# From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs

arXiv: https://arxiv.org/abs/1810.10343 · HTML: https://mlsanity.com/paper/1810.10343/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2018.

## Built on by (most cited first, 9 of 9)

- [VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence](https://mlsanity.com/paper/2310.04992/) (2023)
- [Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning](https://mlsanity.com/paper/2308.13411/) (2023)
- [Deep learning on fundus images detects glaucoma beyond the optic disc](https://mlsanity.com/paper/2103.11895/) (2021)
- [Deep Learning and Computer Vision for Glaucoma Detection: A Review](https://mlsanity.com/paper/2307.16528/) (2023)
- [I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications](https://mlsanity.com/paper/2104.02609/) (2021)
- [Segmentation-based Information Extraction and Amalgamation in Fundus Images for Glaucoma Detection](https://mlsanity.com/paper/2209.11456/) (2022)
- [Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging](https://mlsanity.com/paper/2403.05702/) (2024)
- [AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review](https://mlsanity.com/paper/2410.15947/) (2024)
- [EyeMVP: OCT-Informed Fundus Representation Learning via Paired CFP--OCT Pretraining](https://mlsanity.com/paper/2606.15129/) (2026)

## Abstract (arXiv)

Previous approaches using deep learning algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using spectral-domain optical coherence tomography (SDOCT) data to train a deep learning algorithm to quantify glaucomatous structural damage on optic disc photographs. The dataset included 32,820 pairs of optic disc photos and SDOCT retinal nerve fiber layer (RNFL) scans from 2,312 eyes of 1,198 subjects. A deep learning convolutional neural network was trained to assess optic disc photographs and predict SDOCT average RNFL thickness. The performance of the algorithm was evaluated in an independent test sample. The mean prediction of average RNFL thickness from all 6,292 optic disc photos in the test set was 83.3±14.5 μm, whereas the mean average RNFL thickness from all corresponding SDOCT scans was 82.5±16.8 μm (P = 0.164). There was a very strong correlation between predicted and observed RNFL thickness values (r = 0.832; P<0.001), with mean absolute error of the predictions of 7.39 μm. The areas under the receiver operating characteristic curves for discriminating glaucoma from healthy eyes with the deep learning predictions and actual SDOCT measurements were 0.944 (95% CI: 0.912- 0.966) and 0.940 (95% CI: 0.902 - 0.966), respectively (P = 0.724). In conclusion, we introduced a novel deep learning approach to assess optic disc photographs and provide quantitative information about the amount of neural damage. This approach could potentially be used to diagnose and stage glaucomatous damage from optic disc photographs.