Swept-source OCTA quantification of capillary closure predicts ETDRS severity staging of NPDR.

PURPOSE: To test whether a single or composite set of parameters evaluated with optical coherence tomography angiography (OCTA), representing retinal capillary closure, can predict non-proliferative diabetic retinopathy (NPDR) staging according to the gold standard ETDRS grading scheme. METHODS: 105 patients with diabetes, either without retinopathy or with different degrees of retinopathy (NPDR up to ETDRS grade 53), were prospectively evaluated using swept-source OCTA (SS-OCTA, PlexElite, Carl Zeiss Meditec) with 15×9 mm and 3×3 mm angiography protocols. Seven-field photographs of the fundus were obtained for ETDRS staging. Eyes from age-matched healthy subjects were also imaged as control. RESULTS: In eyes of patients with type 2 diabetes without retinopathy or ETDRS levels 20 and 35, retinal capillary closure was in the macular area, with predominant alterations in the parafoveal retinal circulation (inner ring). Retinal capillary closure in ETDRS stages 43-53 becomes predominant in the retinal midperiphery with vessel density average values of 25.2±7.9 (p=0.001) in ETDRS 43 and 23.5±3.4 (p=0.001) in ETDRS 47-53, when evaluating extended areas of 15×9 protocol. Combination of acquisition protocols 3×3 mm and 15×9 mm, using SS-OCTA, allows discrimination between eyes with mild NPDR (ETDRS 10, 20, 35) and eyes with moderate-to-severe NPDR (ETDRS grades 43-53). CONCLUSIONS: Retinal capillary closure, quantified by SS-OCTA, can identify NPDR severity progression. It is located mainly in the perifoveal retinal capillary circulation in the initial stages of NPDR, whereas the retinal midperiphery is predominantly affected in moderate-to-severe NPDR.

Imaging and artificial intelligence for progression of age-related macular degeneration.

Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications.