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

Newborn resuscitation algorithms emphasize that resuscitation is time-critical, and all algorithm steps are related to the time of birth. Infrared thermal video has the potential to capture events in the delivery room, such as birth, cord clamping, and resuscitative interventions, while upholding the privacy of patients and healthcare providers. The objectives of this concept study were to (i) investigate the technical feasibility of using thermal video in the delivery room to detect birth and cord clamping, and (ii) evaluate the accuracy of manual real-time registrations of the time of birth and cord clamping by comparing it with the accuracy of registrations abstracted from thermal videos. An observational study with data collected at Stavanger University Hospital, Norway, from September 2022 to August 2023. The time of birth and cord clamping were manually registered on a portable tablet by healthcare providers. Thermal cameras were placed in the delivery rooms and operating theatre to capture births. Videos were retrospectively reviewed to determine the time of birth and cord clamping. Participation consent was obtained from 306 mothers, of which 195 births occurred in delivery rooms or an operating theatre with a thermal camera installed. We excluded 12 videos in which no births occurred. Births were detectable in all 183 (100%) thermal videos evaluated. There was a median (quartiles) of 1.8 (0.7, 5.4) s deviation in the manual registrations of the times of births relative to those abstracted from thermal videos. Cord clamping was detectable in 173 of the 183 (95%) thermal videos, with a median of 18.3 (3.3, 108) s deviation in the manual registrations of the times of cord clampings relative to those abstracted from thermal videos. Recognizing the time of birth and cord clamping from thermal videos is technically feasible and provides a method for determining when resuscitative events occur.

After completing this article, readers should be able to: 1. Define the indeterminate class of recommendations for neonatal resuscitation. 2. Describe the two areas of current investigation within the indeterminate class recommendations. 3. Describe the application of two techniques from other settings within the indeterminate class recommendations. 4. Describe the indeterminate class recommendation for which conflicting evidence is emerging. With the shift to evidence-based guidelines, the process of revising the scientific framework for neonatal resuscitation and the derivative educational efforts will become more predictable and accessible. Beginning with the International Guidelines 2000, an Indeterminate Class of recommendations appeared. These focused on areas of intense scientific research that may lead to clinically important therapies; technological developments widely adopted for use in other age groups that may find a role in neonatal resuscitation; or emerging evidence that conflicts substantially with previous data, resulting in a revision of recommendations to withdraw support of a particular therapeutic approach. The advent of changes in evidence-based guidelines carries the obligation to monitor the impact of such changes. Finally, entirely new questions and proposed guideline recommendations will be submitted to evidence evaluation in the future. Five Indeterminate Class recommendations appeared in the neonatal resuscitation portion of the International Guidelines 2000 (Table⇓ ). Cerebral hypothermia following hypoxic-ischemic insult and positive-pressure ventilation with room air represent proposals in the translational research phase, moving from animal and molecular models into clinical trials. The recommendations relating to adjunctive airway techniques, laryngeal mask airway and exhaled carbon dioxide detection, recognize the importance of these techniques in the older pediatric and adult populations, but acknowledge the significant limitations in their application to neonates. The statement regarding high-dose epinephrine reinforces the conflicting nature of evidence relating to this therapy, yet it acknowledges that available evidence is extrapolated largely from older age groups and falls short of supporting …

Reply to Letter: “Laryngeal Mask Airway and newborn resuscitation” [Vincenzo Zanardo

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This paper describes the development of an optimized corn yield prediction model under extreme weather conditions for the Midwestern United States (US). We tested six different artificial intelligence (AI) models using satellite images and meteorological data for the dominant growth period. To examine the effects of extreme weather events, we defined the drought and heatwave by considering the characteristics of corn growth and selected the cases for sensitivity tests from a historical database. In particular, we conducted an optimization for the hyperparameters of the deep neural network (DNN) model to ensure the best configuration for accuracy improvement. The result for drought cases showed that our DNN model was approximately 51–98% more accurate than the other five AI models in terms of root mean square error (RMSE). For the heatwave cases, our DNN model showed approximately 30–77% better accuracy in terms of RMSE. The correlation coefficient was 0.954 for drought cases and 0.887–0.914 for heatwave cases. Moreover, the accuracy of our DNN model was very stable, despite the increases in the duration of the heatwave. It indicates that the optimized DNN model can provide robust predictions for corn yield under conditions of extreme weather and can be extended to other prediction models for various crops in future work.

Training, experience and need of booster courses in neonatal cardiopulmonary resuscitation. Survey to pediatricians

Motivated by the demonstrated success of artificial intelligence (AI) in wireless communications systems, this paper proposes a deep learning-based approach for generating a desired radiation pattern with sidelobe level (SLL) control in active electronically scanned array (AESA) antennas. Recent works in this direction are mostly limited to generating radiation patterns with only beam scanning capability, inhibiting their wide-scale applicability. In this work, we propose a unified deep neural network (DNN) model that enable simultaneous control over both beam scanning angles and SLLs across a range of operating scenarios. To accomplish this task, the DNN model efficiently predicts the phase and amplitude of each array element. To learn the DNN model’s parameters, we construct a training dataset comprising amplitude values and phases as labeled outputs and corresponding 181-point radiation patterns as input features. The training and validation process of the proposed DNN model reveals high accuracy in terms of R2 score and mean square error (MSE). For prediction, the desired radiation pattern consisting of 181 points is fed to the trained DNN model to yield optimized weights of antenna elements. The numerical results on a 1×8 linear phase antenna array, using an assortment of beam scanning angles and SLLs, demonstrate the effectiveness of the proposed model. The numerical results presented in MATLAB and CST simulators are validated by measurements on a 1×8 microstrip prototype array.

