Visual Field Prognosis From Macula and Circumpapillary Spectral Domain Optical Coherence Tomography.

Purpose: To explore the structural-functional loss relationship from optic-nerve-head- and macula-centred spectral-domain (SD) Optical Coherence Tomography (OCT) images in the full spectrum of glaucoma patients using deep-learning methods. Methods: A cohort comprising 5238 unique eyes classified as suspects or diagnosed with glaucoma was considered. All patients underwent ophthalmologic examination consisting of standard automated perimetry (SAP), macular OCT, and peri-papillary OCT on the same day. Deep learning models were trained to estimate G-pattern visual field (VF) mean deviation (MD) and cluster MD using retinal thickness maps from seven layers: retinal nerve fiber layer (RNFL), ganglion cell layer and inner plexiform layer (GCL + IPL), inner nuclear layer and outer plexiform layer (INL + OPL), outer nuclear layer (ONL), photoreceptors and retinal pigmented epithelium (PR + RPE), choriocapillaris and choroidal stroma (CC + CS), total retinal thickness (RT). Results: The best performance on MD prediction is achieved by RNFL, GCL + IPL and RT layers, with R2 scores of 0.37, 0.33, and 0.31, respectively. Combining macular and peri-papillary scans outperforms single modality prediction, achieving an R2 value of 0.48. Cluster MD predictions show promising results, notably in central clusters, reaching an R2 of 0.56. Conclusions: The combination of multiple modalities, such as optic-nerve-head circular B-scans and retinal thickness maps from macular SD-OCT images, improves the performance of MD and cluster MD prediction. Our proposed model demonstrates the highest level of accuracy in predicting MD in the early-to-mid stages of glaucoma. Translational Relevance: Objective measures recorded with SD-OCT can optimize the number of visual field tests and improve individualized glaucoma care by adjusting VF testing frequency based on deep-learning estimates of functional damage.

Analysis of optical coherence tomography biomarker probability detection in central serous chorioretinopathy by using an artificial intelligence-based biomarker detector.

AIM: To adopt a novel artificial intelligence (AI) optical coherence tomography (OCT)-based program to identify the presence of biomarkers associated with central serous chorioretinopathy (CSC) and whether these can differentiate between acute and chronic central serous chorioretinopathy (aCSC and cCSC). METHODS: Multicenter, observational study with a retrospective design enrolling treatment-naïve patients with aCSC and cCSC. The diagnosis of aCSC and cCSC was established with multimodal imaging and for the current study subsequent follow-up visits were also considered. Baseline OCTs were analyzed by an AI-based platform (Discovery® OCT Fluid and Biomarker Detector, RetinAI AG, Switzerland). This software allows to detect several different biomarkers in each single OCT scan, including subretinal fluid (SRF), intraretinal fluid (IRF), hyperreflective foci (HF) and flat irregular pigment epithelium detachment (FIPED). The presence of SRF was considered as a necessary inclusion criterion for performing biomarker analysis and OCT slabs without SRF presence were excluded from the analysis. RESULTS: Overall, 160 eyes of 144 patients with CSC were enrolled, out of which 100 (62.5%) eyes were diagnosed with cCSC and 60 eyes (34.5%) with aCSC. In the OCT slabs showing presence of SRF the presence of biomarkers was found to be clinically relevant (> 50%) for HF and FIPED in aCSC and cCSC. HF had an average percentage of 81% (± 20) in the cCSC group and 81% (± 15) in the aCSC group (p = 0.4295) and FIPED had a mean percentage of 88% (± 18) in cCSC vs. 89% (± 15) in the aCSC (p = 0.3197). CONCLUSION: We demonstrate that HF and FIPED are OCT biomarkers positively associated with CSC when present at baseline. While both HF and FIPED biomarkers could aid in CSC diagnosis, they could not distinguish between aCSC and cCSC at the first visit. AI-assisted biomarker detection shows promise for reducing invasive imaging needs, but further validation through longitudinal studies is needed.

Prediction of chronic central serous chorioretinopathy through combined manual annotation and AI-assisted volume measurement of flat irregular pigment epithelium.

INTRODUCTION: The aim of this study is to investigate the role of an artificial intelligence (AI)-developed OCT program to predict the clinical course of central serous chorioretinopathy (CSC ) based on baseline pigment epithelium detachment (PED) features. METHODS: Single-center, observational study with a retrospective design. Treatment-naïve patients with acute CSC and chronic CSC were recruited and OCTs were analyzed by an AI-developed platform (Discovery OCT Fluid and Biomarker Detector, RetinAI AG, Switzerland), providing automatic detection and volumetric quantification of PEDs. Flat irregular PED presence was annotated manually and afterwards measured by the AI program automatically. RESULTS: 115 eyes of 101 patients with CSC were included, of which 70 were diagnosed with chronic CSC and 45 with acute CSC. It was found that patients with baseline presence of foveal flat PEDs and multiple flat foveal and extrafoveal PEDs had a higher chance of developing chronic form. AI-based volumetric analysis revealed no significant differences between the groups. CONCLUSIONS: While more evidence is needed to confirm the effectiveness of AI-based PED quantitative analysis, this study highlights the significance of identifying flat irregular PEDs at the earliest stage possible in patients with CSC, to optimize patient management and long-term visual outcomes.

BASELINE SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHIC RETINAL LAYER FEATURES IDENTIFIED BY ARTIFICIAL INTELLIGENCE PREDICT THE COURSE OF CENTRAL SEROUS CHORIORETINOPATHY.

PURPOSE: To identify optical coherence tomography (OCT) features to predict the course of central serous chorioretinopathy (CSC) with an artificial intelligence-based program. METHODS: Multicenter, observational study with a retrospective design. Treatment-naïve patients with acute CSC and chronic CSC were enrolled. Baseline OCTs were examined by an artificial intelligence-developed platform (Discovery OCT Fluid and Biomarker Detector, RetinAI AG, Switzerland). Through this platform, automated retinal layer thicknesses and volumes, including intaretinal and subretinal fluid, and pigment epithelium detachment were measured. Baseline OCT features were compared between acute CSC and chronic CSC patients. RESULTS: One hundred and sixty eyes of 144 patients with CSC were enrolled, of which 100 had chronic CSC and 60 acute CSC. Retinal layer analysis of baseline OCT scans showed that the inner nuclear layer, the outer nuclear layer, and the photoreceptor-retinal pigmented epithelium complex were significantly thicker at baseline in eyes with acute CSC in comparison with those with chronic CSC ( P < 0.001). Similarly, choriocapillaris and choroidal stroma and retinal thickness (RT) were thicker in acute CSC than chronic CSC eyes ( P = 0.001). Volume analysis revealed average greater subretinal fluid volumes in the acute CSC group in comparison with chronic CSC ( P = 0.041). CONCLUSION: Optical coherence tomography features may be helpful to predict the clinical course of CSC. The baseline presence of an increased thickness in the outer retinal layers, choriocapillaris and choroidal stroma, and subretinal fluid volume seems to be associated with acute course of the disease.