Newborn Face Research Articles

Newborn face mask ventilation training using a standardised intervention and respiratory function monitor: a before and after manikin study

ObjectiveThe International Liaison Committee on Resuscitation has recommended improvements in training for neonatal resuscitation, highlighting the potential role of respiratory function monitors (RFMs). Our objective was to determine whether a...

Immature neutrophils in cord blood exert increased expression of genes associated with antimicrobial function.

The immune systems of both the mother and the newborn face significant challenges during birth. Proper immune regulation after birth is essential for the survival of neonates. Numerous studies have demonstrated that the neonatal immune system is relatively immature, particularly in its adaptive arm, placing the primary responsibility for immune surveillance on innate immunity. Given the significant role of neutrophils in protecting the neonate after birth, we conducted a study investigating the properties of neutrophils in newborn cord blood using various methodological approaches. Our findings demonstrate the presence of immature low-density neutrophils in the cord blood, which are likely responsible for the observed elevated expression of genes coding for proteins essential to antimicrobial response, including myeloperoxidase, neutrophils elastase, and defensins. We propose that these cells function normally and support the protection of newborns early after birth. Furthermore, our results suggest that the mode of delivery might significantly influence the programming of neutrophil function. The presented findings emphasize the importance of distinct neutrophil subpopulations in neonatal immunity and their potential impact on early postnatal health.

Integration of a respiratory function monitor into newborn positive pressure ventilation training; development of a standardised training intervention

ObjectiveOne in twenty newborns require resuscitation with positive pressure ventilation (PPV) at birth. Newborn face mask ventilation is often poorly performed. To address this, the potential role of respiratory function monitors (RFM) in newborn resuscitation training has been highlighted. The objective of this study was to develop a standardised training intervention on newborn PPV using an RFM with a simple visual display to identify and correct suboptimal ventilations. MethodsWe adapted the framework from a simulation development guideline to create a hands-on intervention on newborn PPV using an RFM with simple visual feedback (Monivent NeoTraining). We enrolled a group of healthcare professionals to a manikin-based pilot study as part of this process, conducting a series of teaching sessions to refine the intervention. Suggested changes were gathered from participants and instructors. Our main objective was to develop a standardised, reproducible training intervention. ResultsA standardised training intervention on newborn PPV was systematically developed. Twenty-six healthcare professionals working in tertiary neonatal care participated in a pilot study, consisting of eight training sessions. Each iteration of the intervention was informed by the previous session. Instructions for the delivery of teaching were standardised and a training algorithm was developed. ConclusionRFM’s have been shown to be effective tools in research settings, addressing poor technique and face mask leak. They are not routinely used in newborn resuscitation training. To address this, we developed a standardised training intervention on newborn PPV using an RFM with simple visual feedback.

Aggregate Channel Features for newborn face detection in Neonatal Intensive Care Units.

An efficient face detector could be very helpful to point out possible neurological dysfunctions such as seizure events in Neonatal Intensive Care Units. However, its development is still challenging because large public datasets of newborns' faces are missing. Over the years several studies introduced semi-automatic approaches. This study proposes a fully automated face detector for newborns in Neonatal Intensive Care Units, based on the Aggregate Channel Feature algorithm. The developed method is tested on a dataset of video recordings from 42 full-term newborns collected at the Neuro-physiopathology and Neonatology Clinical Units, AOU Careggi, Firenze, Italy. The proposed system showed promising results, giving (mean ± standard error): log-Average Miss Rate = 0.47 ± 0.05 and Average Precision Recall = 0.61 ± 0.05. Moreover, achieved results highlighted interesting differences between newborns without seizures, newborns with electro-clinical seizures, and newborns with electrographic-only seizures. For both metrics statistically significant differences were found between patients with electro-clinical seizures and the other two groups. Clinical Relevance- The proposed method, based on quantitative physio-pathological features of facial movements, is of clinical relevance as it could speed up pain or seizure assessment of newborns in Neonatal Intensive Care Units.

Reconstruction of the fetus face from three-dimensional ultrasound using a newborn face statistical shape model

Background and objectiveThe fetal face is an essential source of information in the assessment of congenital malformations and neurological anomalies. Disturbance in early stages of development can lead to a wide range of effects, from subtle changes in facial and neurological features to characteristic facial shapes observed in craniofacial syndromes. Three-dimensional ultrasound (3D US) can provide more detailed information about the facial morphology of the fetus than the conventional 2D US, but its use for pre-natal diagnosis is challenging due to imaging noise, fetal movements, limited field-of-view, low soft-tissue contrast, and occlusions. MethodsIn this paper, we propose the use of a novel statistical morphable model of newborn faces, the BabyFM, for fetal face reconstruction from 3D US images. We test the feasibility of using newborn statistics to accurately reconstruct fetal faces by fitting the regularized morphable model to the noisy 3D US images. ResultsThe results indicate that the reconstructions are quite accurate in the central-face and less reliable in the lateral regions (mean point-to-surface error of 2.35 mm vs 4.86 mm). The algorithm is able to reconstruct the whole facial morphology of babies from US scans while handle adverse conditions (e.g. missing parts, noisy data). ConclusionsThe proposed algorithm has the potential to aid in-utero diagnosis for conditions that involve facial dysmorphology.

