Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings

  title={Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings},
  author={Ver'onica S'anchez-Guti'errez and Paula Hern'andez-Mart'inez and Francisco Jos{\'e} Mu{\~n}oz-Negrete and Jonne Engelberts and Allison M. Luger and Mark J. J. P. van Grinsven},
Background: To determine the ability of a commercially available deep learning system, RetCAD v.1.3.1 (Thirona, Nijmegen, The Netherlands) for the automatic detection of referable diabetic retinopathy (DR) on a dataset of colour fundus images acquired during routine clinical practice in a tertiary hospital screening program, analyzing the reduction of workload that can be released incorporating this artificial intelligence-based technology. Methods: Evaluation of the software was performed on a… 
1 Citations

Tables from this paper

Telemedicine and delivery of ophthalmic care in rural and remote communities: Drawing from Australian experience

Current practices in telemedicine and AI and the future of this technology in improving patient care in the field of ophthalmology are summarized.



Deep learning in ophthalmology: The technical and clinical considerations

Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degeneration

To validate the performance of a commercially available, CE‐certified deep learning (DL) system, RetCAD v.1.3.0, for the joint automatic detection of diabetic retinopathy and age‐related macular degeneration in colour fundus images on a dataset with mixed presence of eye diseases.

Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application

Given the rapid and impressive progress of artificial intelligence technologies, the implementation of deep learning systems into routinely practiced diabetic retinopathy screening could represent a cost-effective alternative to help reduce the incidence of preventable blindness around the world.

Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening.

Deep learning has substantial potential for health care and may allow the identification of which patients are likely to develop a particular disease and, among those with a particular condition, which patients need to be seen more frequently and perhaps treated more aggressively and determination of what specific treatments may be most appropriate for these patients.

Assessment of Automated Disease Detection in Diabetic Retinopathy Screening Using Two-Field Photography

Automated grading can safely reduce the workload of manual grading using two field, mydriatic photography in a routine screening service and be implemented as the first step in grading.

Screening for Diabetic Retinopathy in the Central Region of Portugal. Added Value of Automated ‘Disease/No Disease' Grading

Screening for DR using automated analysis allied to a simplified grading scale identifies DR vision-threatening complications well while decreasing human burden.

Automated Screening for Diabetic Retinopathy – A Systematic Review

It is demonstrated that despite limited specificity, automated retinal image analysis may potentially be valuable in different DR screening scenarios with a relatively high sensitivity and a substantial workload reduction.

The role of haemorrhage and exudate detection in automated grading of diabetic retinopathy

Introduction of algorithms for exudates and haemorrhages resulted in a statistically significant increase in the sensitivity for detection of observable/referable retinopathy from 94.9% to 96.6% without affecting manual grading workload.

OPHDIAT: a telemedical network screening system for diabetic retinopathy in the Ile-de-France.