Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE).
IF 8.1
Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Christopher O Lew, Evan Calabrese, Joshua V Chen, Felicia Tang, Gunvant Chaudhari, Amanda Lee, John Faro, Sandra Juul, Amit Mathur, Robert C McKinstry, Jessica L Wisnowski, Andreas Rauschecker, Yvonne W Wu, Yi Li
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Abstract
Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy using MRI and basic clinical data. Materials and Methods In this study, MRI data of term neonates with encephalopathy in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) trial (ClinicalTrials.gov: NCT02811263), who were enrolled from 17 institutions between January 25, 2017, and October 9, 2019, were retrospectively analyzed. The harmonized MRI protocol included T1-weighted, T2-weighted, and diffusion tensor imaging. Deep learning classifiers were trained to predict the primary outcome of the HEAL trial (death or any neurodevelopmental impairment at 2 years) using multisequence MRI and basic clinical variables, including sex and gestational age at birth. Model performance was evaluated on test sets comprising 10% of cases from 15 institutions (in-distribution test set, n = 41) and 10% of cases from two institutions (out-of-distribution test set, n = 41). Model performance in predicting additional secondary outcomes, including death alone, was also assessed. Results For the 414 neonates (mean gestational age, 39 weeks ± 1.4 [SD]; 232 male, 182 female), in the study cohort, 198 (48%) died or had any neurodevelopmental impairment at 2 years. The deep learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.60, 0.86) and 63% accuracy in the in-distribution test set and an AUC of 0.77 (95% CI: 0.63, 0.90) and 78% accuracy in the out-of-distribution test set. Performance was similar or better for predicting secondary outcomes. Conclusion Deep learning analysis of neonatal brain MRI yielded high performance for predicting 2-year neurodevelopmental outcomes. Keywords: Convolutional Neural Network (CNN), Prognosis, Pediatrics, Brain, Brain Stem Clinical trial registration no. NCT02811263 Supplemental material is available for this article. © RSNA, 2024 See also commentary by Rafful and Reis Teixeira in this issue.
新生儿脑病的人工智能结果预测(AI-OPINE)。
"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校对审核。请注意,在制作最终校对稿的过程中,可能会发现一些可能影响内容的错误。目的 开发一种深度学习算法,利用核磁共振成像和基本临床数据预测缺氧缺血性脑病(HIE)新生儿的 2 年神经发育结局。材料与方法 在本研究中,对2017年1月25日至2019年10月9日期间从17家机构入组的 "高剂量促红细胞生成素治疗窒息(HEAL)"试验(ClinicalTrials.gov:NCT02811263)中患有脑病的足月新生儿的MRI数据进行了回顾性分析。统一的 MRI 方案包括 T1 加权、T2 加权和弥散张量成像。利用多序列 MRI 和基本临床变量(包括出生时的性别和胎龄)训练了深度学习分类器,以预测 HEAL 试验的主要结果(2 岁时死亡或任何神经发育障碍 [NDI])。对模型性能进行评估的测试集包括来自 15 家机构的 10% 病例(分布内测试集,n = 41)和来自 2 家机构的 100% 病例(分布外测试集,n = 41)。此外,还评估了模型在预测其他次要结果(包括单纯死亡)方面的性能。结果 在研究队列中的 414 名新生儿(平均胎龄为 39 周 ± 1.4,232 名男性,182 名女性)中,有 198 名(48%)在 2 年后死亡或出现任何 NDI。深度学习模型在分布内测试集上的接收者操作特征曲线下面积(AUC)为 0.74(95% CI:0.60-0.86),准确率为 63%;在分布外测试集上的接收者操作特征曲线下面积(AUC)为 0.77(95% CI:0.63-0.90),准确率为 78%。在预测次要结果方面的表现类似或更好。结论 对新生儿大脑磁共振成像的深度学习分析在预测2年神经发育结果方面具有很高的性能。©RSNA,2024。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
来源期刊
期刊介绍:
Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.