Continuous sensing and quantification of body motion in infants:A systematic review

Abnormal body motion in infants may be associated with neurodevelopmental delay or critical illness. In contrast to continuous patient monitoring of the basic vitals, the body motion of infants is only determined by discrete periodic clinical observations of caregivers, leaving the infants unattended for observation for a longer time. One step to fill this gap is to introduce and compare different sensing technologies that are suitable for continuous infant body motion quantification. Therefore, we conducted this systematic review for infant body motion quantification based on the PRISMA method (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this systematic review, we introduce and compare several sensing technologies with motion quantification in different clinical applications. We discuss the pros and cons of each sensing technology for motion quantification. Additionally, we highlight the clinical value and prospects of infant motion monitoring. Finally, we provide suggestions with specific needs in clinical practice, which can be referred by clinical users for their implementation. Our findings suggest that motion quantification can improve the performance of vital sign monitoring, and can provide clinical value to the diagnosis of complications in infants.</p

Towards Explainable Abnormal Infant Movements Identification: A Body-part Based Prediction and Visualisation Framework

Providing early diagnosis of cerebral palsy (CP) is key to enhancing the developmental outcomes for those affected. Diagnostic tools such as the General Movements Assessment (GMA), have produced promising results in early diagnosis, however these manual methods can be laborious. In this paper, we propose a new framework for the automated classification of infant body movements, based upon the GMA, which unlike previous methods, also incorporates a visualization framework to aid with interpretability. Our proposed framework segments extracted features to detect the presence of Fidgety Movements (FMs) associated with the GMA spatiotemporally. These features are then used to identify the body-parts with the greatest contribution towards a classification decision and highlight the related body-part segment providing visual feedback to the user. We quantitatively compare the proposed framework's classification performance with several other methods from the literature and qualitatively evaluate the visualization's veracity. Our experimental results show that the proposed method performs more robustly than comparable techniques in this setting whilst simultaneously providing relevant visual interpretability

Towards human-level performance on automatic pose estimation of infant spontaneous movements

Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments and joints by infant pose estimation is required. Four types of convolutional neural networks were trained and evaluated on a novel infant pose dataset, covering the large variation in 1 424 videos from a clinical international community. The localization performance of the networks was evaluated as the deviation between the estimated keypoint positions and human expert annotations. The computational efficiency was also assessed to determine the feasibility of the neural networks in clinical practice. The best performing neural network had a similar localization error to the inter-rater spread of human expert annotations, while still operating efficiently. Overall, the results of our study show that pose estimation of infant spontaneous movements has a great potential to support research initiatives on early detection of developmental disorders in children with perinatal brain injuries by quantifying infant movements from video recordings with human-level performance.Comment: Published in Computerized Medical Imaging and Graphics (CMIG

Automated early prediction of cerebral palsy: interpretable pose-based assessment for the identification of abnormal infant movements

Cerebral Palsy (CP) is currently the most common chronic motor disability occurring in infants, affecting an estimated 1 in every 400 babies born in the UK each year. Techniques which can lead to an early diagnosis of CP have therefore been an active area of research, with some very promising results using tools such as the General Movements Assessment (GMA). By using video recordings of infant motor activity, assessors are able to classify an infant’s neurodevelopmental status based upon specific characteristics of the observed infant movement. However, these assessments are heavily dependent upon the availability of highly skilled assessors. As such, we explore the feasibility of the automated prediction of CP using machine learning techniques to analyse infant motion. We examine the viability of several new pose-based features for the analysis and classification of infant body movement from video footage. We extensively evaluate the effectiveness of the extracted features using several proposed classification frameworks, and also reimplement the leading methods from the literature for direct comparison using shared datasets to establish a new state-of-the-art. We introduce the RVI-38 video dataset, which we use to further inform the design, and establish the robustness of our proposed complementary pose-based motion features. Finally, given the importance of explainable AI for clinical applications, we propose a new classification framework which also incorporates a visualisation module to further aid with interpretability. Our proposed pose-based framework segments extracted features to detect movement abnormalities spatiotemporally, allowing us to identify and highlight body-parts exhibiting abnormal movement characteristics, subsequently providing intuitive feedback to clinicians. We suggest that our novel pose-based methods offer significant benefits over other approaches in both the analysis of infant motion and explainability of the associated data. Our engineered features, which are directly mapped to the assessment criteria in the clinical guidelines, demonstrate state-of-the-art performance across multiple datasets; and our feature extraction methods and associated visualisations significantly improve upon model interpretability

Punctate White Matter Lesions Associated With Altered Brain Development And Adverse Motor Outcome In Preterm Infants.

Preterm infants who develop neurodevelopmental impairment do not always have recognized abnormalities on cerebral ultrasound, a modality routinely used to assess prognosis. In a high proportion of infants, MRI detects punctate white matter lesions that are not seen on ultrasonography. To determine the relation of punctate lesions to brain development and early neurodevelopmental outcome we used multimodal brain MRI to study a large cohort of preterm infants. Punctate lesions without other focal cerebral or cerebellar lesions were detected at term equivalent age in 123 (24.3%) (59 male) of the 506 infants, predominantly in the centrum semiovale and corona radiata. Infants with lesions had higher gestational age, birth weight, and less chronic lung disease. Punctate lesions showed a dose dependent relation to abnormalities in white matter microstructure, assessed with tract-based spatial statistics, and reduced thalamic volume (p < 0.0001), and predicted unfavourable motor outcome at a median (range) corrected age of 20.2 (18.4-26.3) months with sensitivity (95% confidence intervals) 71 (43-88) and specificity 72 (69-77). Punctate white matter lesions without associated cerebral lesions are common in preterm infants currently not regarded as at highest risk for cerebral injury, and are associated with widespread neuroanatomical abnormalities and adverse early neurodevelopmental outcome

Identification of Abnormal Movements in Infants: A Deep Neural Network for Body Part-Based Prediction of Cerebral Palsy

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results, however these manual methods can be laborious. The prospect of automating these processes is seen as key in advancing this field of study. In our previous works, we examined the viability of using pose-based features extracted from RGB video sequences to undertake classification of infant body movements based upon the GMA. In this paper, we propose a new deep learning framework for this classification task. We also propose a visualization framework which identifies body-parts with the greatest contribution towards a classification decision. The inclusion of a visualization framework is an important step towards automation as it helps make the decisions made by the machine learning framework interpretable. We directly compare the proposed framework's classification with several other methods from the literature using two independent datasets. Our experimental results show that the proposed method performs more consistently and more robustly than our previous pose-based techniques as well as other features from related works in this setting. We also find that our visualization framework helps provide greater interpretability, enhancing the likelihood of the adoption of these technologies within the medical domain