RESUMO
INTRODUCTION: This study aimed to evaluate intraretinal microvascular abnormalities (IRMA) in eyes with advanced nonproliferative diabetic retinopathy (NPDR) using multimodal approach in co-located areas focusing on central retina (up to 50°) and to look at possible correlations between IRMA and other structural changes, like ischemia and presence of microaneurysms. METHODS: The RICHARD study (NCT05112445) included 60 eyes from 60 patients with type 2 diabetes with moderate-severe NPDR, diabetic retinopathy severity levels 43, 47, and 53 (DRSS). IRMA were defined as capillary tortuosity covering a minimum circular area of 300 µm (calculated to correspond to the Early Treatment Diabetic Retinopathy Study standard photo 8A) and were identified using multimodal imaging with distinct fields of view (FoV): color fundus photography (CFP) using a Topcon TRC-50DX camera (Topcon Medical Systems, Japan), Optos California ultra wide field fundus fluorescein angiography (UWF-FFA) (Optos plc, UK), and swept-source optical coherence tomography angiography (SS-OCTA) (PLEX® Elite 9000, ZEISS, USA). Different areas of the retina were examined: central macula (up to 20°) and posterior pole (between 20° and 50°). RESULTS: Multimodal imaging was used to identify IRMA in co-located areas (FoV < 50°) including UWF-FFA, CFP, and SS-OCTA. In eyes with DRSS levels 47 and 53, IRMA were identified in both areas of the retina, while in eyes with DRSS level 43, IRMA were detected only outside of the central macula (FoV > 20°). Our results show that when evaluating the presence of IRMA (FoV < 50°), UWF-FFA detected 203 IRMA, SS-OCTA detected 133 IRMA, and CFP detected 104 IRMA. Our results also show that the presence of IRMA was positively associated with presence of microaneurysms. CONCLUSIONS: Identification of IRMA in eyes with advanced NPDR is better achieved by UWF-FFA than CFP and SS-OCTA. A statistically significant correlation was found between the presence of IRMA and the increase in number of microaneurysms. TRIAL REGISTRATION: ClinicalTrials.gov, identifier NCT05112445.
RESUMO
The automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results.