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通过早产儿前晶状体囊血管图像分析估计胎龄:一项初步研究。

Estimation of Gestational Age via Image Analysis of Anterior Lens Capsule Vascularity in Preterm Infants: A Pilot Study.

作者信息

Patel Monalisa, Mukherjee Dibyendu, Farsiu Sina, Munoz Breda, Blood Arlin B, Wilson Christopher G, Griffin Jennifer B

机构信息

Department of Pediatrics, Loma Linda University, Loma Linda, CA, United States.

Department of Biomedical Engineering, Duke University, Durham, NC, United States.

出版信息

Front Pediatr. 2019 Feb 20;7:43. doi: 10.3389/fped.2019.00043. eCollection 2019.

DOI:10.3389/fped.2019.00043
PMID:30842940
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6391335/
Abstract

Anterior lens capsule vascularity (ALCV) is resorbed in the developing fetus from 27 to 35 weeks gestation. In this pilot study, we evaluated the feasibility and validity of combining smartphone ophthalmoscope videos of ALCV and image analysis for gestational age estimation. ALCV videos were captured longitudinally in preterm neonates from delivery using a PanOptic® Ophthalmoscope with an ® adapter (Welch-Allyn). ALCV video frames were manually selected and quantified using semi-automatic image analysis. A predictive model based on ALCV features was compared to gold-standard ultrasound gestational age estimates. A total of 64 image-capture sessions were carried out in 24 neonates. Ultrasound-estimated gestational age and ALCV-predicted gestational age estimates indicate that the two methods are similar ( = 0.78, < 0.0001). ALCV estimates of gestational age were within 0.11 ± 1.3 weeks of ultrasound estimates. In the final model, gestational age was predicted within ± 1 week for 54% and within ± 2 weeks for 86% of the measures. This novel application of smartphone ophthalmoscopy and ALCV image analysis may provide a safe, accurate and non-invasive technology to estimate postnatal gestational age, especially in low income countries where gestational age may not be known at birth.

摘要

前囊膜血管化(ALCV)在妊娠27至35周的胎儿发育过程中会被吸收。在这项初步研究中,我们评估了结合智能手机检眼镜拍摄的ALCV视频和图像分析来估计胎龄的可行性和有效性。使用配备适配器(伟伦)的PanOptic®检眼镜,从分娩时开始对早产儿的ALCV进行纵向视频采集。使用半自动图像分析手动选择并量化ALCV视频帧。将基于ALCV特征的预测模型与金标准超声胎龄估计值进行比较。共对24例新生儿进行了64次图像采集。超声估计胎龄和ALCV预测胎龄表明这两种方法相似( = 0.78, < 0.0001)。ALCV胎龄估计值与超声估计值相差在0.11±1.3周内。在最终模型中,54%的测量值胎龄预测在±1周内,86%的测量值胎龄预测在±2周内。智能手机检眼镜和ALCV图像分析的这种新应用可能提供一种安全、准确且无创的技术来估计出生后的胎龄,特别是在出生时胎龄可能未知的低收入国家。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1e3/6391335/5d649d568603/fped-07-00043-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1e3/6391335/5a26c5738e2b/fped-07-00043-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1e3/6391335/5d649d568603/fped-07-00043-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1e3/6391335/5a26c5738e2b/fped-07-00043-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1e3/6391335/5d649d568603/fped-07-00043-g0003.jpg

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Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.使用深度学习和图搜索对非渗出性年龄相关性黄斑变性患者的光学相干断层扫描(OCT)图像中的九个视网膜层边界进行自动分割。
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Tree Topology Estimation.
使用PanOptic iExaminer系统对眼底照片中的视网膜血管系统进行特征分析。
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