Advances in Structural and Functional Retinal Imaging and Biomarkers for Early Detection of Diabetic Retinopathy

Abstract

Diabetic retinopathy (DR), a vision-threatening microvascular complication of diabetes mellitus (DM), is a leading cause of blindness worldwide that requires early detection and intervention. However, diagnosing DR early remains challenging due to the subtle nature of initial pathological changes. This review explores developments in multimodal imaging and functional tests for early DR detection. Where conventional color fundus photography is limited in the field of view and resolution, advanced quantitative analysis of retinal vessel traits such as retinal microvascular caliber, tortuosity, and fractal dimension (FD) can provide additional prognostic value. Optical coherence tomography (OCT) has also emerged as a reliable structural imaging tool for assessing retinal and choroidal neurodegenerative changes, which show potential as early DR biomarkers. Optical coherence tomography angiography (OCTA) enables the evaluation of vascular perfusion and the contours of the foveal avascular zone (FAZ), providing valuable insights into early retinal and choroidal vascular changes. Functional tests, including multifocal electroretinography (mfERG), visual evoked potential (VEP), multifocal pupillographic objective perimetry (mfPOP), microperimetry, and contrast sensitivity (CS), offer complementary data on early functional deficits in DR. More importantly, combining structural and functional imaging data may facilitate earlier detection of DR and targeted management strategies based on disease progression. Artificial intelligence (AI) techniques show promise for automated lesion detection, risk stratification, and biomarker discovery from various imaging data. Additionally, hematological parameters, such as neutrophil-lymphocyte ratio (NLR) and neutrophil extracellular traps (NETs), may be useful in predicting DR risk and progression. Although current methods can detect early DR, there is still a need for further research and development of reliable, cost-effective methods for large-scale screening and monitoring of individuals with DM.

Keywords: combined measures; deep learning; diabetes mellitus; diagnostic tests; early diabetic retinopathy; optical coherence tomography; optical coherence tomography angiography.

Figure 1

CellScope Retina schematic and workflow. (A) Schematic of the optical system. Light from LEDs is directed through a mask and forms an annulus that passes through the peripheral cornea, focusing through the pupil. In propagating through the eye, the light becomes defocused, providing even illumination at the retina. Polarization filters minimize unwanted reflections from anterior ocular surfaces, enabling the smartphone to capture a clear image of the retina. (B) The compact optical system and custom-control electronics fit inside a handheld enclosure. (C) Red LED illumination of 655 nm peak emission is used for focusing on the retina, which is within the spectral range of the iPhone camera but outside the peak photopic response of the eye. (D) A white LED with a broad emission spectrum is flashed for recording images of the retina. LED spectra in (C,D) are from respective datasheets; photopic response is from the CIE 1931 standard [31]; phone response is approximate for a CMOS phone sensor. (E) Smartphone user interface enables (1) patient data capture, (2) preview during focus/alignment with swipe gestures adjusting camera settings, and (3) exam data review with pinch and swipe gestures for browsing stitched image montages. (F) Photos can be uploaded directly from the smartphone to a cloud database allowing remote diagnosis with a web interface. Reproduced with permission from Reference [24].

Figure 2

Ultra-wide-field swept-source optical coherence tomography angiography (SS-OCTA) imaging systems in a normal eye (A) and in a case of proliferative diabetic retinopathy (B). The triangles highlight areas of non-perfusion, while the arrows indicate the presence of retinal neovascularization. (Images courtesy of Dr. Jialiang Duan, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China).

Figure 3

High inter-person variability among normal individuals in the foveal avascular zone (FAZ). (A) A woman with a spherical equivalent of +0.75 diopters and an axial length of 24.01 mm exhibited a FAZ area of 0.070 mm². (B) A man with a spherical equivalent of −3.50 diopters and an axial length of 23.97 mm showcased a notably larger FAZ area, measuring 0.671 mm².