| 人工智能与脑网络组融合助力意识障碍预后预测研究 |
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| 引用本文： | 宋明. 人工智能与脑网络组融合助力意识障碍预后预测研究[J]. 临床神经外科杂志, 2020, 17(1): 8-10 |
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| 作者姓名： | 宋明 |
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| 作者单位： | 中国科学院自动化研究所, 北京,100190 |
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| 基金项目： | 国家自然科学基金(31870984) |
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| 摘 要： | 意识障碍(DOC)患者的预后预测对临床治疗及患者家属都有重要意义。但目前的预后预测模型的准确度不高,敏感性和特异性均较低。本研究在基于病因、年龄和病程作为重要预测指标的基础上,融合使用基于脑功能磁共振影像的患者脑功能网络特征,利用人工智能和机器学习算法,研发出了一个预测意识障碍患者能否苏醒的计算模型,准确率达到了88%。
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| 关 键 词： | 脑网络 人工智能 意识障碍 预后预测 功能磁共振 |
Artificial intelligence and brain network research improve the prognostications of disorders of consciousness |
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| SONG Ming. Artificial intelligence and brain network research improve the prognostications of disorders of consciousness[J]. Journal of Clinical Neurosurgery, 2020, 17(1): 8-10 |
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| Authors: | SONG Ming |
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| Affiliation: | (Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China) |
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| Abstract: | Although some indicators and models have been proposed to prognosticate disorders of consciousness(DOC),any single method when used alone carries a high risk of false prediction.This study aimd to develop a multidomain prognostic model that combined functional brain networks with clinical characteristics to predict one year outcomes at the single-patient level.The model discriminate patients who would later recover consciousness from those who would not with an accuracy of around 88%. |
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| Keywords: | brain network artificial intelligence disorders of consciousness prognostications fMRI |
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