A novel depth estimation algorithm of chest compression for feedback of high-quality cardiopulmonary resuscitation based on a smartwatch

Detection and Evaluation for High-Quality Cardiopulmonary Resuscitation Based on a Three-Dimensional Motion Capture System: A Feasibility Study

High-quality cardiopulmonary resuscitation (CPR) and training are important for successful revival during out-of-hospital cardiac arrest (OHCA). However, existing training faces challenges in quantifying each aspect. This study aimed to explore the possibility of using a three-dimensional motion capture system to accurately and effectively assess CPR operations, particularly about the non-quantified arm postures, and analyze the relationship among them to guide students to improve their performance. We used a motion capture system (Mars series, Nokov, China) to collect compression data about five cycles, recording dynamic data of each marker point in three-dimensional space following time and calculating depth and arm angles. Most unstably deviated to some extent from the standard, especially for the untrained students. Five data sets for each parameter per individual all revealed statistically significant differences (p < 0.05). The correlation between Angle 1' and Angle 2' for trained (rs = 0.203, p < 0.05) and untrained students (rs = -0.581, p < 0.01) showed a difference. Their performance still needed improvement. When conducting assessments, we should focus on not only the overall performance but also each compression. This study provides a new perspective for quantifying compression parameters, and future efforts should continue to incorporate new parameters and analyze the relationship among them.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Impact of Ultrasonography on Chest Compression Fraction and Survival in Patients with Out-of-hospital Cardiac Arrest

INTRODUCTION

Whether ultrasonography (US) contributes to delays in chest compressions and hence a negative impact on survival is uncertain. In this study we aimed to investigate the impact of US on chest compression fraction (CCF) and patient survival.

METHODS

We retrospectively analyzed video recordings of the resuscitation process in a convenience sample of adult patients with non-traumatic, out-of-hospital cardiac arrest. Patients receiving US once or more during resuscitation were categorized as the US group, while the patients who did not receive US were categorized as the non-US group. The primary outcome was CCF, and the secondary outcomes were the rates of return of spontaneous circulation (ROSC), survival to admission and discharge, and survival to discharge with a favorable neurological outcome between the two groups. We also evaluated the individual pause duration and the percentage of prolonged pauses associated with US.

RESULTS

A total of 236 patients with 3,386 pauses were included. Of these patients, 190 received US and 284 pauses were related to US. Longer resuscitation duration was observed in the US group (median, 30.3 vs 9.7 minutes, P<.001). The US group had comparable CCF (93.0% vs 94.3%, P=0.29) with the non-US group. Although the non-US group had a better rate of ROSC (36% vs 52%, P=0.04), the rates of survival to admission (36% vs 48%, P=0.13), survival to discharge (11% vs 15%, P=0.37), and survival with favorable neurological outcome (5% vs 9%, P=0.23) did not differ between the two groups. The pause duration of pulse checks with US was longer than pulse checks alone (median, 8 vs 6 seconds, P=0.02). The percentage of prolonged pauses was similar between the two groups (16% vs 14%, P=0.49).

CONCLUSION

When compared to the non-ultrasound group, patients receiving US had comparable chest compression fractions and rates of survival to admission and discharge, and survival to discharge with a favorable neurological outcome. The individual pause was lengthened related to US. However, patients without US had a shorter resuscitation duration and a better rate of ROSC. The trend toward poorer results in the US group was possibly due to confounding variables and nonprobability sampling. It should be better investigated in further randomized studies.

A Novel Assessment Using a Panoramic Video Camera of Resuscitation Quality in Patients following Out-of-Hospital Cardiac Arrest

The assessment of cardiopulmonary resuscitation and teamwork quality in prehospital settings has always been challenging. Currently, commercialized quality-monitored chest pads and single-angle cameras are being used to monitor prehospital the resuscitation quality in patients following out-of-hospital cardiac arrest (OHCA). However, both these methods have drawbacks. In New Taipei City, we introduced the panoramic video camera as a novel method to assess the resuscitation quality of OHCA patients to monitor both technical skills and teamwork. The panoramic video camera enabled a comprehensive evaluation of prehospital resuscitation, thereby allowing team members to evaluate their performance by reviewing the video after resuscitation. This is the first step toward improving the evaluation of prehospital resuscitation. Using this panoramic video camera and a high-speed internet connection, real-time resuscitation feedback from the dispatch center or medical directors can be provided promptly, thus, making prehospital resuscitation safe and efficient.

A New Immersive Virtual Reality Station for Cardiopulmonary Resuscitation Objective Structured Clinical Exam Evaluation

The Objective Structured Clinical Exam (OSCE) is an assessment tool used as a reliable method for clinical competence evaluation of students. This paper presents an investigation focused on the chain of survival, its related exploration, management, and technical skills, and how Virtual Reality (VR) can be used for the creation of immersive environments capable of evaluating students' performance while applying the correct protocols. In particular, the Cardiopulmonary Resuscitation (CPR) procedure is studied as an essential step in the development of the chain of survival. The paper also aims to highlight the limitations of traditional methods using mechanical mannequins and the benefits of the new approaches that involve the students in virtual, immersive, and dynamic environments. Furthermore, an immersive VR station is presented as a new technique for assessing CPR performance through objective data collection and posterior evaluation. A usability test was carried out with 33 clinicians and OSCE evaluators to test the viability of the presented scenario, reproducing conditions of a real examination. Results suggest that the environment is intuitive, quick, and easy to learn and could be used in clinical practice to improve CPR performance and OSCE evaluation.

An evolutionary approach to continuously estimate CPR quality parameters from a wrist-worn inertial sensor

Cardiopulmonary resuscitation (CPR) is one of the most critical emergency interventions for sudden cardiac arrest. In this paper, a robust sinusoidal model-fitting method based on a Evolution Strategy inspired algorithm for CPR quality parameters – naming chest compression frequency and depth – as measured by an inertial measurement unit (IMU) attached to the wrist is presented. The proposed approach will allow bystanders to improve CPR as part of a continuous closed-loop support system once integrated into a smartphone or smartwatch application. By evaluating the model’s precision with data recorded by a training mannequin as reference standard, a variance for the compression frequency of $$\pm 2.22$$ ± 2.22 compressions per minute (cpm) has been found for the IMU attached to the wrist. It was found that this previously unconsidered position and thus, the use of smartwatches is a suitable alternative to the typical placement of phones in hand for CPR training.