Cerebral cortical representation of motor kinematics is crucial for understanding human motor behavior, potentially extending to efficient control of the brain-computer interface. Numerous single-neuron studies have found the existence of a relationship between neuronal activity and motor kinematics such as acceleration, velocity, and position. Despite differences between kinematic characteristics, it is hard to distinguish neural representations of these kinematic characteristics with macroscopic functional images such as electroencephalography (EEG) and magnetoencephalography (MEG). The reason might be because cortical signals are not sensitive enough to segregate kinematic characteristics due to their limited spatial and temporal resolution. Considering different roles of each cortical area in producing movement, there might be a specific cortical representation depending on characteristics of acceleration, velocity, and position. Recently, neural network modeling has been actively pursued in the field of decoding. We hypothesized that neural features of each kinematic parameter could be identified with a high-performing model for decoding with an explainable AI method. Time-series deep neural network (DNN) models were used to measure the relationship between cortical activity and motor kinematics during reaching movement. With DNN models, kinematic parameters of reaching movement in a 3D space were decoded based on cortical source activity obtained from MEG data. An explainable artificial intelligence (AI) method was then adopted to extract the map of cortical areas, which strongly contributed to decoding each kinematics from DNN models. We found that there existed differed as well as shared cortical areas for decoding each kinematic attribute. Shared areas included bilateral supramarginal gyri and superior parietal lobules known to be related to the goal of movement and sensory integration. On the other hand, dominant areas for each kinematic parameter (the contralateral motor cortex for acceleration, the contralateral parieto-frontal network for velocity, and bilateral visuomotor areas for position) were mutually exclusive. Regarding the visuomotor reaching movement, the motor cortex was found to control the muscle force, the parieto-frontal network encoded reaching movement from sensory information, and visuomotor areas computed limb and gaze coordination in the action space. To the best of our knowledge, this is the first study to discriminate kinematic cortical areas using DNN models and explainable AI.

The following guidelines are intended for practitioners responsible for resuscitating neonates. They apply primarily to neonates undergoing transition from intrauterine to extrauterine life. The recommendations are also applicable to neonates who have completed perinatal transition and require resuscitation during the first few weeks to months following birth. Practitioners who resuscitate infants at birth or at any time during the initial hospital admission should consider following these guidelines. The terms newborn and neonate are intended to apply to any infant during the initial hospitalization. The term newly born is intended to apply specifically to an infant at the time of birth. Approximately 10% of newborns require some assistance to begin breathing at birth. About 1% require extensive resuscitative measures. Although the vast majority of newly born infants do not require intervention to make the transition from intrauterine to extrauterine life, because of the large number of births, a sizable number will require some degree of resuscitation. Those newly born infants who do not require resuscitation can generally be identified by a rapid assessment of the following 4 characteristics: If the answer to all 4 of these questions is “yes,” the baby does not need resuscitation and should not be separated from the mother. The baby can be dried, placed directly on the mother’s chest, and covered with dry linen to maintain temperature. Observation of breathing, activity, and color should be ongoing. If the answer to any of these assessment questions is “no,” there is general agreement that the infant should receive one or more of the following 4 categories of action in sequence:

IntroductionInfants born with critical congenital heart defects (CCHDs) have unique transitional pathophysiology that often requires special resuscitation and management considerations in the delivery room (DR). While much is known about...

