Spontaneous movements in the newborns: a tool of quantitative video analysis of preterm babies

NeoCam: An Edge-Cloud Platform for Non-Invasive Real-Time Monitoring in Neonatal Intensive Care Units

In this work we introduce NeoCam, an open source hardware-software platform for video-based monitoring of preterms infants in Neonatal Intensive Care Units (NICUs). NeoCam includes an edge computing device that performs video acquisition and processing in real-time. Compared to other proposed solutions, it has the advantage of handling data more efficiently by performing most of the processing on the device, including proper anonymisation for better compliance with privacy regulations. In addition, it allows to perform various video analysis tasks of clinical interest in parallel at speeds of between 20 and 30 frames-per-second. We introduce algorithms to measure without contact the breathing rate, motor activity, body pose and emotional status of the infants. For breathing rate, our system shows good agreement with existing methods provided there is sufficient light and proper imaging conditions. Models for motor activity and stress detection are new to the best of our knowledge. NeoCam has been tested on preterms in the NICU of the University Hospital Puerta del Mar (Cádiz, Spain), and we report the lessons learned from this trial.

Automated postural asymmetry assessment in infants neurodevelopmental evaluation using novel video-based features

BACKGROUND AND OBJECTIVE

Neurodevelopmental assessment enables the identification of infant developmental disorders in the first months of life. Thus, the appropriate therapy can be initiated promptly, increasing the chances for correct motor function. Posture asymmetry is one of the crucial aspects evaluated during the diagnosis. Available diagnostic methods are mainly based on qualitative assessment and subjective expert opinion. Current trends in computer-aided diagnosis focus mostly on analyzing infants' spontaneous movement videos using artificial intelligence methods, based primarily on limbs movement. This study aims to develop an automatic method for determining the infant's positional asymmetry in a video recording using computer image processing methods.

METHODS

We made the first attempt to determine positional preferences in a recording automatically. We proposed six quantitative features describing trunk and head position based on pose estimation. As a result of our algorithm, we estimate the percentage of each trunk position in a recording using known machine learning methods. The training and test sets were created from 51 recordings collected during our research and 12 recordings from the benchmark dataset evaluated by five of our experts. The method was assessed using the leave-one-subject-out cross-validation method for ground truth video fragments and different classifiers. Log loss for multiclass classification and ROC AUC were determined to evaluate the results for both our and benchmark datasets.

RESULTS

In a classification of the shortened side, the QDA classifier yields the most accurate results, gaining the lowest log loss of 0.552 and AUC of 0.913. The high accuracy (92.03) and sensitivity (93.26) confirm the method's potential in screening for asymmetry.

CONCLUSIONS

The method allows obtaining quantitative information about positional preference, a valuable extension of basic diagnostics without additional tools and procedures. In combination with an analysis of limbs movement, it may constitute one of the elements of a novelty computer-aided infants' diagnosis system in the future.

Design and Construct Validity of a Postural Control Test for Pre-Term Infants

A review of the literature indicated that the greatest prognostic value for predicting motor impairment in high-risk infants is the absence of fidgety movements (FMs) at 3 months of post-term age. The purpose of the present study was to characterize a new posturometric test (PT) based on a center-of-pressure (CoP) movement analysis, in terms of design and construct validity, for the detection of postural control disturbances in pre-term infants. The comparative studies were carried out between pre-term infants who presented normal FMs (18 participants) and infants with absent FMs (19 participants), which consisted of the analysis of the CoP trajectory and CoP area in supine and prone positions using the force platform. New PT was performed simultaneously with GMs recorded using a force platform. Statistical analyses revealed significant differences between the groups of infants who presented absent FMs and normal FMs for almost all CoP parameters describing spontaneous sway in the supine position. Based on these preliminary results, it can be concluded, that the application of PT based on the analysis of CoP trajectory, area, and velocity in the supine position has been demonstrated to be valid for the detection of postural control disturbances in pre-term infants.