Full-convolution Siamese network algorithm under deep learning used in tracking of facial video image in newborns.

This study was carried out with the aim of exploring the full-convolution Siamese network (SiamFC) in the application of neonatal facial video image tracking, achieving accurate recognition of neonatal pain and helping doctors evaluate neonatal emotions in an automatic manner. The current technology shows low accuracy on facial image recognition of newborns, so the SiamFC algorithm under the deep learning was optimized in this study. Besides, a newborn facial video image tracking model (FVIT model) was constructed based on the SiamFC algorithm in combination with the attention mechanism with face tracking algorithm, and the facial features of newborns were tracked and recognized. In addition, a newborn face database was constructed based on the adult face database to evaluate performance of the FVIT model. It was found that the accuracy of the improved algorithm is 0.889, higher by 0.036 in contrast to other models; the area under the curve (AUC) of success rate reaches 0.748, higher by 0.075 compared with other algorithms. What’s more, the improved algorithm shows good performance in tracking the facial occlusion, facial expression changes, and scale conversion of newborns. Therefore, the improved algorithm shows higher accuracy and success rate and has good effect in capturing and tracking the facial images of newborns, thereby providing an experimental basis for facial recognition and pain assessment of newborns in the later stage.

Pose and Illumination Invariant Hybrid Feature Extraction for Newborn

Background: Abduction, swapping and mix-ups are the unfortunate events that could happen to Newborn while in hospital premises and medical personnel are finding it difficult to curb this unfortunate incident. Accurate patient identification (ID) is essential for patient safety, especially with our smallest and most vulnerable paediatric patients. The level of security is very crucial issue in maternity ward and the problem of missing and swapping of Newborn is of prime concern to the persons involved and affected. There is a common perception in the society that nothing can be done to prevent this unfortunate tragedy. In comparison to developed nations the developing countries are facing more challenges because of overcrowding and scarcity of medical facilities in the hospital. The face of a newborn baby changes appearance in different lighting conditions in spite of various contrast enhancement techniques. The class of images which are under similar environment conditions (inter-class) may result in more differences than that of the intra-class images under different illumination conditions. This gives rise to the misclassification of images. Objective: The main objective of this paper is perform newborn face recognition in a hybrid approach. Speeded Up Robust Features (SURF) and Local Binary Pattern (LBP). Methods: The scientific contributions of the proposed work are stated below: For face recognition of newborns in a semi controlled environment. Overcoming the pose and illumination challenge. Uses single gallery image. Uses a hybrid approach to improve the results. Conclusion: In this paper new semi supervised technique is used for demonstrate the improve the performance of the newborn face recognition system in different illumination and pose conditions. In our. Results: The average Rank 1 accuracy of the proposed method is 93.65% whereas that of the existing algorithms is quite low. The proposed technique is 12.8% more accurate than LBP, 10.5% more accurate than SURF, 12.8% more accurate than LDA and 18.1% more accurate than PCA for rank1.

A Novel Genetic Convolutional Neuro Multi-Fuzzy Techniques for Newborn Face Recognition

Advances in technology have made life simpler in today's society by supplying us with a variety of emerging demands lacking By assessing the progressive stability of biometric recognition accuracy for newborns, biometric recognition can be used to recognize missing newborns and prevent them from being switched in higher-level hospitals.. Recognizing and authenticating newborns is a major problem in many hospitals. The face recognition system does an outstanding job of identifying and authenticating the newborn. To answer these concerns, create a face recognition device for newborns. The proposed approach improves picture consistency on a newborn's face. Our objectives are to propose a genetic, convolutional neural network, and fuzzy logic-based automated framework for newborn face recognition. As a paradigm GCNMF is suggested for real-world newborn face recognition. Convolutional, pooling, and fully-connected layers, as well as a Neuro Fuzzy layer, form the Inherited Convolutional Neuro Multi-Fuzzy. The model employs hereditary, convolutional neural networks, and fuzzy logic to deal with ambiguity and imprecision in the input configuration representation. The efficacy and outcomes of the recommended method are then analyzed using newborn face datasets and the Genetic Convolutional Neuro Multi-Fuzzy (GCNMF) Approach.

