Activity Recognition From Newborn Resuscitation Videos

Objective: Birth asphyxia is one of the leading causes of neonatal deaths. A key for survival is performing immediate and continuous quality newborn resuscitation. A dataset of recorded signals during newborn resuscitation, including videos, has been collected in Haydom, Tanzania, and the aim is to analyze the treatment and its effect on the newborn outcome. An important step is to generate timelines of relevant resuscitation activities, including ventilation, stimulation, suction, etc., during the resuscitation episodes. Methods: We propose a two-step deep neural network system, ORAA-net, utilizing low-quality video recordings of resuscitation episodes to do activity recognition during newborn resuscitation. The first step is to detect and track relevant objects using Convolutional Neural Networks (CNN) and post-processing, and the second step is to analyze the proposed activity regions from step 1 to do activity recognition using 3D CNNs. Results: The system recognized the activities newborn uncovered, stimulation, ventilation and suction with a mean precision of 77.67 %, a mean recall of 77,64 %, and a mean accuracy of 92.40 %. Moreover, the accuracy of the estimated number of Health Care Providers (HCPs) present during the resuscitation episodes was 68.32 %. Conclusion: The results indicate that the proposed CNN-based two-step ORAAnet could be used for object detection and activity recognition in noisy low-quality newborn resuscitation videos. Significance: A thorough analysis of the effect the different resuscitation activities have on the newborn outcome could potentially allow us to optimize treatment guidelines, training, debriefing, and local quality improvement in newborn resuscitation.Comment: 10 page

NewbornTime - improved newborn care based on video and artificial intelligence - study protocol

Background Approximately 3-8% of all newborns do not breathe spontaneously at birth, and require time critical resuscitation. Resuscitation guidelines are mostly based on best practice, and more research on newborn resucitation is highly sought for. Methods The NewbornTime project will develop artificial intelligence (AI) based solutions for activity recognition during newborn resuscitations based on both visible light spectrum videos and infrared spectrum (thermal) videos. In addition, time-of-birth detection will be developed using thermal videos from the delivery rooms. Deep Neural Network models will be developed, focusing on methods for limited supervision and solutions adapting to on-site environments. A timeline description of the video analysis output enables objective analysis of resuscitation events. The project further aims to use machine learning to find patterns in large amount of such timeline data to better understand how newborn resuscitation treatment is given and how it can be improved. The automatic video analysis and timeline generation will be developed for on-site usage, allowing for data-driven simulation and clinical debrief for health-care providers, and paving the way for automated real-time feedback. This brings added value to the medical staff, mothers and newborns, and society at large. Discussion The project is a interdisciplinary collaboration, combining AI, image processing, blockchain and cloud technology, with medical expertise, which will lead to increased competences and capacities in these various fields.publishedVersio

Object Detection During Newborn Resuscitation Activities

Birth asphyxia is a major newborn mortality problem in low-resource countries. International guideline provides treatment recommendations; however, the importance and effect of the different treatments are not fully explored. The available data is collected in Tanzania, during newborn resuscitation, for analysis of the resuscitation activities and the response of the newborn. An important step in the analysis is to create activity timelines of the episodes, where activities include ventilation, suction, stimulation etc. Methods: The available recordings are noisy real-world videos with large variations. We propose a two-step process in order to detect activities possibly overlapping in time. The first step is to detect and track the relevant objects, like bag-mask resuscitator, heart rate sensors etc., and the second step is to use this information to recognize the resuscitation activities. The topic of this paper is the first step, and the object detection and tracking are based on convolutional neural networks followed by post processing. Results: The performance of the object detection during activities were 96.97 % (ventilations), 100 % (attaching/removing heart rate sensor) and 75 % (suction) on a test set of 20 videos. The system also estimate the number of health care providers present with a performance of 71.16 %. Conclusion: The proposed object detection and tracking system provides promising results in noisy newborn resuscitation videos. Significance: This is the first step in a thorough analysis of newborn resuscitation episodes, which could provide important insight about the importance and effect of different newborn resuscitation activitiesComment: 8 page

Advanced cardiac life support training by problem based method: effect on the trainees skills, knowledge and evaluation of trainers

Background: Cardiopulmonary-cerebral resuscitation (CPCR) training is essential for all hospital workers, especially junior residents who might become the manager of the resuscitation team. In our center, the traditional CPCR knowledge training curriculum for junior residents up to 5 years ago was lecture-based and had some faults. This study aimed to evaluate the effect of a problem-based method on residents’ CPCR knowledge and skills as well as their evaluation of their CPCR trainers. Methods: This study, conducted at Tehran University of Medical Sciences, included 290 first-year residents in 2009-2010-who were trained via a problem-based method (the problem-based group) - and 160 first-year residents in 2003-2004 - who were trained via a lecture-based method (the lecture-based group). Other educational techniques and facilities were similar. The participants self-evaluated their own CPCR knowledge and skills pre and post workshop and also assessed their trainers’ efficacy post workshop by completing special questionnaires. Results: The problem-based group, trained via the problem-based method, had higher self-assessment scores of CPCR knowledge and skills post workshop: the difference as regards the mean scores between the problem-based and lecture-based groups was 32.36 ± 19.23 vs. 22.33 ± 20.35 for knowledge (p value = 0.003) and 10.13 ± 7.17 vs. 8.19 ± 8.45 for skills (p value = 0.043). The residents’ evaluation of their trainers was similar between the two study groups (p value = 0.193), with the mean scores being 15.90 ± 2.59 and 15.46 ± 2.90 in the problem-based and lecture-based groups – respectively. Conclusion: The problem-based method increased our residents’ self-evaluation score of their own CPCR knowledge and skills

Generalized vs Specialized activity recognition system for newborn resuscitation videos using Deep Neural Networks.