A markerless pipeline to analyze spontaneous movements of preterm infants

BACKGROUND AND OBJECTIVE

The analysis of spontaneous movements of preterm infants is important because anomalous motion patterns can be a sign of neurological disorders caused by lesions in the developing brain. A diagnosis in the first weeks of child's life is crucial to plan timely and appropriate rehabilitative interventions. An accurate visual assessment of infants' spontaneous movements requires highly specialized personnel, not always available, and it is operator dependent. Motion capture systems, markers and wearable sensors are commonly used for human motion analysis, but they can be cumbersome, limiting their use in the study of infants' movements.

METHODS

In this paper we propose a computer-aided pipeline to characterize and classify infants' motion from 2D video recordings. The final goal is detecting anomalous motion patterns. The implemented pipeline is based on computer vision and machine learning algorithms and includes a specific step to increase the interpretability of the results. Specifically, it can be summarized by the following steps: (i) body keypoints detection: we rely on a deep learning-based semantic features detector to localize the positions of meaningful landmark points on infants' bodies; (ii) parameters extraction: starting from the trajectories of the detected landmark points, we extract quantitative parameters describing infants motion patterns; (iii) classification: we implement different classifiers (Support Vector Machines, Random Forest, fully connected Neural Network, Long Short Term Memory) that, starting from the motion parameters, classify between normal or abnormal motion patterns.

RESULTS

We tested the proposed pipeline on a dataset, recorded at the 40th gestational week, of 142 infants, 59 with evidence of neuromotor disorders according to a medical assessment carried out a posteriori. Our procedure successfully discriminates normal and anomalous motion patterns with a maximum accuracy of 85.7%.

CONCLUSIONS

In conclusion, our pipeline has the potential to be adopted as a tool to support the early detection of abnormal motion patterns in preterm infants.

Prediction of autistic tendencies at 18 months of age via markerless video analysis of spontaneous body movements in 4-month-old infants

Early intervention is now considered the core treatment strategy for autism spectrum disorders (ASD). Thus, it is of significant clinical importance to establish a screening tool for the early detection of ASD in infants. To achieve this goal, in a longitudinal design, we analyzed spontaneous bodily movements of 4-month-old infants from general population and assessed their ASD-like behaviors at 18 months of age. A total of 26 movement features were calculated from video-recorded bodily movements of infants at 4 months of age. Their risk of ASD was assessed at 18 months of age with the Modified Checklist for Autism in Toddlerhood, a widely used screening questionnaire. Infants at high risk for ASD at 18 months of age exhibited less rhythmic and weaker bodily movement patterns at 4 months of age than low-risk infants. When the observed bodily movement patterns were submitted to a machine learning-based analysis, linear and non-linear classifiers successfully predicted ASD-like behavior at 18 months of age based on the bodily movement patterns at 4 months of age, at the level acceptable for practical use. This study analyzed the relationship between spontaneous bodily movements at 4 months of age and the ASD risk at 18 months of age. Experimental results suggested the utility of the proposed method for the early screening of infants at risk for ASD. We revealed that the signs of ASD risk could be detected as early as 4 months after birth, by focusing on the infant's spontaneous bodily movements.

Why we should care about full-term infants admitted to a neonatal intensive care unit

ABSTRACT This study aims to analyze why we should care about full-term newborns admitted to a neonatal intensive care unit. This is a documented, descriptive, and retrospective study of 262 full-term newborns. Variables used: newborns’ characteristics; main diagnosis, length of stay, follow-up by a multidisciplinary team; post-discharge referral. Most newborns were boys (52%), had a 5-minute Apgar score of nine, and most newborns and their mothers were white (61.1% and 48.9% respectively). Respiratory dysfunction was the main diagnosis (28.8%). Length of stay was eight days. There was a significant difference regarding length of stay (p=0.013), in which those with cardiorespiratory and other diseases stayed less time compared to those with malformation or maternal diseases. The social service was the most sought (81.2%) service, whereas physical therapy the least sought (18%). Newborns with higher weight were hospitalized for less time. Those that underwent physical therapy had longer stay (p<0.001). Main outcome was hospital discharge (68.7%) and referrals to the Basic Health Unit (57%). This study outcomes indicated newborns with less severe conditions, low number of specific studies for the full-term population, other diagnoses that refer to non-intensive care.

