DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs

Abstract

Purpose: In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score.Design: DeepSeeNet, a deep learning model, was developed to classify patients automatically by the AREDS Simplified Severity Scale (score 0-5) using bilateral CFP.Participants: DeepSeeNet was trained on 58,402 and tested on 900 images from the longitudinal follow-up of 4,549 participants from AREDS. Gold standard labels were obtained using reading center grades.Methods: DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale. Main Outcome Measures: Overall accuracy, specificity, sensitivity, Cohen's kappa, and area under the curve (AUC). The performance of DeepSeeNet was compared with that of retinal specialists. Results: DeepSeeNet performed better on patient-based classification (accuracy = 0.671; kappa = 0.558) than retinal specialists (accuracy = 0.599; kappa = 0.467) with high AUC in the detection of large drusen (0.94), pigmentary abnormalities (0.93), and late AMD (0.97). DeepSeeNet also outperformed retinal specialists in the detection of large drusen (accuracy 0.742 vs. 0.696; kappa 0.601 vs. 0.517) and pigmentary abnormalities (accuracy 0.890 vs. 0.813; kappa 0.723 vs. 0.535) but showed lower performance in the detection of late AMD (accuracy 0.967 vs. 0.973; kappa 0.663 vs. 0.754). Conclusions: By simulating the human grading process, DeepSeeNet demonstrated high accuracy with increased transparency in the automated assignment of individual patients to AMD risk categories based on the AREDS Simplified Severity Scale. These results highlight the potential of deep learning to assist and enhance clinical decision-making in patients with AMD, such as early AMD detection and risk prediction for developing late AMD. DeepSeeNet is publicly available on https://github.com/ncbi-nlp/ DeepSeeNet.

Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration

“…; Peng et al. ) have showed performance close or even superior to that achieved by human graders. However, these systems perform independent analysis of each disease, although these diseases can coexist and a solution for joint detection would be beneficial (Chan et al.…”

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

“…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 .…”

“…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. Rather than using a multi‐step approach, Peng et al (2018) detected individual AMD risk factors including drusen, pigmentary changes and late AMD. Although they used late AMD as a classifier, this included geographic atrophy which our DLA does not classify as referable 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.…”

Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images

“…Recently, the development of artificial intelligence (AI) has enabled the efficient and automatic detection of retinopathies such as diabetic retinopathy (DR), agerelated degeneration (AMD), and glaucoma. However, most of these studies trained the deep learning models using images acquired from traditional fundus camera imaging [14][15][16][17][18][19] . Such imaging is adequate for observation of the optic nerve and posterior pole, but provides little information regarding the peripheral retina due to the limited visible scope (30°to 60°).…”