Ahmad Tahani, Guida Alessandro, Stewart Samuel, Barrett Noah, Jiang Xiang, Vincer Michael, Afifi Jehier
Department of Pediatric Radiology, IWK Health, Halifax, NS, Canada.
Department of Diagnostic Imaging, Dalhousie University, Halifax, NS, Canada.
Eur Radiol. 2025 Apr;35(4):1948-1958. doi: 10.1007/s00330-024-11028-4. Epub 2024 Aug 30.
Cerebral ultrasound (CUS) is the main imaging screening tool in preterm infants. The aim of this work is to develop deep learning (DL) models that classify normal vs abnormal CUS to serve as a computer-aided detection tool providing timely interpretation of the scans.
A population-based cohort of very preterm infants (22-30 weeks) born between 2004 and 2016 in Nova Scotia, Canada. A set of nine sequential CUS images per infant was retrieved at three specific coronal landmarks at three pre-identified times (first, sixth weeks, and term age). A radiologist manually labeled each image as normal or abnormal. The dataset was split into training/development/test subsets (80:10:10). Different convolutional neural networks were tested, with filtering of the most uncertain prediction. The model's performance was assessed using precision/recall and the receiver operating area under the curve.
Sequential CUS retrieved for 538/665 babies (81% of the cohort). Four thousand one hundred eighty images were used to develop and test the model. The model performance was only discrete at the beginning but, through different machine learning strategies was boosted to good levels averaging 0.86 ROC AUC (95% CI: 0.82, 0.90) and 0.87 PR AUC (95% CI: 0.84, 0.90) (model uncertainty estimation filters using normalized entropy threshold = 0.5).
This study offers proof of the feasibility of applying DL to CUS. This basic diagnostic model showed good discriminative ability to classify normal versus abnormal CUS. This serves as a CAD and a framework for constructing a prognostic model.
This DL model can serve as a computer-aided detection tool to classify CUS of very preterm babies as either normal or abnormal. This model will also be used as a framework to develop a prognostic model.
Binary computer-aided detection models of CUS are applicable for classifying ultrasound images in very preterm babies. This model acts as a step towards developing a model for predicting neurodevelopmental outcomes in very preterm babies. This model serves as a tool for interpretation of CUS in this patient population with a heightened risk of brain injury.
脑超声（CUS）是早产儿主要的影像筛查工具。本研究旨在开发深度学习（DL）模型，对正常和异常的CUS进行分类，作为一种计算机辅助检测工具，及时解读扫描结果。
基于人群的队列研究，纳入2004年至2016年在加拿大新斯科舍省出生的极早产儿（22 - 30周）。在三个预先确定的时间点（出生后第一周、第六周和足月时），在三个特定的冠状位标志处，为每个婴儿获取一组九张连续的CUS图像。一名放射科医生将每张图像手动标记为正常或异常。数据集被分为训练/开发/测试子集（80:10:10）。测试了不同的卷积神经网络，并对最不确定的预测进行筛选。使用精确率/召回率和曲线下面积评估模型性能。
为538/665名婴儿（占队列的81%）获取了连续的CUS图像。4180张图像用于模型的开发和测试。模型性能一开始仅为离散状态，但通过不同的机器学习策略，提升至良好水平，平均曲线下面积（ROC AUC）为0.86（95%置信区间：0.82, 0.90），精确率-召回率曲线下面积（PR AUC）为0.87（95%置信区间：0.84, 0.90）（使用归一化熵阈值 = 0.5的模型不确定性估计过滤器）。
本研究证明了将DL应用于CUS的可行性。这个基本诊断模型在区分正常和异常CUS方面具有良好的判别能力。它可作为计算机辅助检测工具以及构建预后模型的框架。
这个DL模型可作为计算机辅助检测工具，将极早产儿的CUS分类为正常或异常。该模型还将用作开发预后模型的框架。
CUS的二元计算机辅助检测模型适用于对极早产儿的超声图像进行分类。该模型是朝着开发预测极早产儿神经发育结局模型迈出的一步。该模型可作为在这个脑损伤风险较高的患者群体中解读CUS的工具。
