Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration

Abstract

IMPORTANCE Although deep learning (DL) can identify the intermediate or advanced stages of age-related macular degeneration (AMD) as a binary yes or no, stratified gradings using the more granular Age-Related Eye Disease Study (AREDS) 9-step detailed severity scale for AMD provide more precise estimation of 5-year progression to advanced stages. The AREDS 9-step detailed scale's complexity and implementation solely with highly trained fundus photograph graders potentially hampered its clinical use, warranting development and use of an alternate AREDS simple scale, which although valuable, has less predictive ability. OBJECTIVE To describe DL techniques for the AREDS 9-step detailed severity scale for AMD to estimate 5-year risk probability with reasonable accuracy. DESIGN, SETTING, AND PARTICIPANTS This study used data collected from November 13, 1992, to November 30, 2005, from 4613 study participants of the AREDS data set to develop deep convolutional neural networks that were trained to provide detailed automated AMD grading on several AMD severity classification scales, using a multiclass classification setting. Two AMD severity classification problems using criteria based on 4-step (AMD-1, AMD-2, AMD-3, and AMD-4 from classifications developed for AREDS eligibility criteria) and 9-step (from AREDS detailed severity scale) AMD severity scales were investigated. The performance of these algorithms was compared with a contemporary human grader and against a criterion standard (fundus photograph reading center graders) used at the time of AREDS enrollment and follow-up. Three methods for estimating 5-year risk were developed, including one based on DL regression. Data were analyzed from December 1, 2017, through April 15, 2018. MAIN OUTCOMES AND MEASURES Weighted κ scores and mean unsigned errors for estimating 5-year risk probability of progression to advanced AMD. RESULTS This study used 67 401 color fundus images from the 4613 study participants. The weighted κ scores were 0.77 for the 4-step and 0.74 for the 9-step AMD severity scales. The overall mean estimation error for the 5-year risk ranged from 3.5% to 5.3%. CONCLUSIONS AND RELEVANCE These findings suggest that DL AMD grading has, for the 4-step classification evaluation, performance comparable with that of humans and achieves promising results for providing AMD detailed severity grading (9-step classification), which normally requires highly trained graders, and for estimating 5-year risk of progression to advanced AMD. Use of DL has the potential to assist physicians in longitudinal care for individualized, detailed risk assessment as well as clinical studies of disease progression during treatment or as public screening or monitoring worldwide.

Factors in Color Fundus Photographs That Can Be Used by Humans to Determine Sex of Individuals

“…In ophthalmology, AI is now about to enter into the clinical phase for the diagnosis and prognosis of diseases. [1][2][3][4] In the field of AI in ophthalmology, there are some new findings assumed to be not possible before AI. One of the most unexpected findings was the ability of AI to identify the sex of an individual based on the characteristics of the ocular color fundus photographs (CFPs) of the individual.…”

Development and validation of a deep‐learning algorithm for the detection of neovascular age‐related macular degeneration from colour fundus photographs

“…For example, Grassman et al defined 13 classes, one indicating little or no AMD, grades 2 to 9 representing changes associated with early or intermediate AMD and grades 10 to 12 covering late‐stage AMD such as geographic atrophy and neovascular AMD. Greater numbers of classification groups led to lower kappa scores in the study by Burlina et al (2018) (0.77 for four‐step approach and 0.74 in the nine‐step approach) and an overall accuracy of 63% in the study by Grassman et al (2018) . Similarly, this was shown by Burlina et al (2017) who obtained accuracy values of 79.4%, 81.5% and 93.4% for 4‐class, 3‐class and 2‐class classifications, respectively.…”

“…Over the past decade, automated techniques for the assessment of AMD, via feature extraction from small retinal image datasets (<1000), have been reported with variable accuracy. More recent reports provide novel data on the accuracy of deep‐learning systems for the detection of AMD . The majority of these studies have utilized the Age‐Related Eye Disease Study (AREDS) participants to develop and test the accuracy of their DLAs .…”

“…Direct comparison between recent reports relating to AMD detection using DLAs is made difficult due to differing classification criteria. Burlina et al (2018), Grassman et al (2018) and Burlina et al (2017) utilized multi‐step approaches using classifications developed for AREDS. For example, Grassman et al defined 13 classes, one indicating little or no AMD, grades 2 to 9 representing changes associated with early or intermediate AMD and grades 10 to 12 covering late‐stage AMD such as geographic atrophy and neovascular AMD.…”

“…More recent reports provide novel data on the accuracy of deep-learning systems for the detection of AMD. [3][4][5]11,19,20 The majority of these studies have utilized the Age-Related Eye Disease Study (AREDS) participants to develop and test the accuracy of their DLAs. [3][4][5]19,20 The AREDS dataset is useful for training and internally validating DLAs as the images are of high-quality (pupillary dilation ≥5 mm), are collected within research settings and have already been gold standard graded by experts.…”