# DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs

arXiv: https://arxiv.org/abs/1811.07492 · HTML: https://mlsanity.com/paper/1811.07492/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 22 of 22)

- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images](https://mlsanity.com/paper/2202.07983/) (2022)
- [RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark](https://mlsanity.com/paper/2201.00466/) (2022)
- [Neuro-Symbolic Learning: Principles and Applications in Ophthalmology](https://mlsanity.com/paper/2208.00374/) (2022)
- [LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models](https://mlsanity.com/paper/2410.01620/) (2024)
- [Analyzing Epistemic and Aleatoric Uncertainty for Drusen Segmentation in Optical Coherence Tomography Images](https://mlsanity.com/paper/2101.08888/) (2021)
- [Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation](https://mlsanity.com/paper/2305.10975/) (2023)
- [tdCoxSNN: Time-Dependent Cox Survival Neural Network for Continuous-time Dynamic Prediction](https://mlsanity.com/paper/2307.05881/) (2023)
- [AI Workflow, External Validation, and Development in Eye Disease Diagnosis](https://mlsanity.com/paper/2409.15087/) (2024)
- [AMD-Mamba: A Phenotype-Aware Multi-Modal Framework for Robust AMD Prognosis](https://mlsanity.com/paper/2508.02957/) (2025)
- [Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification](https://mlsanity.com/paper/2209.13420/) (2022)
- [Artificial intelligence as a gateway to scientific discovery: Uncovering features in retinal fundus images](https://mlsanity.com/paper/2301.06675/) (2023)
- [Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network](https://mlsanity.com/paper/2303.01939/) (2023)
- [Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling](https://mlsanity.com/paper/2405.08780/) (2024)
- [A better approach to diagnose retinal diseases: Combining our Segmentation-based Vascular Enhancement with deep learning features](https://mlsanity.com/paper/2405.16235/) (2024)
- [Multiscale Color Guided Attention Ensemble Classifier for Age-Related Macular Degeneration using Concurrent Fundus and Optical Coherence Tomography Images](https://mlsanity.com/paper/2409.00718/) (2024)
- [Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection](https://mlsanity.com/paper/2505.05291/) (2025)
- [Development of a Mobile Application for at-Home Analysis of Retinal Fundus Images](https://mlsanity.com/paper/2509.16814/) (2025)
- [Vessel-Aware Deep Learning for OCTA-Based Detection of AMD](https://mlsanity.com/paper/2603.06735/) (2026)
- [Toward Multimodal Conversational AI for Age-Related Macular Degeneration](https://mlsanity.com/paper/2604.25720/) (2026)
- [Deep Learning-assisted AMD Staging based on OCT and OCT Angiography](https://mlsanity.com/paper/2606.05379/) (2026)
- [Exploring the Latent Structure of AMD Severity Using Retinal Foundation Models](https://mlsanity.com/paper/2610.14138/) (2026)

## Abstract (arXiv)

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. DeepSeeNet, a deep learning model, was developed to classify patients automatically by the AREDS Simplified Severity Scale (score 0-5) using bilateral CFP. DeepSeeNet was trained on 58,402 and tested on 900 images from the longitudinal follow-up of 4549 participants from AREDS. Gold standard labels were obtained using reading center grades. 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. 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 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.