Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features

The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In our previous work, we explored the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal. The classification was based upon the GMA, which was carried out on the video data by an independent expert reviewer. In this paper we extend our previous work by extracting the normalised pose-based feature sets, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for use in new deep learning architectures. We explore the viability of using these pose-based feature sets for automated classification within a deep learning framework by carrying out extensive experiments on five new deep learning architectures. Experimental results show that the proposed fully connected neural network FCNet performed robustly across different feature sets. Furthermore, the proposed convolutional neural network architectures demonstrated excellent performance in handling features in higher dimensionality. We make the code, extracted features and associated GMA labels publicly available

Establishing Pose Based Features Using Histograms for the Detection of Abnormal Infant Movements

The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In this paper, we conducted a pilot study on extracting important information from video sequences to classify the body movement into two categories, normal and abnormal, and compared the results provided by an independent expert reviewer based on GMA. We present two new pose-based features, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for the pose-based analysis and classification of infant body movement from video footage. We extract the 2D skeletal joint locations from 2D RGB images using Cao et al.’s method 1. Using the MINI-RGBD dataset 2, we further segment the body into local regions to extract part specific features. As a result, the pose and the degree of displacement are represented by histograms of normalised data. To demonstrate the effectiveness of the proposed features, we trained several classifiers using combinations of HOJO2D and HOJD2D features and conducted a series of experiments to classify the body movement into categories. The classification algorithms used included k-Nearest Neighbour (kNN, k=1 and k=3), Linear Discriminant Analysis (LDA) and the Ensemble classifier. Encouraging results were attained, with high accuracy (91.67{\%}) obtained using the Ensemble classifier

Preterm gut microbiota and metabolome following discharge from intensive care

The development of the preterm gut microbiome is important for immediate and longer-term health following birth. We aimed to determine if modifications to the preterm gut on the neonatal intensive care unit (NICU) impacted the gut microbiota and metabolome long-term. Stool samples were collected from 29 infants ages 1–3 years post discharge (PD) from a single NICU. Additional NICU samples were included from 14/29 infants. Being diagnosed with disease or receiving increased antibiotics while on the NICU did not significantly impact the microbiome PD. Significant decreases in common NICU organisms including K. oxytoca and E. faecalis and increases in common adult organisms including Akkermansia sp., Blautia sp., and Bacteroides sp. and significantly different Shannon diversity was shown between NICU and PD samples. The metabolome increased in complexity, but while PD samples had unique bacterial profiles we observed comparable metabolomic profiles. The preterm gut microbiome is able to develop complexity comparable to healthy term infants despite limited environmental exposures, high levels of antibiotic administration, and of the presence of serious disease. Further work is needed to establish the direct effect of weaning as a key event in promoting future gut health