Newborn face recognition using deep convolutional neural network

Development of expertise in Face Recognition has led researchers to apply its various techniques for newborn recognition as some of the problems such as swapping, kidnapping are still prevalent. Th...

Does All Newborn Face Lookalike to Human and Machine?

In this paper we present human recognition capability for newborn face, machine recognition capability for newborn face, comparison and analysis of human recognition with machine and propose automatic algorithms which are closer to human visual cortex and brain to improve face verification accuracy. The analysis demonstrates that neither humans nor machines are efficient in recognising newborn that lookalike, however, the recognition capability of machine is better than that of human.

Staring us in the face? An embodied theory of innate face preference

Human expertise in face perception grows over development, but even within minutes of birth, infants exhibit an extraordinary sensitivity to face-like stimuli. The dominant theory accounts for innate face detection by proposing that the neonate brain contains an innate face detection device, dubbed 'Conspec'. Newborn face preference has been promoted as some of the strongest evidence for innate knowledge, and forms a canonical stage for the modern form of the nature-nurture debate in psychology. Interpretation of newborn face preference results has concentrated on monocular stimulus properties, with little mention or focused investigation of potential binocular involvement. However, the question of whether and how newborns integrate the binocular visual streams bears directly on the generation of observable visual preferences. In this theoretical paper, we employ a synthetic approach utilizing robotic and computational models to draw together the threads of binocular integration and face preference in newborns, and demonstrate cases where the former may explain the latter. We suggest that a system-level view considering the binocular embodiment of newborn vision may offer a mutually satisfying resolution to some long-running arguments in the polarizing debate surrounding the existence and causal structure of newborns' 'innate knowledge' of faces.

The neural correlates of processing newborn and adult faces in 3‐year‐old children

The current study examines the processing of upright and inverted faces in 3-year-old children (n = 35). Event-related potentials (ERPs) were recorded during a passive looking paradigm including adult and newborn face stimuli. We observed three face-sensitive components, the P1, the N170 and the P400. Inverted faces elicited shorter P1 latency and larger P400 amplitude. P1 and N170 amplitudes were larger for adult faces. To examine the role of experience in the development of face processing, the processing of adult and newborn faces was compared for children with a younger sibling (n = 23) and children without a younger sibling (n = 12). Age of sibling at test correlated negatively with P1 amplitude for adult and newborn faces. This may indicate more efficient processing of different face ages in children with a younger sibling and potentially reflects a more flexible face representation.

Face recognition for newborns

Mixing, abduction and illegal adoption of newborns is a global problem and the research done to solve this problem is minimal. Traditional methods of biometric and non-biometric techniques have their own limitations and fail to provide enough level of security for newborns. The work on adult face recognition has been an active research area in recent years and automatic face recognition for newborns is least reported in the literature. Therefore it is imperative to initiate research, so that future face recognition algorithms will be able to solve this important problem for identification of newborns. The contributions of this research are (i) proposed method, which mitigates the effect of the covariates of newborn face; (ii) providing an analytical and experimental underpinning effect of different facial expressions exhibited by newborns; (iii) preparing newborn face database of 280 individuals with slight variations in pose, illumination and expression. The accuracy of the proposed matching algorithms on the newborn face database with neutral expression is 87.04% and this negates the notion that all newborns look alike.

Can massive but passive exposure to faces contribute to face recognition abilities?

Recent studies have suggested that individuation of other-race faces is more crucial for enhancing recognition performance than exposure that involves categorization of these faces to an identity-irrelevant criterion. These findings were primarily based on laboratory training protocols that dissociated exposure and individuation by using categorization tasks. However, the absence of enhanced recognition following categorization may not simulate key aspects of real-life massive exposure without individuation to other-race faces. Real-life exposure spans years of seeing a multitude of faces, under variant conditions, including expression, view, lighting and gaze, albeit with no subcategory individuation. However, in most real-life settings, massive exposure operates in concert with individuation. An exception to that are neonatology nurses, a unique population that is exposed to--but do not individuate--massive numbers of newborn faces. Our findings show that recognition of newborn faces by nurses does not differ from adults who are rarely exposed to newborn faces. A control study showed that the absence of enhanced recognition cannot be attributed to the relatively short exposure to each newborn face in the neonatology unit or to newborns' apparent homogeneous appearance. It is therefore the quality--not the quantity--of exposure that determines recognition abilities.

