Alryalat, Saif Aldeen, Al-Antary, Mohammad, Arafa, Yasmine, Azad, Babak, Boldyreff, Cornelia ORCID: https://orcid.org/0000-0002-2737-7671, Ghnaimat, Tasneem, Al-Antary, Nada, Alfegi, Safa, Elfalah, Mutasem and Abu-Ameerh, Mohammed (2022) Deep learning prediction of response to anti-vegf among diabetic macular edema patients: Treatment response analyzer system (tras). Diagnostics, 12 (2):312. pp. 1-15. ISSN 2075-4418 (Online) (doi:10.3390/diagnostics12020312)

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Abstract

Diabetic macular edema (DME) is the most common cause of visual impairment among
patients with diabetes mellitus. Anti-vascular endothelial growth factors (Anti-VEGFs) are considered
the first line in its management. The aim of this research has been to develop a deep learning (DL)
model for predicting response to intravitreal anti-VEGF injections among DME patients. The research
included treatment naive DME patients who were treated with anti-VEGF. Patient’s pre-treatment
and post-treatment clinical and macular optical coherence tomography (OCT) were assessed by
retina specialists, who annotated pre-treatment images for five prognostic features. Patients were
also classified based on their response to treatment in their post-treatment OCT into either good
responder, defined as a reduction of thickness by >25% or 50 µm by 3 months, or poor responder.
A novel modified U-net DL model for image segmentation, and another DL EfficientNet-B3 model
for response classification were developed and implemented for predicting response to anti-VEGF
injections among patients with DME. Finally, the classification DL model was compared with different
levels of ophthalmology residents and specialists regarding response classification accuracy. The
segmentation deep learning model resulted in segmentation accuracy of 95.9%, with a specificity
of 98.9%, and a sensitivity of 87.9%. The classification accuracy of classifying patients’ images into
good and poor responders reached 75%. Upon comparing the model’s performance with practicing
ophthalmology residents, ophthalmologists and retina specialists, the model’s accuracy is comparable
to ophthalmologist’s accuracy. The developed DL models can segment and predict response to
anti-VEGF treatment among DME patients with comparable accuracy to general ophthalmologists.
Further training on a larger dataset is nonetheless needed to yield more accurate response predictions.

Item Type:	Article
Additional Information:	This article belongs to the Special Issue Artificial Intelligence in Eye Disease.
Uncontrolled Keywords:	anti-VEGF; artificial intelligence; deep learning; diabetic retinopathy; macular edema
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	28 Apr 2023 16:06
URI:	http://gala.gre.ac.uk/id/eprint/42014

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