急性缺血性脑卒中(AIS)是一种严重影响人类健康相关的疾病,其拥有高发病率和高死亡率,并与吸烟、高脂饮食等不良生活习惯相关,人工智能(AI)比如机器学习(ML)和深度学习(DL),可以实现从临床及辅助检查尤其是成像学检查中提取特征数据,经过算法处理,得出可信结果。近几年AI更多地应用于医院系统的工作中,并成为临床工作及科研项目有力的帮手。本文全面综述了AI预测急性缺血性脑卒中(AIS)患者在经过血管内治疗,尤其是经过血栓切除术治疗后的预后情况,从而实现精准有效的临床管理和护理决策。此外,本文还批判性地评估了现有研究的局限性,并且指出了新的研究方向,最终目标是提高AIS患者的生存率。Acute ischemic stroke (AIS) is a serious human health-related disease that is characterized by elevated morbidity and mortality rates. It is often linked to detrimental lifestyle behaviors, including smoking and high-fat dietary intake. The advent of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) methodologies, facilitates the extraction and analysis of feature data derived from clinical and ancillary assessments, particularly imaging studies. These data are processed through sophisticated algorithms to yield reliable outcomes. In recent years, AI has been increasingly integrated into hospital systems, emerging as a formidable tool in both clinical practice and research initiatives. This paper presents a comprehensive analysis of AI applications in predicting the prognosis of acute ischemic stroke (AIS) patients following endovascular interventions, with a particular focus on thrombectomy procedures. The objective is to enhance the accuracy and efficacy of clinical management and care decision-making processes. Furthermore, the study critically examines the limitations inherent in current research and identifies prospective avenues for future investigation, ultimately aiming to improve the survival outcomes of AIS patients.
目的调查广州市城乡养老院老年人痴呆患病情况。方法采用分层整群抽样方法,将广州地区养老院分为市级、区级、民营、厂矿及农村乡镇,每层各抽取1个养老院进行调查。应查者为抽中养老院的入住者,年龄≥60岁。实查1105人,失访12人(1.07%)。按美国精神障碍诊断与统计手册第4版的标准诊断痴呆。结果 (1)检出率:在1105名老人中,查出痴呆患者324例,检出率为29.32%。其中阿尔茨海默病(AD)为20.36%(225例)、血管性痴呆(VD)为7.69%(85例)、混合性痴呆为0.45%(5例)、帕金森病痴呆为0.27%(3例)、其他类型痴呆为0.54%(6例)。(2)女性痴呆患者235例,检出率(31.8%)高于男性(89例,24.3%;P<0.05)。(3)痴呆检出率随年龄增长而上升。(4)痴呆患者中轻度71例(占21.9%)、中度103例(占31.8%)、重度150例(占46.3%)。结论广州地区养老院老人的痴呆检出率为29.32%,其中 AD 检出率高于 VD,女性高于男性,以重度者多见,且痴呆检出率随年龄的增长而升高。
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