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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引用次数: 0
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.
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.