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

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

doi:10.3390/diagnostics12020312

Open AccessArticle

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

by

Saif Aldeen Alryalat

^1,*,†

,

Mohammad Al-Antary

^2,†

,

Yasmine Arafa

²,

Babak Azad

³

,

Cornelia Boldyreff

²,

Tasneem Ghnaimat

⁴,

Nada Al-Antary

⁵

,

Safa Alfegi

⁶

,

Mutasem Elfalah

¹

and

Mohammed Abu-Ameerh

¹

Department of Ophthalmology, The University of Jordan Hospital, The University of Jordan, Amman 11942, Jordan

²

School of Computing and Mathematical Sciences, University of Greenwich, London SE10 9LS, UK

³

School of Computer Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran

⁴

Department of Computer Science, University of Jordan, Amman 11942, Jordan

⁵

Ibn Al Haytham Hospital, Amman 11190, Jordan

⁶

Tripoli Central Hospital, Tripoli 22131, Libya

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Diagnostics 2022, 12(2), 312; https://doi.org/10.3390/diagnostics12020312

Submission received: 3 December 2021 / Revised: 16 January 2022 / Accepted: 24 January 2022 / Published: 26 January 2022

(This article belongs to the Special Issue Artificial Intelligence in Eye Disease)

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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.

Keywords: anti-VEGF; artificial intelligence; deep learning; diabetic retinopathy; macular edema

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MDPI and ACS Style

Alryalat, S.A.; Al-Antary, M.; Arafa, Y.; Azad, B.; Boldyreff, C.; Ghnaimat, T.; Al-Antary, N.; Alfegi, S.; Elfalah, M.; Abu-Ameerh, M. Deep Learning Prediction of Response to Anti-VEGF among Diabetic Macular Edema Patients: Treatment Response Analyzer System (TRAS). Diagnostics 2022, 12, 312. https://doi.org/10.3390/diagnostics12020312

AMA Style

Alryalat SA, Al-Antary M, Arafa Y, Azad B, Boldyreff C, Ghnaimat T, Al-Antary N, Alfegi S, Elfalah M, Abu-Ameerh M. Deep Learning Prediction of Response to Anti-VEGF among Diabetic Macular Edema Patients: Treatment Response Analyzer System (TRAS). Diagnostics. 2022; 12(2):312. https://doi.org/10.3390/diagnostics12020312

Chicago/Turabian Style

Alryalat, Saif Aldeen, Mohammad Al-Antary, Yasmine Arafa, Babak Azad, Cornelia Boldyreff, Tasneem Ghnaimat, Nada Al-Antary, Safa Alfegi, Mutasem Elfalah, and Mohammed Abu-Ameerh. 2022. "Deep Learning Prediction of Response to Anti-VEGF among Diabetic Macular Edema Patients: Treatment Response Analyzer System (TRAS)" Diagnostics 12, no. 2: 312. https://doi.org/10.3390/diagnostics12020312

APA Style

Alryalat, S. A., Al-Antary, M., Arafa, Y., Azad, B., Boldyreff, C., Ghnaimat, T., Al-Antary, N., Alfegi, S., Elfalah, M., & Abu-Ameerh, M. (2022). Deep Learning Prediction of Response to Anti-VEGF among Diabetic Macular Edema Patients: Treatment Response Analyzer System (TRAS). Diagnostics, 12(2), 312. https://doi.org/10.3390/diagnostics12020312

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

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