Automated Quantification of Retinopathy of Prematurity Stage via Ultrawidefield OCT

作者：Spencer S. Burt, Aaron S. Coyner, Elizabeth V. Roti, Yakub A. Bayhaqi, John Jackson, Mani K. Woodward, Shuibin Ni, Susan Ostmo, Guangru B. Liang, Yali Jia, David Huang, Michael F. Chiang, Benjamin K. Young, Yifan Jian, J. Peter Campbell · 发表于：Ophthalmology Science · 年份：2024 · DOI：10.1016/j.xops.2024.100663 · 被引用次数：9 · 研究领域：Retinopathy of Prematurity Studies、Child Abuse and Related Trauma、Ophthalmology and Visual Impairment Studies

Purpose: Retinopathy of prematurity (ROP) stage is defined by the visual appearance of the vascular-avascular border, which reflects a spectrum of pathologic neurovascular tissue (NVT). Previous work demonstrated that the thickness of the ridge lesion, measured using OCT, corresponds to higher clinical diagnosis of stage. This study evaluates whether the volume of anomalous NVT (ANVTV), defined as abnormal tissue protruding from the regular contour of the retina, can be measured automatically using deep learning to develop quantitative OCT-based biomarkers in ROP. Design: Single-center retrospective case series. Participants: Thirty-three infants with ROP in the Oregon Health & Science University neonatal intensive care unit. Methods: OCT B-scans were collected using an investigational ultrawidefield OCT. The ANVTV was manually segmented. A set of 3347 B-scans and corresponding manual segmentations from 12 volumes from 6 patients were used to train an automated segmentation tool using a U-Net. An additional held-out test data set of 60 B-scans from 6 infants was used to evaluate model performance. The Dice-Sorensen coefficient (DSC) comparing manual and automated segmentation of ANVTV was calculated. Scans from 21 additional infants were used for clinical evaluation of ANVTV using the visit in which they had developed their peak stage of ROP. Each infant had every B-scan in a volume automatically segmented for ANVTV (total number of segmented voxels within the 60° temporal to...