GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration

Autor:	Shan, Xuhao, Li, Xinyang, Ge, Ruiquan, Wu, Shibin, Elazab, Ahmed, Zhu, Jichao, Zhang, Lingyan, Jia, Gangyong, Xiao, Qingying, Wan, Xiang, Wang, Changmiao
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Intracerebral Hemorrhage (ICH) is a severe condition resulting from damaged brain blood vessel ruptures, often leading to complications and fatalities. Timely and accurate prognosis and management are essential due to its high mortality rate. However, conventional methods heavily rely on subjective clinician expertise, which can lead to inaccurate diagnoses and delays in treatment. Artificial intelligence (AI) models have been explored to assist clinicians, but many prior studies focused on model modification without considering domain knowledge. This paper introduces a novel deep learning algorithm, GCS-ICHNet, which integrates multimodal brain CT image data and the Glasgow Coma Scale (GCS) score to improve ICH prognosis. The algorithm utilizes a transformer-based fusion module for assessment. GCS-ICHNet demonstrates high sensitivity 81.03% and specificity 91.59%, outperforming average clinicians and other state-of-the-art methods. Comment: 6 pages, 3 figures, 5 tables, published to BIBM 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.04772 Zobrazit plný text záznamu View this record from Arxiv