Despite major advances in pharmacotherapy and interventional procedures, coronary artery disease (CAD) remains a principal cause of morbidity and mortality worldwide. Invasive coronary imaging along with the computation of hemodynamic forces, primarily endothelial shear stress and plaque structural stress, have enabled a comprehensive identification of atherosclerotic plaque components, providing a unique insight into the understanding of plaque vulnerability and progression, which may help guide patient treatment. However, the invasive-only approach to CAD has failed to show high predictive value. Meanwhile, it is becoming increasingly evident that along with the “vulnerable plaque”, the presence of a “vulnerable patient” state is also necessary to precipitate an acute coronary thrombotic event. Non-invasive imaging techniques have also evolved, providing new opportunities for the identification of high-risk plaques, the study of atherosclerosis in asymptomatic individuals, and general population screening. Additionally, risk stratification scores, circulating biomarkers, immunology, and genetics also complete the armamentarium of a broader “vulnerable plaque and patient” concept approach. In the current review article, the invasive and non-invasive modalities used for the detection of high-risk plaques in patients with CAD are summarized and critically appraised. The challenges of the vulnerable plaque concept are also discussed, highlighting the need to shift towards a more interdisciplinary approach that can identify the “vulnerable plaque” in a “vulnerable patient”.

This study aims to evaluate the inner plexiform layer (IPL) microstructure and its changes with aging using commercial spectral-domain OCT macular scans of healthy individuals with a semiautomated segmentation program. Cross-sectional study conducted at the Athens Vision Eye Institute from January to July 2024. The study included 92 healthy participants. OCT images were captured with the Optovue Avanti SD-OCT and processed using ImageJ to measure thickness and analyze the hyperreflective and hyporeflective bands within the IPL. Information about signal intensity, microstructure, and contrast between these sublayers was obtained. Statistical analyses, including Spearman correlation and linear regression, assessed relationships between age and IPL extracted features. Intraeye and intereye repeatability were evaluated using paired samples t tests combined with bootstrap analyses. The primary outcomes measured were signal intensity of the IPL, contrast between its hyperreflective and hyporeflective bands, and the percentage of IPL with identifiable sublayers. The secondary outcomes included inner retinal thickness measurements, including the IPL, nerve fiber layer (NFL), and ganglion cell complex (GCC). The IPL exhibited a multilayered structure with 5 sublayers, 3 hyperreflective and 2 hyporeflective, arranged in an alternating pattern. Aging was associated with higher signal intensity from hyporeflective bands and minimal changes in hyperreflective bands, resulting in an overall reduced contrast between the 5 sublayers. Older participants showed a lower percentage of IPL with identifiable sublayers, along with a lower contrast variance within the IPL. Aging also correlated with reduced inner retinal thickness, including the IPL, NFL, and GCC, with a stronger association for the IPL. Inner plexiform layer analysis exhibited high intraeye and intereye repeatability, with significant correlations and nonsignificant mean differences observed in most key parameters. Analysis of the IPL and its sublayers is both feasible and reproducible using commercially available OCT along with a semiautomated segmentation program. Our findings indicate that the IPL microstructure changes with aging. A comprehensive evaluation of the IPL could serve as a valuable biomarker for early diagnosis and monitoring of diseases affecting synaptic health in this layer. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Objectives: To validate a deep learning-based algorithm for automated detection, segmentation, and quantitative assessment of retinal fluid on optical coherence tomography (OCT) across multiple retinal diseases. Methods: In this retrospective validation study, spectral-domain OCT volume scans from eyes with neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), and macular telangiectasia type 2 (MacTel) were analyzed. Automated segmentation was performed using the fluid analyzer on Skopix by NetraMind Innovations, a deep learning-based framework trained to identify intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), and subretinal hyperreflective material (SHRM). Automated segmentations were reviewed and manually corrected by two masked experienced retina graders, with discrepancies adjudicated by a senior retina specialist. Scan-level detection performance was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC), and Gwet’s first-order agreement coefficient (AC1). Spatial segmentation agreement across all B-scans was assessed using the Dice coefficient. Quantitative agreement between automated and manually corrected fluid volumes was evaluated using correlation and Bland–Altman analyses. Inter-grader reliability was assessed using the intraclass correlation coefficient (ICC). Results: A total of 98 OCT volumes comprising 31 nAMD, 25 DME, and 42 MacTel eyes were included. In nAMD, IRF detection demonstrated a sensitivity of 81.8%, specificity of 83.0%, and AUC of 0.876, while SRF demonstrated a sensitivity of 81.4%, specificity of 96.7%, and AUC of 0.901. In DME, sensitivity, specificity, and AUC were 99.6%, 97.3%, and 0.989 for IRF and 98.4%, 96.4%, and 0.978 for SRF, respectively. In MacTel, IRF demonstrated a sensitivity of 87.3%, specificity of 95.1%, and AUC of 0.927, while SRF demonstrated a sensitivity of 86.8%, specificity of 98.4%, and AUC of 0.929. Segmentation agreement between automated and manually corrected annotations was high across disease groups, with Dice coefficients of 0.829 and 0.922 for IRF and SRF in nAMD, 0.858 and 0.941 in DME, and 0.908 and 0.97 in MacTel, respectively. Automated fluid volumes demonstrated strong correlations with manually corrected measurements, with minimal systematic bias on Bland–Altman analysis. Conclusions: The automated segmentation algorithm demonstrated strong validation performance for the detection and quantitative assessment of retinal fluid across nAMD, DME, and MacTel. High segmentation agreement, particularly for IRF and SRF, and strong volumetric agreement with expert-corrected measurements support the potential use of automated OCT fluid quantification in clinical practice and research.