Evaluation of a New Neural Network Classifier for Diabetic Retinopathy

Or Katz, Dan Presil, Liz Cohen, Roi Nachmani, Naomi Kirshner, Yaacov Hoch, Tsvi Lev, Aviel Hadad, Richard John Hewitt, David R. Owens

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Medical image segmentation is a well-studied subject within the field of image processing. The goal of this research is to create an AI retinal screening grading system that is both accurate and fast. We introduce a new segmentation network which achieves state-of-the-art results on semantic segmentation of color fundus photographs. By applying the net-work to identify anatomical markers of diabetic retinopathy (DR) and diabetic macular edema (DME), we collect sufficient information to classify patients by grades R0 and R1 or above, M0 and M1. Methods: The AI grading system was trained on screening data to evaluate the presence of DR and DME. The core algorithm of the system is a deep learning network that segments relevant anatomical features in a retinal image. Patients were graded according to the standard NHS Diabetic Eye Screening Program feature-based grading protocol. Results: The algorithm performance was evaluated with a series of 6,981 patient retinal images from routine diabetic eye screenings. It correctly predicted 98.9% of retinopathy events and 95.5% of maculopathy events. Non-disease events prediction rate was 68.6% for retinopathy and 81.2% for maculopathy. Conclusion: This novel deep learning model was trained and tested on patient data from annual diabetic retinopathy screenings can classify with high accuracy the DR and DME status of a person with diabetes. The system can be easily reconfigured according to any grading protocol, without running a long AI training procedure. The incorporation of the AI grading system can increase the graders’ productivity and improve the final outcome accuracy of the screening process.

Original languageEnglish
Pages (from-to)1401-1409
Number of pages9
JournalJournal of diabetes science and technology
Volume16
Issue number6
DOIs
StatePublished - 1 Nov 2022
Externally publishedYes

Keywords

  • AI
  • diabetic retinopathy
  • imaging
  • screening

ASJC Scopus subject areas

  • Bioengineering
  • Internal Medicine
  • Biomedical Engineering
  • Endocrinology, Diabetes and Metabolism

Fingerprint

Dive into the research topics of 'Evaluation of a New Neural Network Classifier for Diabetic Retinopathy'. Together they form a unique fingerprint.

Cite this