Cardiac arrest and cardiopulmonary resuscitation in the next decade: Predicting and shaping the impact of technological innovations

Semeraro, Federico; Schnaubelt, Sebastian; Malta Hansen, Carolina; Bignami, Elena Giovanna; Piazza, Ornella; Monsieurs, Koenraad G.

doi:10.1016/j.resuscitation.2024.110250

Resuscitation

2024

DOI: 10.1016/j.resuscitation.2024.110250

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Cardiac arrest and cardiopulmonary resuscitation in the next decade: Predicting and shaping the impact of technological innovations

Federico Semeraro,

Sebastian Schnaubelt,

Carolina Malta Hansen

et al.

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2024

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Cited by 3 publications

References 28 publications

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Systems of care for cardiac arrest patients: Where are we going for tomorrow?

Lauridsen,

Lim

2024

Resuscitation Plus

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Systems of care for cardiac arrest patients: Where are we going for tomorrow?

Lauridsen,

Lim

2024

Resuscitation Plus

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Cardiac arrest and medical technological innovations in the next decade: How about artificial intelligence-assisted tailored cardiopulmonary resuscitation?

Cucino,

Palmisano,

Stirparo

et al. 2024

Resuscitation

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A Deep-Learning-Based CPR Action Standardization Method

Li,

Yin,

et al. 2024

Sensors

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In emergency situations, ensuring standardized cardiopulmonary resuscitation (CPR) actions is crucial. However, current automated external defibrillators (AEDs) lack methods to determine whether CPR actions are performed correctly, leading to inconsistent CPR quality. To address this issue, we introduce a novel method called deep-learning-based CPR action standardization (DLCAS). This method involves three parts. First, it detects correct posture using OpenPose to recognize skeletal points. Second, it identifies a marker wristband with our CPR-Detection algorithm and measures compression depth, count, and frequency using a depth algorithm. Finally, we optimize the algorithm for edge devices to enhance real-time processing speed. Extensive experiments on our custom dataset have shown that the CPR-Detection algorithm achieves a mAP0.5 of 97.04%, while reducing parameters to 0.20 M and FLOPs to 132.15 K. In a complete CPR operation procedure, the depth measurement solution achieves an accuracy of 90% with a margin of error less than 1 cm, while the count and frequency measurements achieve 98% accuracy with a margin of error less than two counts. Our method meets the real-time requirements in medical scenarios, and the processing speed on edge devices has increased from 8 fps to 25 fps.

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Cardiac arrest and cardiopulmonary resuscitation in the next decade: Predicting and shaping the impact of technological innovations

Cited by 3 publications

References 28 publications

Systems of care for cardiac arrest patients: Where are we going for tomorrow?

Systems of care for cardiac arrest patients: Where are we going for tomorrow?

Cardiac arrest and medical technological innovations in the next decade: How about artificial intelligence-assisted tailored cardiopulmonary resuscitation?

A Deep-Learning-Based CPR Action Standardization Method

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