Purpose: The prevalence of artificial intelligence (AI) in breast cancer screening and diagnosis has substantially increased over the past 5 years. AI can be used to assist in the interpretation of mammograms and other breast imaging modalities, as well as predict patient outcomes. This study aims to quantify AI's diagnostic accuracy and its ability to detect breast cancer from a large database of mammograms. The implementation of AI in breast cancer screening holds immense potential to not only enhance the accuracy of mammogram interpretation but also improve the overall efficiency of radiologists and healthcare providers. Furthermore, we aim to contribute to the advancement of the rapidly growing field of machine learning in medicine, particularly in breast cancer care. Our hope is that by applying this technology, we can improve patient outcomes and increase accessibility to breast cancer screening and early detection. Methods: A deep neural network (DNN) model was built on Tensorflow utilizing Radiological Society of North America Screening Mammography Breast Cancer Detection dataset, consisting of normal mammograms and abnormal mammograms with tumors. The model's efficacy was assessed using Area under the curve (AUC), precision, and recall, with images randomly split into training (80%), validation (10%), and test sets (10%). Four consecutive training sessions each lasting 2 hours and 13 minutes using 8,877 images consisting of 4,621 breast cancer and 4,456 normal breast mammogram images was done. Results: The model attained an AUC of 0.926, Specificity of 95%, Sensitivity of 73% and Accuracy of 84%. Conclusion: This DNN model was created for diagnosing breast cancer from mammograms, with higher AUC than the current standard. This study highlights the potential of AI to revolutionize breast cancer screening, prevention, and diagnosis, which will ultimately improve patient outcomes and increase accessibility for patients. The use of AI in detecting breast cancer on mammograms can not only provide support to radiologists but also improve their efficacy and accuracy, reducing the burden of high volume images. Additionally, the use of AI in detection and screening can address the barriers that come with physician shortages, especially in underserved areas. Extending AI’s reach to these areas of limited access can improve early detection and screening which tends to be lacking in under-resourced areas, thus addressing the gap in health equity worldwide. While this study highlights encouraging data at the intersection of AI and medicine, we acknowledge that AI’s role in medicine is not to replace but rather enhance the expertise of radiologists. Lastly, we hope that our developed tool can alleviate the workload of radiologists and increase efficiency in high-pressure settings such as emergency departments and rural areas, ultimately leading to improved patient care. Citation Format: Parsa Riazi Esfahani, Maya M Maalouf, Akshay J Reddy, Prashant Chawla. Utilizing Machine Learning Techniques to Investigate Mammograms for Breast Cancer Detection [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A088.

Background: Neonatal resuscitation is the set of interventions provided at the time of birth to support the establishment of breathing and circulation. 136 million births annually, (approx. 5-10%) require simple stimulation at birth to help them breath like rubbing and drying. Basic resuscitation with a bag-and-mask is required for an estimated 6 million (approx. 3-6%) of these babies each year, and is sufficient to resuscitate most neonates with secondary apnea. Methods: A Descriptive cross-sectional design is used through the present study in order to :To assess the nurses knowledge concerning cardiopulmonary resuscitation to neonatal and To find out the relationship between the knowledge scores of the nurses and their selected demographic variables of age, gender, level of education, years of experience, and training session. The period of the study is from The study was carried out from 20th December 2018 up to march 28th, 2019. A Non-probability (purposive ) sample of (60) nurses who are working in the delivery rooms in (Al-Zahra Hospital, AL-Forat general Hospital Al-Hakeem general Hospital and Al- Manathera hospital ) are included in the study sample. The data are collected through using a well-designed questionnaire that consist of (2) parts: Part I: Demographic data. This part consists of (8) items, including age, gender, Marital Status, years of experience, Level of education, Economic state, Residency, Training course in cardiopulmonary resuscitation ,and number of training courses. And Part 2: Information of the Nurses Knowledge toward Neonatal Resuscitation in the Delivery Rooms: This part of the questionnaire is consisting of ( 22) questions about Neonatal Resuscitation in the Delivery Rooms . the validity through (12) experts from different specialties (Face Validity) for reviewing the study instrument. The data was analyzed through using of the descriptive and inferential statistical analysis approaches. Results: The findings of the present study indicate that the Overall assessment for nurses Knowledge about the neonatal cardiopulmonary resuscitation are Fair. Also there is a there is a highly significant association between the nurses Knowledge concerning cardiopulmonary resuscitation to neonatal and their (Years of Experience, Hospital Name, No. of training course), while there is a non-significant relationship with the remaining demographic and clinical data

Laryngeal mask airway and newborn resuscitation

After completing this article, readers should be able to: 1. Describe the six new guidelines recommendations for neonatal resuscitation. 2. Explain the classes of the new recommendations. Neonatal resuscitation is comprised of a complex series of evaluations of the newborn, decisions on the intervention required, and actions to carry out resuscitation procedures. As attention to the condition of the newborn became more focused in the 1950s and 1960s, well-designed clinical research investigated certain fundamental aspects of care in the delivery room (eg, Apgar scoring and understanding of acute volume expansion after hemorrhage or cord accident). However, much of the sequence of neonatal resuscitation varied substantially between centers and even among individuals within a center. With the advent of the Neonatal Resuscitation Program (NRP) in 1987, a measure of standardization entered the routines of care. The algorithm and procedures within the NRP represented the consensus of expert opinion, based on knowledge of the available resuscitation literature. A mechanism for periodic scientific updates was linked to the Guidelines Conference of the Emergency Cardiovascular Care Committee of the American Heart Association (AHA-ECC). With the 2000 Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care, the process of neonatal resuscitation has taken a major step toward evidence-based recommendations. Questions submitted to evidence evaluation (Table 1⇓ ) generally represented problematic steps in resuscitation or areas of intense scientific research. Questions were identified through both the Neonatal Resuscitation Steering Committee of the American Academy of Pediatrics and the Pediatric Subcommittee, AHA-ECC. Literature review was directed by at least one expert from the United States and one from outside the United States, representing the International Liaison Committee on Resuscitation. This approach assured better incorporation of the nonEnglish scientific literature and varied viewpoints. As part of the process of classifying the evidence by level (Table 2⇓ ) and measuring …

Positive end-expiratory pressure during resuscitation at birth in very-low-birth-weight infants: A randomized controlled pilot trial