Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, China.
Eur J Radiol. 2024 Jul;176:111533. doi: 10.1016/j.ejrad.2024.111533. Epub 2024 May 25.
To develop and validate an end-to-end model for automatically predicting hematoma expansion (HE) after spontaneous intracerebral hemorrhage (sICH) using a novel deep learning framework.
This multicenter retrospective study collected cranial noncontrast computed tomography (NCCT) images of 490 patients with sICH at admission for model training (n = 236), internal testing (n = 60), and external testing (n = 194). A HE-Mind model was designed to predict HE, which consists of a densely connected U-net for segmentation process, a multi-instance learning strategy for resolving label ambiguity and a Siamese network for classification process. Two radiomics models based on support vector machine or logistic regression and two deep learning models based on residual network or Swin transformer were developed for performance comparison. Reader experiments including physician diagnosis mode and artificial intelligence mode were conducted for efficiency comparison.
The HE-Mind model showed better performance compared to the comparative models in predicting HE, with areas under the curve of 0.849 and 0.809 in the internal and external test sets respectively. With the assistance of the HE-Mind model, the predictive accuracy and work efficiency of the emergency physician, junior radiologist, and senior radiologist were significantly improved, with accuracies of 0.768, 0.789, and 0.809 respectively, and reporting times of 7.26 s, 5.08 s, and 3.99 s respectively.
The HE-Mind model could rapidly and automatically process the NCCT data and predict HE after sICH within three seconds, indicating its potential to assist physicians in the clinical diagnosis workflow of HE.
利用新型深度学习框架，开发并验证一种用于自动预测自发性脑出血（sICH）后血肿扩大（HE）的端到端模型。
这项多中心回顾性研究共纳入了 490 名 sICH 患者入院时的颅脑非增强 CT（NCCT）图像，用于模型训练（n=236）、内部测试（n=60）和外部测试（n=194）。设计了一个 HE-Mind 模型来预测 HE，该模型由一个用于分割过程的密集连接 U-net、一个用于解决标签歧义的多实例学习策略和一个用于分类过程的孪生网络组成。还开发了基于支持向量机或逻辑回归的两个放射组学模型和基于残差网络或 Swin 变压器的两个深度学习模型，用于性能比较。为了进行效率比较，还进行了包括医生诊断模式和人工智能模式的读者实验。
HE-Mind 模型在预测 HE 方面的表现优于比较模型，内部和外部测试集的曲线下面积分别为 0.849 和 0.809。在 HE-Mind 模型的辅助下，急诊医师、初级放射科医师和高级放射科医师的预测准确性和工作效率均显著提高，准确率分别为 0.768、0.789 和 0.809，报告时间分别为 7.26s、5.08s 和 3.99s。
HE-Mind 模型可以快速自动处理 NCCT 数据，并在三秒内预测 sICH 后的 HE，表明其有可能辅助医生在 HE 的临床诊断工作流程中。
