Xu Manli, Fu Xianjun, Jin Hui, Yu Xinlei, Xu Gang, Ma Zishuo, Pan Cheng, Liu Bo
The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310053, China.
School of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou 325016, China.
Brain Sci. 2024 Jun 20;14(6):618. doi: 10.3390/brainsci14060618.
Intracerebral hemorrhage (ICH) is a critical condition characterized by a high prevalence, substantial mortality rates, and unpredictable clinical outcomes, which results in a serious threat to human health. Improving the timeliness and accuracy of prognosis assessment is crucial to minimizing mortality and long-term disability associated with ICH. Due to the complexity of ICH, the diagnosis of ICH in clinical practice heavily relies on the professional expertise and clinical experience of physicians. Traditional prognostic methods largely depend on the specialized knowledge and subjective judgment of healthcare professionals. Meanwhile, existing artificial intelligence (AI) methodologies, which predominantly utilize features derived from computed tomography (CT) scans, fall short of capturing the multifaceted nature of ICH. Although existing methods are capable of integrating clinical information and CT images for prognosis, the effectiveness of this fusion process still requires improvement. To surmount these limitations, the present study introduces a novel AI framework, termed the ICH Network (ICH-Net), which employs a joint-attention cross-modal network to synergize clinical textual data with CT imaging features. The architecture of ICH-Net consists of three integral components: the Feature Extraction Module, which processes and abstracts salient characteristics from the clinical and imaging data, the Feature Fusion Module, which amalgamates the diverse data streams, and the Classification Module, which interprets the fused features to deliver prognostic predictions. Our evaluation, conducted through a rigorous five-fold cross-validation process, demonstrates that ICH-Net achieves a commendable accuracy of up to 87.77%, outperforming other state-of-the-art methods detailed within our research. This evidence underscores the potential of ICH-Net as a formidable tool in prognosticating ICH, promising a significant advancement in clinical decision-making and patient care.
脑出血（ICH）是一种危急病症，具有高发病率、高死亡率和不可预测的临床结果，对人类健康构成严重威胁。提高预后评估的及时性和准确性对于将与脑出血相关的死亡率和长期残疾降至最低至关重要。由于脑出血的复杂性，临床实践中脑出血的诊断严重依赖于医生的专业知识和临床经验。传统的预后方法在很大程度上取决于医疗专业人员的专业知识和主观判断。同时，现有的人工智能（AI）方法主要利用计算机断层扫描（CT）扫描得出的特征，无法捕捉脑出血的多方面性质。尽管现有方法能够整合临床信息和CT图像进行预后评估，但这种融合过程的有效性仍需提高。为了克服这些局限性，本研究引入了一种新颖的人工智能框架，称为脑出血网络（ICH-Net），该框架采用联合注意力跨模态网络将临床文本数据与CT成像特征协同起来。ICH-Net的架构由三个不可或缺的组件组成：特征提取模块，用于处理和提取临床和成像数据中的显著特征；特征融合模块，用于合并不同的数据流；分类模块，用于解释融合后的特征以进行预后预测。我们通过严格的五折交叉验证过程进行的评估表明，ICH-Net达到了高达87.77%的可观准确率，优于我们研究中详述的其他现有最先进方法。这一证据强调了ICH-Net作为脑出血预后强大工具的潜力，有望在临床决策和患者护理方面取得重大进展。
