Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence

Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence

Abstract This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-instrument training dataset composed of Optical coherence tomography-angiography (OCTA) images from two different manufacturers. A retrospectiv...

Full description

Saved in:
Bibliographic Details
Main Authors: Anna Heinke, Haochen Zhang, Krzysztof Broniarek, Katarzyna Michalska-Małecka, Wyatt Elsner, Carlo Miguel B. Galang, Daniel N. Deussen, Alexandra Warter, Fritz Kalaw, Ines Nagel, Akshay Agnihotri, Nehal N. Mehta, Julian Elias Klaas, Valerie Schmelter, Igor Kozak, Sally L. Baxter, Dirk-Uwe Bartsch, Lingyun Cheng, Cheolhong An, Truong Nguyen, William R. Freeman
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Optical coherence tomography-angiography (OCTA)
Cross-instrument training
Artificial intelligence (AI)
Deep neural networks (DNN)
Age-related macular degeneration (AMD)
Online Access:https://doi.org/10.1038/s41598-024-78327-0
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-instrument training dataset composed of Optical coherence tomography-angiography (OCTA) images from two different manufacturers. A retrospective cross-sectional study analyzed 2D vascular en-face OCTA images from Heidelberg Spectralis (1478 samples: 1102 training, 276 validation, 100 testing) and Optovue Solix (1003 samples: 754 training, 189 validation, 60 testing). OCTA scans were labeled based on clinical diagnoses and adjacent B-scan OCT fluid information, categorizing activity into normal, dry AMD, active wet AMD, and wet AMD in remission. Experiments explored cross-instrument disease classification using separate and combined datasets for training the DNN. Testing involved 100 Heidelberg and 60 Optovue samples. Training on Heidelberg data alone yielded 73% accuracy on Heidelberg images and 60% on Optovue images. Training on Optovue data alone resulted in 34% accuracy on Heidelberg and 85% on Optovue images. Combined training data from both instruments achieved 78% accuracy on Heidelberg and 76% on Optovue test sets. Results indicate that cross-instrument classifier training demonstrates high classification prediction accuracy, making cross-instrument training viable for future clinical applications. This implies that vascular morphology in OCTA can predict disease progression.
ISSN:2045-2322

Similar Items