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SCREEN-DR: Collaborative platform for diabetic retinopathy.

Micael Pedrosa1, Jorge Miguel Silva1, João Figueira Silva1

  • 1DETI/IEETA, University of Aveiro, Portugal.

International Journal of Medical Informatics
|November 10, 2018
PubMed
Summary
This summary is machine-generated.

The SCREEN-DR project developed a collaborative platform to improve diabetic retinopathy (DR) screening. This platform aids in creating machine learning algorithms for faster and more efficient DR detection from retinal images.

Keywords:
Collaborative PACSComputer-aided diagnosisDiabetic retinopathy screeningImage annotationTelemedicine

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Science

Background:

  • Diabetic retinopathy (DR) is a leading cause of vision loss, necessitating early detection through retinal imaging.
  • Current screening methods face inefficiencies due to asymptomatic early stages and poor image quality.

Purpose of the Study:

  • To develop computer-aided methods for diabetic retinopathy detection.
  • To create a multidisciplinary collaborative platform for physicians and researchers to facilitate DR screening.

Main Methods:

  • Developed a collaborative platform for textual and visual annotation of DR image datasets.
  • Optimized platform architecture based on physician feedback for efficient annotation.
  • Implemented data aggregation, indexing, anonymization, and role-based access control.

Main Results:

  • The SCREEN-DR platform is deployed and its source code is publicly available.
  • Physicians created 1826 annotations for 701 images.
  • Annotated data is being used to train classification models for DR detection.

Conclusions:

  • The SCREEN-DR platform provides a valuable tool for creating annotated datasets for AI in DR screening.
  • This collaborative approach enhances the efficiency and effectiveness of diabetic retinopathy detection programs.