Birth asphyxia is a global problem which has resulted in a high mortality rate of newborn babies all over the globe, it is a newborn’s inability to establish breathing at birth. A notable breakthrough is the marrying of medical technology with information technology in an attempt to tackle this global health problem. An example of this is the Safer Births project which is focused on establishing technological advancement to curb newborn deaths. In the year 2013, the Safer Births project started and has till date gathered a lot of data captured during resuscitation sessions. The Haydom data used for the Safer Births project and additional data from Nepal and SUS will be used with the aim of comparing a specialized and generalized model trained on activity recognition system I3D and RGB stream excluding optical flow. With focus on only the newborn region, the reason for this is to simplify the existing model. The experiment was conducted in view of the possibility of achieving a system that can generalize or specialize with a combination of different hospital data on some specific activities of interest namely Ventilation, Suction, Stimulation. A new simplified pipeline, which is a reduction of the previous work done by the saferbirth group, showed a very poor performance when generalized

Training Hospital Providers in Basic CPR Skills in Botswana: Acquisition, Retention and Impact of Novel Training Techniques

Objective Globally, one third of deaths each year are from cardiovascular diseases, yet no strong evidence supports any specific method of CPR instruction in a resource-limited setting. We hypothesized that both existing and novel CPR training programs significantly impact skills of hospital-based healthcare providers (HCP) in Botswana. Methods HCP were prospectively randomized to 3 training groups: instructor led, limited instructor with manikin feedback, or self-directed learning. Data was collected prior to training, immediately after and at 3 and 6 months. Excellent CPR was prospectively defined as having at least 4 of 5 characteristics: depth, rate, release, no flow fraction, and no excessive ventilation. GEE was performed to account for within subject correlation. Results Of 214 HCP trained, 40% resuscitate ≥1/month, 28% had previous formal CPR training, and 65% required additional skills remediation to pass using AHA criteria. Excellent CPR skill acquisition was significant (infant: 32% vs. 71%, p \u3c 0.01; adult 28% vs. 48%, p \u3c 0.01). Infant CPR skill retention was significant at 3 (39% vs. 70%, p \u3c 0.01) and 6 months (38% vs. 67%, p \u3c 0.01), and adult CPR skills were retained to 3 months (34% vs. 51%, p = 0.02). On multivariable analysis, low cognitive score and need for skill remediation, but not instruction method, impacted CPR skill performance. Conclusions HCP in resource-limited settings resuscitate frequently, with little CPR training. Using existing training, HCP acquire and retain skills, yet often require remediation. Novel techniques with increased student: instructor ratio and feedback manikins were not different compared to traditional instruction

2021 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Neonatal Life Support; Education, Implementation, and Teams; First Aid Task Forces; and the COVID-19 Working Group

The International Liaison Committee on Resuscitation initiated a continuous review of new, peer-reviewed published cardiopulmonary resuscitation science. This is the fifth annual summary of the International Liaison Committee on Resuscitation International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations; a more comprehensive review was done in 2020. This latest summary addresses the most recently published resuscitation evidence reviewed by International Liaison Committee on Resuscitation task force science experts. Topics covered by systematic reviews in this summary include resuscitation topics of video-based dispatch systems; head-up cardiopulmonary resuscitation; early coronary angiography after return of spontaneous circulation; cardiopulmonary resuscitation in the prone patient; cord management at birth for preterm and term infants; devices for administering positive-pressure ventilation at birth; family presence during neonatal resuscitation; self-directed, digitally based basic life support education and training in adults and children; coronavirus disease 2019 infection risk to rescuers from patients in cardiac arrest; and first aid topics, including cooling with water for thermal burns, oral rehydration for exertional dehydration, pediatric tourniquet use, and methods of tick removal. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, according to the Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations or good practice statements. Insights into the deliberations of the task forces are provided in Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces listed priority knowledge gaps for further research

European Resuscitation Council Guidelines for Resuscitation 2015 Section 10. Education and implementation of resuscitation

M. Castren on Educ Implementation Resuscitation -työryhmän jäsen.Peer reviewe

Download entire PDF InterProfessional Education and Care Newsletter, Vol. 2, No. 1, Fall 2010

This issue includes articles on: Developing Interprofessional Teams using High Fidelity Resuscitation Clinical Care Plan, Interprofessional Course The Patient Centered Medical Home: Federal, State and Local Initiatives to Transform Primary Care The First of an Occasional Series in Interprofessional Education and Care in the Patient-Centered Medical Home JCIPE hosted its First International Interprofessional Education and Care Conferenc