans. Two masked reviewers independently labeled the quality and specific pathological features of each scan. Evaluated features included overall image quality, presence of fovea, and disease signatures including subretinal fluid (SRF), intraretinal fluid (IRF), drusen, pigment epithelial detachment (PED), and hyperreflective material. The raw percentage agreement and Cohen's kappa (κ) coefficient were used to evaluate concurrence between the two sets of labels. Results: Our analysis revealed κ = 0.60 for the inter-rater reliability of overall scan quality, indicating substantial agreement. In contrast, there was slight agreement in determining the cause of poor image quality (κ = 0.18). The binary determination of presence and absence of retinal disease signatures showed almost complete agreement between reviewers (κ = 0.85). Specific retinal pathologies, such as the foveal location of the scan (0.78), IRF (0.63), drusen (0.73), and PED (0.87), exhibited substantial concordance. However, less agreement was found in identifying SRF (0.52), hyperreflective dots (0.41), and hyperreflective foci (0.33). Conclusions: Our study demonstrates significant inter-rater reliability in labeling the quality and retinal pathologies on OCT scans. While some features show stronger agreement than others, these standardized labels can be utilized to create automated machine learning tools for diagnosing retinal diseases and capturing valuable pathological features in each scan. This standardization will aid in the consistency of medical diagnoses and enhance the accessibility of OCT diagnostic tools. [ABSTRACT FROM AUTHOR]

Eye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. Optical coherence tomography (OCT) imaging provides clinicians with in vivo, cross-sectional views of the retina, enabling the identification of key pathological features. However, manual interpretation of OCT scans is labor-intensive and prone to variability, often leading to diagnostic inconsistencies. To address this, we leveraged the RETFound model, a foundation model pretrained on 1.6 million unlabeled retinal OCT images, to automate the classification of key disease signatures on OCT. We finetuned RETFound and compared its performance with the widely used ResNet-50 model, using single-task and multitask modes. The dataset included 1770 labeled B-scans with various disease features, including subretinal fluid (SRF), intraretinal fluid (IRF), drusen, and pigment epithelial detachment (PED). The performance was evaluated using accuracy and AUC-ROC values, which ranged across models from 0.75 to 0.77 and 0.75 to 0.80, respectively. RETFound models display comparable specificity and sensitivity to ResNet-50 models overall, making it also a promising tool for retinal disease diagnosis. These findings suggest that RETFound may offer improved diagnostic accuracy and interpretability for specific tasks, potentially aiding clinicians in more efficient and reliable OCT image analysis. [ABSTRACT FROM AUTHOR]

Background/Objectives: The aim of this study was to evaluate the choroidal vascular response using optical coherence tomography (OCT) in patients with chronic central serous chorioretinopathy (CSCR) during transient increases in blood pressure. Methods: This observational, case–control study enrolled chronic CSCR patients and age-matched healthy controls. OCT scans of the macula were performed at rest and during hand-grip (HG) isometric exercise. Mean ocular perfusion pressure (MOPP) and subfoveal choroidal thickness (SCT) were measured at baseline and during stress. Quantitative OCT assessment included the bright area (BA, stroma), dark area (DA, vascular lumen), and total choroidal area (CA). The choroidal vascularity index (CVI) was calculated as DA/CA to assess vascular response to stress. A comparative analysis between CSCR patients and controls was conducted. Conclusions: MOPP was significantly higher (p = 0.008) at baseline in CSCR patients and further increased under stress compared to controls. SCT and CA were both significantly higher in CSCR patients than in healthy subjects at rest and under stress (p < 0.001), but no change occurred after HG. A significant decrease in CVI (p = 0.005) was noted in controls under stress, but not in CSCR patients. Additionally, a negative correlation between CVI and MOPP was found in healthy subjects (−0.648; p = 0.043). The study demonstrated a choroidal vasoconstrictive response to stress in healthy subjects, as evidenced by a decrease in CVI, but not in CSCR patients. This suggests that CSCR patients may experience impaired choroidal blood flow regulation, resulting in potentially higher perfusion pressures during stress without compensatory vasoconstriction, potentially affecting the choriocapillaris. [ABSTRACT FROM AUTHOR]