基于深度学习的心肺复苏术人工智能实时辅助培训和考核系统的构建

doi:10.3760/cma.j.cn115259-20220504-00571

中华医学教育杂志 ›› 2023, Vol. 43 ›› Issue (6): 418-422.DOI: 10.3760/cma.j.cn115259-20220504-00571

基于深度学习的心肺复苏术人工智能实时辅助培训和考核系统的构建

刘畅¹, 李广普², 陈梦欢¹, 王培松¹, 刘东伟², 李胜云¹

¹郑州大学第一附属医院临床技能培训中心, 郑州 450052;
²郑州大学第一附属医院中西医结合肾病科, 郑州 450052

收稿日期:2022-05-04 发布日期:2023-05-31
通讯作者: 李胜云, Email:lishengyun1@163.com
基金资助:
河南省医学科技公关计划联合共建项目(LHGJ20200382)

A deep learning based artificial intelligence real-time training and assessment system for cardiopulmonary resuscitation

Liu Chang¹, Li Guangpu², Chen Menghuan¹, Wang Peisong¹, Liu Dongwei², Li Shengyun¹

¹Clinical Skills Training Center, The First Affiliated Hospital of Zhengzhou University Zhengzhou 450052, China;
²TCM-Integrate Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

Received:2022-05-04 Published:2023-05-31
About author:Li Shengyun, Email: lishengyun1@163.com
Supported by:
Henan Province Medical Science and Technology Research Project Joint Construction Project (LHGJ20200382)

摘要/Abstract

摘要： 目的探索构建基于深度学习的心肺复苏术(cardiopulmonary resuscitation,CPR)实时人工智能辅助培训和考核系统,为CPR培训和考核提供有效便捷的方法。方法 2021年3月至2022年3月获取郑州大学第一附属医院临床技能培训中心90名学员的心肺复苏培训和考试数据,随机分为训练组(72例)与测试组(18例)。应用训练组视频资料和深度学习模型,构建CPR人工智能实时辅助培训和考核系统,对两组学员视频中身体的关键点进行检测和定位,提取CPR的考核指标,实时展示在移动终端上。比较两组学员的考核指标,并与传感器显示的考核指标进行比较,同时比较人工智能评分与考官评分,采用t检验、非参数检验或方差分析。结果视频人工智能分析结果显示,训练组和测试组每轮按压时间分别为(16.82±1.24)秒、(16.88±1.22)秒,按压深度分别为[5.85(5.60,6.00)]厘米、[5.80(5.50,6.10)] 厘米,按压频率分别为(107.64±8.13) 次/分钟、(107.11±8.49)次/分钟,差异均无统计学意义(P>0.05)。人工智能分析结果和传感器测量结果显示,按压时间分别为(16.83±1.23)秒、(16.80±1.21)秒,按压频率分别为(107.42±8.07) 次/分钟、(107.71±8.00)次/分钟,差异均无统计学意义(P>0.05)。人工智能与3位考官的评价分别为(68.92±3.20)分,(68.92±3.28)分,(68.83±3.24)分,(68.84±3.25)分,差异均无统计学意义(P>0.05)。结论 CPR人工智能实时辅助培训和考核系统能够通过对学员CPR操作时身体关键点的检测和定位,较为准确地实时检测学员CPR操作情况并在移动终端显示,这有助于学员CPR的培训和考核。

关键词: 心肺复苏术, 深度学习, 人工智能, 考核, 培训

Abstract: Objective To explore the development of a real-time artificial intelligence (AI) assisted training and assessment system for cardiopulmonary resuscitation (CPR) based on deep learning, providing an effective and convenient method for CPR training and assessment. Methods Video data and sensor data of 90 trainees undergoing CPR training and examination at the Clinical Skills Training Center of the First Affiliated Hospital of Zhengzhou University from March 2021 to March 2022 were collected and randomly divided into a training group (72 participants) and a testing group (18 participants). Using the training group video data and deep learning models, an AI real-time assisted training and assessment system for CPR was constructed to detect and locate the key points of trainees′ bodies during CPR and extract the assessment indicators, which were displayed in real-time on mobile devices. The assessment indicators obtained from the video analysis by AI in the two groups of trainees were compared, as well as the comparison between the assessment indicators obtained from the video analysis and the sensor data and the comparison between the scores given by the AI system and the human examiners. T-test, non-parametric test or analysis of variance was used for statistical analyses. Results The AI analysis of the video data showed that the compression time, depth, and frequency for both the training and testing groups were (16.82±1.24) and (16.88±1.22) seconds, [5.85(5.60, 6.00)] and [5.80(5.50, 6.10)] cm, (107.64±8.13) and (107.11±8.49) times/minute, respectively, with no statistical difference (P>0.05). The assessment indicators obtained from the video analysis by AI and the sensor data showed that the compression time and frequency for both the training and testing groups were (16.83±1.23) and (16.80±1.21) seconds, (107.42±8.07) and (107.71±8.00) times/minute, respectively, with no statistical difference (P>0.05) between the two groups. The scores given by AI system and the human examiner were (68.92±3.20), (68.92±3.28), (68.83±3.24), (68.84±3.25), respectively. There was no statistical difference between the scores given by the AI system and the human examiners (P>0.05). Conclusions The real-time AI assisted training and assessment system for CPR detects and locates the key points of trainees′ bodies during CPR, and accurately displays the situation of trainees′ CPR in real-time on mobile devices, which is helpful for CPR training and assessment.

Key words: Cardiopulmonary resuscitation(CPR), Deep learning, Artificial intelligence (AI), Assessment, Training

中图分类号:

刘畅, 李广普, 陈梦欢, 王培松, 刘东伟, 李胜云. 基于深度学习的心肺复苏术人工智能实时辅助培训和考核系统的构建[J]. 中华医学教育杂志, 2023, 43(6): 418-422.

Liu Chang, Li Guangpu, Chen Menghuan, Wang Peisong, Liu Dongwei, Li Shengyun. A deep learning based artificial intelligence real-time training and assessment system for cardiopulmonary resuscitation[J]. Chinese Journal of Medical Education, 2023, 43(6): 418-422.

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基于深度学习的心肺复苏术人工智能实时辅助培训和考核系统的构建

A deep learning based artificial intelligence real-time training and assessment system for cardiopulmonary resuscitation

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