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