Performance of a deep learning-based convolutional neural network for quantification of outer retina atrophy compared with in vivo imaging and manual histologic scoring.

Christiane V Löhr¹, Typhaine Lejeune², Lindsey A Smith^3,4, Judith Jayawickrama¹, Aleksandra U Zuraw^5,6

¹Oregon State University, Corvallis, OR.

²Charles River Laboratories Montreal ULC, Senneville, QC, Canada.

³Aiforia Inc. Cambridge Innovation Center, Cambridge, MA.

⁴MySME, LLC, Palos Heights, IL.

⁵Charles River Laboratories Inc., Frederick, MD.

⁶Charles River Laboratories Inc., Reno, NV.

PubMed DOI

Abstract

The efficacy of candidate drugs for neuroprotection is tested in rodent models of retinal atrophy where light-induced outer retinal atrophy (ORA) is quantified by manual or semi-automated measurements, analyses that are time-consuming and error-prone. We developed a quantitative, automated image analysis-based method of ORA assessment in whole-slide images (WSIs). A commercial, cloud-based, artifi…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.