A Deep Learning Approach for Predicting the Response to Anti-VEGF Treatment in Diabetic Macular Edema Patients Using Optical Coherence Tomography Images

Karima Garraoui, Karima Garraoui, Ines Rahmany, Ines Rahmany, Salah Dhahri, Salah Dhahri, Hedi Tabia, Desiré Sidibé, Hsouna Zgolli, Nawres Khlifa

2025

Abstract

Diabetic macular edema (DME) is a serious complication of diabetes that can lead to vision loss, making the prediction of patient response to anti-vascular endothelial growth factor (anti-VEGF) treatment crucial for optimizing therapeutic strategies. This study introduces ESSDP (Extended Siam Saves Diabetes Patients), a novel deep learning approach leveraging a Siamese network architecture with EfficientNetB2 to predict therapeutic response in DME patients through optical coherence tomography (OCT) image analysis. By classifying patients into good or poor responder groups based on central macular thickness reduction after injection, the proposed framework achieved a predictive performance with an accuracy of 0.80, sensitivity of 0.71, precision of 0.89, and an F1-Score of 0.74. These findings highlight the potential of Siamese network-based deep learning architectures as effective tools for predicting treatment outcomes in DME patients, even when working with limited datasets, and pave the way for enhancing personalized treatment strategies in ophthalmology.

Paper Citation

in Harvard Style

Garraoui K., Rahmany I., Dhahri S., Tabia H., Sidibé D., Zgolli H. and Khlifa N. (2025). A Deep Learning Approach for Predicting the Response to Anti-VEGF Treatment in Diabetic Macular Edema Patients Using Optical Coherence Tomography Images. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 453-462. DOI: 10.5220/0013181700003890

in Bibtex Style

@conference{icaart25,
author={Karima Garraoui and Ines Rahmany and Salah Dhahri and Hedi Tabia and Desiré Sidibé and Hsouna Zgolli and Nawres Khlifa},
title={A Deep Learning Approach for Predicting the Response to Anti-VEGF Treatment in Diabetic Macular Edema Patients Using Optical Coherence Tomography Images},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={453-462},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013181700003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - A Deep Learning Approach for Predicting the Response to Anti-VEGF Treatment in Diabetic Macular Edema Patients Using Optical Coherence Tomography Images
SN - 978-989-758-737-5
AU - Garraoui K.
AU - Rahmany I.
AU - Dhahri S.
AU - Tabia H.
AU - Sidibé D.
AU - Zgolli H.
AU - Khlifa N.
PY - 2025
SP - 453
EP - 462
DO - 10.5220/0013181700003890
PB - SciTePress