HE-Mind: A model for automatically predicting hematoma expansion after spontaneous intracerebral hemorrhage

作者全名：Zhou, Zhiming; Chen, Weidao; Yu, Ruize; Chen, Yuanyuan; Li, Xuejiao; Zhou, Hongli; Fan, Qianrui; Wang, Jing; Wu, Xiaojia; Zhou, Yu; Zhou, Xi; Guo, Dajing

作者地址：[Zhou, Zhiming; Chen, Yuanyuan; Wang, Jing; Wu, Xiaojia; Zhou, Yu; Zhou, Xi; Guo, Dajing] Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, 74 Linjiang Rd, Chongqing 400010, Peoples R China; [Chen, Weidao; Yu, Ruize; Fan, Qianrui] Ocean Int Ctr, Inst Res, InferVis, Beijing 100025, Peoples R China; [Li, Xuejiao] Chongqing Med Univ, Affiliated Hosp 2, Dept Emergency, Chongqing, Peoples R China; [Zhou, Hongli] Nanchong Cent Hosp, Dept Radiol, Nanchong 637000, Sichuan, Peoples R China

通信作者：Guo, DJ (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, 74 Linjiang Rd, Chongqing 400010, Peoples R China.

来源：EUROPEAN JOURNAL OF RADIOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001251666200001

JCR分区：Q1

影响因子：3.2

年份：2024

卷号：176

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Intracerebral hemorrhage; Hematoma expansion; Computed tomography; Artificial intelligence; Deep learning

摘要：Purpose: 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. Methods: 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. Results: 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. Conclusions: 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.

基金机构：Natural Science Foundation of Chongqing, China [CSTB2022NSCQ-MSX0116]; Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University [CQYC2020030389]

基金资助正文：This study was supported by Natural Science Foundation of Chongqing, China (Grant No.CSTB2022NSCQ-MSX0116) and the Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University (CQYC2020030389, 2020-7).