Importance of Muscle Movement for Normal Craniofacial Development

After the craniofacial structures have completed embryologic development, movement of facial muscles begins. Paraxial mesoderm of the first (mastication) and second pharyngeal (facial expression) arches gives rise to the muscles of the craniofacial area. Muscles derived from the third and fourth pharyngeal arches are involved in swallowing and vocalization. For the human newborn face to have a normal morphologic appearance, contractions of these muscles must occur to stimulate forward growth of bone, cartilage growth, and facial muscle bulk. Facial muscles begin to contract between 6 and 8 weeks of embryonic development and can be observed on prenatal ultrasound by 9 weeks after fertilization. Lack of craniofacial muscle contractions may lead to ocular hypertelorism, flat zygoma and midface, high bridge of the nose, depressed tip of the nose, small and open mouth, trismus, microretrognathia, small tongue, and abnormal palate (high arch, bifid uvula, submucous cleft, and cleft palate).

Innate immunity of the newborn: basic mechanisms and clinical correlates

The fetus and newborn face a complex set of immunological demands, including protection against infection, avoidance of harmful inflammatory immune responses that can lead to pre-term delivery, and balancing the transition from a sterile intra-uterine environment to a world that is rich in foreign antigens. These demands shape a distinct neonatal innate immune system that is biased against the production of pro-inflammatory cytokines. This bias renders newborns at risk of infection and impairs responses to many vaccines. This Review describes innate immunity in newborns and discusses how this knowledge might be used to prevent and treat infection in this vulnerable population.

Eating Your Heart Out

Kuma et al. have uncovered an intriguing role for autophagy--a stress response in which cells sequester and then cannibalize portions of their own cytoplasm--in the transition from survival as a fetus to neonatal life (see Heintz). Newborn mammals face many challenges: Among these is maintaining an adequate supply of nutrients during the interval between losing placental support and starting to nurse. Using a fluorescently labeled molecular marker for autophagosomes, Kumo et al. showed that, after birth, there was a dramatic increase in autophagy in mouse heart muscle and diaphragm (whose energy requirements rise after birth), as well as in alveolar cells and skin (whose external environment changes). This increase in autophagosome number was apparent within 30 min of birth, was maximal at 3 to 6 hours (by which time suckling had started), and subsided within a day or two. Mice lacking the gene encoding Atg5 (a protein required for autophagy) appeared nearly normal at birth but died sooner than wild-type mice under nonsuckling conditions. Plasma and tissue amino acid concentration of Atg –/– mice was normal at delivery but lower then that of wild-type mice by 10 hours. Further, the activity of the energy sensor AMP-activated kinase (AMPK) was stimulated by a 10-hour fast in Atg –/– mice, but not in wild-type mice, indicating an increase in the AMP:ATP ratio (and thus energy depletion). Thus, amino acid production through the autophagic pathway appears to play a key role in early energy homeostasis. A. Kuma, M. Hatano, M. Matsui, A. Yamamoto, H. Nakaya, T. Yoshimori, Y. Ohsumi, T. Tokuhisa, N. Mizushima, The role of autophagy during the early neonatal starvation period. Nature 432 , 1032-1036 (2004). [PubMed] N. Heintz, Survival by self-digestion. Nature 432 , 963 (2004). [PubMed]

Learning Innate Face Preferences

Newborn humans preferentially orient to facelike patterns at birth, but months of experience with faces are required for full face processing abilities to develop. Several models have been proposed for how the interaction of genetic and environmental influences can explain these data. These models generally assume that the brain areas responsible for newborn orienting responses are not capable of learning and are physically separate from those that later learn from real faces. However, it has been difficult to reconcile these models with recent discoveries of face learning in newborns and young infants. We propose a general mechanism by which genetically specified and environment-driven preferences can coexist in the same visual areas. In particular, newborn face orienting may be the result of prenatal exposure of a learning system to internally generated input patterns, such as those found in PGO waves during REM sleep. Simulating this process with the HLISSOM biological model of the visual system, we demonstrate that the combination of learning and internal patterns is an efficient way to specify and develop circuitry for face perception. This prenatal learning can account for the newborn preferences for schematic and photographic images of faces, providing a computational explanation for how genetic influences interact with experience to construct a complex adaptive system.

Self‐scratching injuries on the newborn's face

Self‐scratching injuries (SI) on the face were studied in 300 newborns in order to know how they are made. Of the 300 infants studied, 298 had the scratches. Scored SI (SI score) according to the grades of the injury were compared in each of nine different subject groups into which the infants were divided on the basis of their birth weights, gestational ages, Apgar scores, modes of delivery, and the presence or absence of delivery complications. Each of the mean SI scores of the low birth weight infants, the preterm infants, the infants with a certain delivery complication, and of the infants delivered by a cesarean section was significantly lower than that of the healthy and matured infants. It is thus concluded that the scratching injuries are attributed to normal neonatal movements and the degree of injuries may reflect the maturity and the physical activity of the newborn.