Artificial Intelligence Versus Rules-Based Approach for Segmenting NonPerfusion Area in a DRCR Retina Network Optical Coherence Tomography Angiography Dataset.

Journal: Investigative ophthalmology & visual science

PMID: 40062815

Abstract

PURPOSE: Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable screening tool. This study compares two optical coherence tomography angiography algorithms for quantifying NPA.

Authors

Tristan T Hormel

Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA.
Wesley T Beaulieu

Jaeb Center for Health Research, Tampa, Florida, United States.
Jie Wang
Jennifer K Sun

Beetham Eye Institute, Joslin Diabetes Center, Boston, MA, USA.
Yali Jia

Casey Eye Institute, Oregon Health and Science University, Portland, Oregon.

Keywords

Adult Aged Algorithms Artificial Intelligence Deep Learning Diabetic Retinopathy Female Fluorescein Angiography Fundus Oculi Humans Male Middle Aged Retinal Vessels ROC Curve Tomography, Optical Coherence

External Resources

View on PubMed Access via DOI PubMed (40062815)

Artificial Intelligence Versus Rules-Based Approach for Segmenting NonPerfusion Area in a DRCR Retina Network Optical Coherence Tomography Angiography Dataset.

Abstract

Authors

Keywords

External Resources

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