Deep Learning Prediction of Response to Anti-VEGF among Diabetic Macular Edema Patients: Treatment Response Analyzer System (TRAS).

Saif Alryalat,Nada Al-Antary,Yasmine Arafa,Mohammed Abu-Ameerh,Mutasem Elfalah,Safa Alfegi,Mohammad Al-Antary,Tasneem Ghnaimat,Cornelia Boldyreff,Babak Azad

doi:10.3390/diagnostics12020312

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.

Highlights

Diabetic macular edema (DME) is the most common cause of visual impairment among patients with diabetes mellitus, affecting almost 4% of patients with diabetes [1,2]
One strategy that has been proposed, but not yet applied sufficiently, is the use of artificial intelligence (AI) and its machine and deep learning derivatives to aid in the prediction of the outcome of anti-VEGF [7]
DME was defined as central subfield thickness greater than 320 μm for men or 305 μm for women as measured on optical coherence tomography (OCT) [9], treatment-naive diabetic macular edema or more than three months since last anti-VEGF injection or more than six months since last steroid injection, and had macular OCT done within 7 days before and 7 days after intravitreal anti-VEGF

Summary

Introduction

Diabetic macular edema (DME) is the most common cause of visual impairment among patients with diabetes mellitus, affecting almost 4% of patients with diabetes [1,2]. Anti-vascular endothelial growth factors (Anti-VEGFs) are considered the first line in managing DME [3]. Those anti-VEGFs carry high costs and burden even in high resource settings [4,5]. The importance of allocating resources has been recently investigated during recent lockdowns, where only a limited number of ophthalmic procedures were allowed in Jordan [6]. In such situations, healthcare providers need to prioritize patients who would better respond to treatment and suffer more adverse outcomes if treatment was delayed.

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Discussion

Conclusion