早产儿视网膜病变
人工智能
医学
概化理论
深度学习
人工智能应用
计算机科学
机器学习
数学
遗传学
生物
统计
胎龄
怀孕
作者
Rebekah H. Gensure,Michael F. Chiang,J. Peter Campbell
出处
期刊:Current Opinion in Ophthalmology
[Ovid Technologies (Wolters Kluwer)]
日期:2020-07-18
卷期号:31 (5): 312-317
被引量:36
标识
DOI:10.1097/icu.0000000000000680
摘要
Purpose of review In this article, we review the current state of artificial intelligence applications in retinopathy of prematurity (ROP) and provide insight on challenges as well as strategies for bringing these algorithms to the bedside. Recent findings In the past few years, there has been a dramatic shift from machine learning approaches based on feature extraction to ‘deep’ convolutional neural networks for artificial intelligence applications. Several artificial intelligence for ROP approaches have demonstrated adequate proof-of-concept performance in research studies. The next steps are to determine whether these algorithms are robust to variable clinical and technical parameters in practice. Integration of artificial intelligence into ROP screening and treatment is limited by generalizability of the algorithms to maintain performance on unseen data and integration of artificial intelligence technology into new or existing clinical workflows. Summary Real-world implementation of artificial intelligence for ROP diagnosis will require massive efforts targeted at developing standards for data acquisition, true external validation, and demonstration of feasibility. We must now focus on ethical, technical, clinical, regulatory, and financial considerations to bring this technology to the infant bedside to realize the promise offered by this technology to reduce preventable blindness from ROP.
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