Por que devemos nos preocupar com os bebês a termo internados em uma unidade de terapia intensiva neonatal

RESUMO O objetivo deste estudo foi analisar a razão pela qual devemos nos preocuparmos com os bebês a termo internados em uma unidade de terapia intensiva neonatal. Trata-se de estudo documental, descritivo e retrospectivo de 262 recém-nascidos (RNs) a termo. As variáveis utilizadas foram: características dos RN; diagnóstico principal, tempo de permanência e acompanhamento pela equipe multiprofissional; e encaminhamento pós-alta. Houve prevalência do sexo masculino (52%), de Apgar 9 no 5º minuto e da raça/cor branca do RN e da mãe (61,1% e 48,9%, respectivamente). O diagnóstico principal foi a disfunção respiratória (28,8%), e o tempo de permanência foi de oito dias. Houve diferença significativa entre os tempos de permanência (p=0,013), em que as doenças cardiorrespiratórias e outras doenças levaram a um menor tempo de internação em relação à má formação ou às doenças maternas. O serviço social foi o mais procurado para o acompanhamento (81,2%) e a fisioterapia, o menos buscado (18%). RNs com maior peso ficaram menos tempo internados, e os acompanhados por fisioterapia apresentaram tempo de permanência mais elevados (p<0,001). O principal desfecho foi a alta hospitalar (68,7%) e encaminhamentos para a Unidade Básica de Saúde (57%). Os achados deste estudo apontam a presença de bebês menos graves, baixo número de estudos específicos para a população a termo e outros diagnósticos que nos remetem a cuidados não intensivos.

Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study

The General Movements Assessment requires extensive training. As an alternative, a novel automated movement analysis was developed and validated in preterm infants. Infants < 31 weeks’ gestational age or birthweight ≤ 1500 g evaluated at 3−5 months using the general movements assessment were included in this ambispective cohort study. The C-statistic, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for a predictive model. A total of 252 participants were included. The median gestational age and birthweight were 274/7 weeks (range 256/7−292/7 weeks) and 960 g (range 769−1215 g), respectively. There were 29 cases of cerebral palsy (11.5%) at 18−24 months, the majority of which (n = 22) were from the retrospective cohort. Mean velocity in the vertical direction, median, standard deviation, and minimum quantity of motion constituted the multivariable model used to predict cerebral palsy. Sensitivity, specificity, positive, and negative predictive values were 55%, 80%, 26%, and 93%, respectively. C-statistic indicated good fit (C = 0.74). A cluster of four variables describing quantity of motion and variability of motion was able to predict cerebral palsy with high specificity and negative predictive value. This technology may be useful for screening purposes in very preterm infants; although, the technology likely requires further validation in preterm and high-risk term populations.

Spatial, But Not Temporal, Kinematics of Spontaneous Upper Extremity Movements Are Related to Gross and Fine Motor Skill Attainment in Infancy

Background: Spontaneous upper extremity movements in infancy provide insight on neuromotor development. Spatiotemporal kinematics have been used to evaluate typical development of reaching, a foundational motor skill in infancy. This study evaluates the relationship between spontaneous upper extremity movements, not elicited by a toy, and motor skill attainment. Methods: N = 12 healthy infants (2–8 months) participated in this longitudinal study (one to four sessions). Motor skills were assessed with the Bayley Scales of Infant and Toddler Development, 3rd Edition: gross motor subtest (GM) and fine motor subtest. Spontaneous upper extremity movements were collected using 3D motion capture technology. Infants were placed in supine for three to twelve 30-s trials, and their movements were recorded. Repeated measure correlation coefficients (Rmcorr) were used to evaluate relationships between variables. Results: There were significant, moderate, positive relationships between the straight distance from start to end of a movement and (a) fine motor score (Rmcorr = .55, p = .03), (b) GM score (Rmcorr = .63, p = .01), and (c) age (Rmcorr = .56, p = .02). There was a significant, moderate, negative relationship between straightness ratio and GM score (Rmcorr = −.52, p = .047). Discussion: Fine and GM skills are related to the straight distance from start to end of a movement and the straightness ratio of underlying spontaneous upper extremity movements.