Integration of Optical Coherence Tomography Images and Real-Life Clinical Data for Deep Learning Modeling: A Unified Approach in Prognostication of Diabetic Macular Edema

Muhammed Enes Atik*, İbrahim Kocak, Nihat Sayin, Sadik Etka Bayramoglu, Ahmet Ozyigit

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The primary ocular effect of diabetes is diabetic retinopathy (DR), which is associated with diabetic microangiopathy. Diabetic macular edema (DME) can cause vision loss for people with DR. For this reason, deciding on the appropriate treatment and follow-up has a critical role in terms of curing the disease. Current artificial intelligence (AI) approaches focus on OCT images and may ignore clinical, laboratory, and demographic information obtained by the specialist. This study presents a novel deep learning (DL) framework for evaluating the visual outcome of the TREX anti-VEGF intravitreal injection regimen. DL models are trained to extract deep features from OCT and ILM topographic images and the obtained deep features are combined with patients' demographic, clinical, and laboratory findings to predict the direction of the treatment process. When the ResNet-18 network is used, the proposed DL framework is able to predict the prognosis status of patients with the highest accuracy.

Original languageEnglish
JournalJournal of Biophotonics
DOIs
Publication statusAccepted/In press - 2024

Bibliographical note

Publisher Copyright:
© 2024 Wiley-VCH GmbH.

Keywords

  • clinical data
  • deep learning
  • diabetic macular edema
  • diabetic retinopathy
  • OCT images

Fingerprint

Dive into the research topics of 'Integration of Optical Coherence Tomography Images and Real-Life Clinical Data for Deep Learning Modeling: A Unified Approach in Prognostication of Diabetic Macular Edema'. Together they form a unique fingerprint.

Cite this