Mothers' acceptability of using novel technology with video and audio recording during newborn resuscitation: A cross-sectional survey.

OBJECTIVE: This study aims to assess the acceptability of a novel technology, MAchine Learning Application (MALA), among the mothers of newborns who required resuscitation. SETTING: This study took place at Bharatpur Hospital, which is the second-largest public referral hospital with 13 000 deliveries per year in Nepal. DESIGN: This is a cross-sectional survey. DATA COLLECTION AND ANALYSIS: Data collection took place from January 21 to February 13, 2022. Self-administered questionnaires on acceptability (ranged 1-5 scale) were collected from participating mothers. The acceptability of the MALA system, which included video and audio recordings of the newborn resuscitation, was examined among mothers according to their age, parity, education level and technology use status using a stratified analysis. RESULTS: The median age of 21 mothers who completed the survey was 25 years (range 18-37). Among them, 11 mothers (52.4%) completed their bachelor's or master's level of education, 13 (61.9%) delivered first child, 14 (66.7%) owned a computer and 16 (76.2%) carried a smartphone. Overall acceptability was high that all participating mothers positively perceived the novel technology with video and audio recordings of the infant's care during resuscitation. There was no statistical difference in mothers' acceptability of MALA system, when stratified by mothers' age, parity, or technology usage (p>0.05). When the acceptability of the technology was stratified by mothers' education level (up to higher secondary level vs. bachelor's level or higher), mothers with Bachelor's degree or higher more strongly felt that they were comfortable with the infant's care being video recorded (p = 0.026) and someone using a tablet when observing the infant's care (p = 0.046). Compared with those without a computer (n = 7), mothers who had a computer at home (n = 14) more strongly agreed that they were comfortable with someone observing the resuscitation activity of their newborns (71.4% vs. 14.3%) (p = 0.024). CONCLUSION: The novel technology using video and audio recordings for newborn resuscitation was accepted by mothers in this study. Its application has the potential to improve resuscitation quality in low-and-middle income settings, given proper informed consent and data protection measures are in place.

Usability, acceptability and feasibility of a novel technology with visual guidance with video and audio recording during newborn resuscitation: a pilot study.

OBJECTIVE: Inadequate adherence to resuscitation for non-crying infants will have poor outcome and thus rationalise a need for real-time guidance and quality improvement technology. This study assessed the usability, feasibility and acceptability of a novel technology of real-time visual guidance, with sound and video recording during resuscitation. SETTING: A public hospital in Nepal. DESIGN: A cross-sectional design. INTERVENTION: The technology has an infant warmer with light, equipped with a tablet monitor, NeoBeat and upright bag and mask. The tablet records resuscitation activities, ventilation sound, heart rate and display time since birth. Healthcare providers (HCPs) were trained on the technology before piloting. DATA COLLECTION AND ANALYSIS: HCPs who had at least 8 weeks of experience using the technology completed a questionnaire on usability, feasibility and acceptability (ranged 1-5 scale). Overall usability score was calculated (ranged 1-100 scale). RESULTS: Among the 30 HCPs, 25 consented to the study. The usability score was good with the mean score (SD) of 68.4% (10.4). In terms of feasibility, the participants perceived that they did not receive adequate support from the hospital administration for use of the technology, mean score (SD) of 2.44 (1.56). In terms of acceptability, the information provided in the monitor, that is, time elapsed from birth was easy to understand with mean score (SD) of 4.60 (0.76). CONCLUSION: The study demonstrates reasonable usability, feasibility and acceptability of a technological solution that records audio visual events during resuscitation and provides visual guidance to improve care.

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 ORAA-net 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.

Object Detection During Newborn Resuscitation Activities.

OBJECTIVE: 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 are 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, such as 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 activities.

Real-Time Chest Compression Quality Measurements by Smartphone Camera.

Out-of-hospital cardiac arrest (OHCA) is recognized as a global mortality challenge, and digital strategies could contribute to increase the chance of survival. In this paper, we investigate if cardiopulmonary resuscitation (CPR) quality measurement using smartphone video analysis in real-time is feasible for a range of conditions. With the use of a web-connected smartphone application which utilizes the smartphone camera, we detect inactivity and chest compressions and measure chest compression rate with real-time feedback to both the caller who performs chest compressions and over the web to the dispatcher who coaches the caller on chest compressions. The application estimates compression rate with 0.5 s update interval, time to first stable compression rate (TFSCR), active compression time (TC), hands-off time (TWC), average compression rate (ACR), and total number of compressions (NC). Four experiments were performed to test the accuracy of the calculated chest compression rate under different conditions, and a fifth experiment was done to test the accuracy of the CPR summary parameters TFSCR, TC, TWC, ACR, and NC. Average compression rate detection error was 2.7 compressions per minute (±5.0 cpm), the calculated chest compression rate was within ±10 cpm in 98% (±5.5) of the time, and the average error of the summary CPR parameters was 4.5% (±3.6). The results show that real-time chest compression quality measurement by smartphone camera in simulated cardiac arrest is feasible under the conditions tested.