Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review

Abstract

PurposeTo assess the quality of optical coherence tomography (OCT) grading algorithms for retinal biomarkers of age-related macular degeneration (AMD).MethodsFollowing a systematic review of the literature data on detection and quantification of AMD retinal biomarkers by available algorithms were extracted and descriptively synthesized. Algorithm quality was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies 2 checklist with a focus on accuracy against established reference standards and risk of bias.ResultsThirty five studies reporting computer-aided diagnosis (CAD) tools for qualitative analysis or algorithms for quantitative analysis were identified. Compared with manual assessment in reference standards correlation coefficients ranged from 0.54 to 0.97 for drusen, 0.80 to 0.98 for geographic atrophy (GA), and 0.30 to 0.98 for intra- or subretinal fluid and pigment epithelial detachment (PED) detection by automated algorithms. CAD tools achieved area under the curve (AUC) values of 0.94 to 0.99, sensitivity of 0.90 to 1.00, and specificity of 0.89 to 0.92.ConclusionsAutomated analysis of AMD biomarkers on OCT is promising. However, most of the algorithm validation was performed in preselected patients, exhibiting the targeted biomarker only. In addition, type and quality of reported algorithm validation varied substantially.Translational RelevanceThe development of algorithms for combined, simultaneous analysis of multiple AMD biomarkers including AMD staging and the agreement on standardized validation procedures would be of considerable translational value for the clinician and the clinical researcher.

Semi‐automated quantification of geographic atrophy with blue‐light autofluorescence and spectral‐domain optical coherence tomography: a comparison between the region finder and the advanced retinal pigment epithelium tool in the clinical setting

“…Computer‐assisted GA analysis is a promising approach, but the number of commercially available software is very limited and there is a need for quality and performance assessments in ‘real‐world’ settings, as pointed out in a recently published review (Wintergerst et al. ).…”

The European Eye Epidemiology spectral‐domain optical coherence tomography classification of macular diseases for epidemiological studies

“…For instance, in AMD automated detection of drusen, geographic atrophy or subretinal fluid has been developed (Wintergerst et al. ). The potential benefits are to facilitate access and allow an early detection.…”

“…Recently, many papers on automated image analysis in several retinal diseases have been published (Bogunovic et al 2017;Karri et al 2017;Khalid et al 2017). For instance, in AMD automated detection of drusen, geographic atrophy or subretinal fluid has been developed (Wintergerst et al 2017). The potential benefits are to facilitate access and allow an early detection.…”

Segmentation of retinal low-cost optical coherence tomography images using deep learning

“…In recent years, algorithms for the segmentation of OCT-based biomarkers have been described in several works. 6 Among the state-of-the-art methods, the number of deep-learning-based techniques such as convolutional neural networks (CNNs) is constantly increasing. Deep learning methods for segmentation of the total retinal segmentation, 7 retinal fluids like IRC, SRF 8 or PED 9 have been proposed.…”