| Title: | Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5) |
| Journal: | Scientific Reports |
| Published: | 9 Nov 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37945665/ |
| DOI: | https://doi.org/10.1038/s41598-023-46626-7 |
| URL: | https://www.nature.com/articles/s41598-023-46626-7.pdf |
Publication 8366
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Abstract
Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records alone are often not enough to generate a high-quality dataset for clinical, statistical, and machine learning analysis. We have developed a deep learning-based age-related macular degeneration (AMD) stage classifier, to efficiently identify the first onset of early/intermediate (iAMD), atrophic (GA), and neovascular (nAMD) stage of AMD in retrospective data. We trained a two-stage convolutional neural network to classify macula-centered 3D volumes from Topcon OCT images into 4 classes: Normal, iAMD, GA and nAMD. In the first stage, a 2D ResNet50 is trained to identify the disease categories on the individual OCT B-scans while in the second stage, four smaller models (ResNets) use the concatenated B-scan-wise output from the first stage to classify the entire OCT volume. Classification uncertainty estimates are generated with Monte-Carlo dropout at inference time. The model was trained on a real-world OCT dataset, 3765 scans of 1849 eyes, and extensively evaluated, where it reached an average ROC-AUC of 0.94 in a real-world test set.</p>
12 Authors
- Oliver Leingang
- Sophie Riedl
- Julia Mai
- Gregor S. Reiter
- Georg Faustmann
- Philipp Fuchs
- Hendrik P. N. Scholl
- Sobha Sivaprasad
- Daniel Rueckert
- Andrew Lotery
- Ursula Schmidt-Erfurth
- Hrvoje Bogunović
1 Application
| Application ID | Title |
|---|---|
| 45477 | Deciphering AMD by deep phenotyping and machine learning- PINNACLE |
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