Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images

IF 7.8 1区 医学 Q1 Biochemistry, Genetics and Molecular Biology
Aging Cell Pub Date : 2024-06-06 DOI:10.1111/acel.14196
Yiyang Wang, Yunyan Ye, Shengyi Shi, Kehang Mao, Haonan Zheng, Xuguang Chen, Hanting Yan, Yiming Lu, Yong Zhou, Weimin Ye, Jing Ye, Jing-Dong J. Han
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Abstract

Stroke is a major threat to life and health in modern society, especially in the aging population. Stroke may cause sudden death or severe sequela-like hemiplegia. Although computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis methods, and artificial intelligence models have been built based on these images, shortage in medical resources and the time and cost of CT/MRI imaging hamper fast detection, thus increasing the severity of stroke. Here, we developed a convolutional neural network model by integrating four networks, Xception, ResNet50, VGG19, and EfficientNetb1, to recognize stroke based on 2D facial images with a cross-validation area under curve (AUC) of 0.91 within the training set of 185 acute ischemic stroke patients and 551 age- and sex-matched controls, and AUC of 0.82 in an independent data set regardless of age and sex. The model computed stroke probability was quantitatively associated with facial features, various clinical parameters of blood clotting indicators and leukocyte counts, and, more importantly, stroke incidence in the near future. Our real-time facial image artificial intelligence model can be used to rapidly screen and prediagnose stroke before CT scanning, thus meeting the urgent need in emergency clinics, potentially translatable to routine monitoring.

Abstract Image

Abstract Image

利用人工智能从面部图像对急性缺血性中风进行诊断前识别。
脑卒中是现代社会生命和健康的主要威胁,尤其是在老龄人口中。脑卒中可能导致猝死或类似偏瘫的严重后遗症。虽然计算机断层扫描(CT)和磁共振成像(MRI)是标准的诊断方法,并且已经根据这些图像建立了人工智能模型,但医疗资源的短缺以及 CT/MRI 成像的时间和成本阻碍了快速检测,从而增加了脑卒中的严重性。在此,我们通过整合 Xception、ResNet50、VGG19 和 EfficientNetb1 四个网络开发了一个卷积神经网络模型,该模型可基于二维面部图像识别脑卒中,在 185 名急性缺血性脑卒中患者和 551 名年龄和性别匹配的对照组的训练集中,交叉验证曲线下面积(AUC)为 0.91;在不考虑年龄和性别的独立数据集中,交叉验证曲线下面积(AUC)为 0.82。该模型计算出的中风概率与面部特征、凝血指标和白细胞计数等各种临床参数定量相关,更重要的是与近期中风发病率相关。我们的实时面部图像人工智能模型可用于在 CT 扫描前快速筛查和预诊中风,从而满足急诊的迫切需要,并有可能转化为常规监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Aging Cell
Aging Cell 生物-老年医学
CiteScore
14.40
自引率
2.60%
发文量
212
审稿时长
8 weeks
期刊介绍: Aging Cell, an Open Access journal, delves into fundamental aspects of aging biology. It comprehensively explores geroscience, emphasizing research on the mechanisms underlying the aging process and the connections between aging and age-related diseases.
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