Nutrient-enriched formula versus standard formula for preterm infants

BACKGROUND: Preterm infants may accumulate nutrient deficits leading to extrauterine growth restriction. Feeding preterm infants with nutrient-enriched rather than standard formula might increase nutrient accretion and growth rates and might improve neurodevelopmental outcomes. OBJECTIVES: To compare the effects of feeding with nutrient-enriched formula versus standard formula on growth and development of preterm infants. SEARCH METHODS: We used the Cochrane Neonatal standard search strategy. This included electronic searches of the Cochrane Central Register of Controlled Trials (CENTRAL; 2018, Issue 11), MEDLINE, Embase, and the Cumulative Index to Nursing and Allied Health Literature (until November 2018), as well as conference proceedings, previous reviews, and clinical trials databases. SELECTION CRITERIA: Randomised and quasi-randomised controlled trials that compared feeding preterm infants with nutrient-enriched formula (protein and energy plus minerals, vitamins, or other nutrients) versus standard formula. DATA COLLECTION AND ANALYSIS: We extracted data using the Cochrane Neonatal standard methods. Two review authors separately evaluated trial quality and extracted and synthesised data using risk ratios (RRs), risk differences, and mean differences (MDs). We assessed certainty of evidence at the outcome level using Grading of Recommendations Assessment, Development and Evaluation (GRADE) methods. MAIN RESULTS: We identified seven trials in which a total of 590 preterm infants participated. Most participants were clinically stable preterm infants of birth weight less than 1850 g. Few participants were extremely preterm, extremely low birth weight, or growth restricted at birth. Trials were conducted more than 30 years ago, were formula industry funded, and were small with methodological weaknesses (including lack of masking) that might bias effect estimates. Meta-analyses of in-hospital growth parameters were limited by statistical heterogeneity. There is no evidence of an effect on time to regain birth weight (MD -1.48 days, 95% confidence interval (CI) -4.73 to 1.77) and low-certainty evidence suggests that feeding with nutrient-enriched formula increases in-hospital rates of weight gain (MD 2.43 g/kg/d, 95% CI 1.60 to 3.26) and head circumference growth (MD 1.04 mm/week, 95% CI 0.18 to 1.89). Meta-analysis did not show an effect on the average rate of length gain (MD 0.22 mm/week, 95% CI -0.70 to 1.13). Fewer data are available for growth and developmental outcomes assessed beyond infancy, and these do not show consistent effects of nutrient-enriched formula feeding. Data from two trials did not show an effect on Bayley Mental Development Index scores at 18 months post term (MD 2.87, 95% CI -1.38 to 7.12; moderate-certainty evidence). Infants who received nutrient-enriched formula had higher Bayley Psychomotor Development Index scores at 18 months post term (MD 6.56. 95% CI 2.87 to 10.26; low-certainty evidence), but no evidence suggested an effect on cerebral palsy (typical RR 0.79, 95% CI 0.30 to 2.07; 2 studies, 377 infants). Available data did not indicate any other benefits or harms and provided low-certainty evidence about the effect of nutrient-enriched formula feeding on the risk of necrotising enterocolitis in preterm infants (typical RR 0.72, 95% CI 0.41 to 1.25; 3 studies, 489 infants). AUTHORS' CONCLUSIONS: Available trial data show that feeding preterm infants nutrient-enriched (compared with standard) formulas has only modest effects on growth rates during their initial hospital admission. No evidence suggests effects on long-term growth or development. The GRADE assessment indicates that the certainty of this evidence is low, and that these findings should be interpreted and applied with caution. Further randomised trials would be needed to resolve this uncertainty

Improved Cauchy radius for scalar and matrix polynomials

We improve the Cauchy radius of both scalar and matrix polynomials, which is an upper bound on the moduli of the zeros and eigenvalues, respectively, by using appropriate polynomial multipliers.Comment: 12 page

Bone Mineral Density and Osteoporosis after Preterm Birth: The Role of Early Life Factors and Nutrition

The effects of preterm birth and perinatal events on bone health in later life remain largely unknown. Bone mineral density (BMD) and osteoporosis risk may be programmed by early life factors. We summarise the existing literature relating to the effects of prematurity on adult BMD and the Developmental Origins of Health and Disease hypothesis and programming of bone growth. Metabolic bone disease of prematurity and the influence of epigenetics on bone metabolism are discussed and current evidence regarding the effects of breastfeeding and aluminium exposure on bone metabolism is summarised. This review highlights the need for further research into modifiable early life factors and their effect on long-term bone health after preterm birth

A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants

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, the prospect of automating these processes may improve accessibility of the assessment and also enhance the understanding of movement development of infants. Previous works have established 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 series of new and improved features, and a feature fusion pipeline for this classification task. We also introduce the RVI-38 dataset, a series of videos captured as part of routine clinical care. By utilising this challenging dataset we establish the robustness of several motion features for classification, subsequently informing the design of our proposed feature fusion framework based upon the GMA. We evaluate our proposed framework’s classification performance using both the RVI-38 dataset and the publicly available MINI-RGBD dataset. We also implement several other methods from the literature for direct comparison using these two independent datasets. Our experimental results and feature analysis show that our proposed pose-based method performs well across both datasets. The proposed features afford us the opportunity to include finer detail than previous methods, and further model GMA specific body movements. These new features also allow us to take advantage of additional body-part specific information as a means of improving the overall classification performance, whilst retaining GMA relevant, interpretable, and shareable features

Probiotics for preterm infants and the recent FDA alert in the USA

The US Food and Drug Administration (FDA) recently issued a warning regarding probiotic use in preterm infants1 leading to the withdrawal of at least two products in the USA. Product withdrawal may extend to Europe, if companies become unwilling to risk legal challenges for products that may not generate significant profits. The FDA alert highlighted the risk of € invasive, potentially fatal' sepsis with probiotics, and the unregulated nature of the market. Product marketing may have implied probiotics reduce necrotising enterocolitis (NEC), when such a claim can only be made with high-quality randomised controlled trial (RCT) evidence. Despite a plethora of RCTs and meta-analyses (MAs), no RCT with NEC as a primary outcome has shown benefit, nor has any RCT been powered with NEC as the primary outcome. This is disappointing as NEC is responsible for more than 1:20 child deaths before age 10 years.2 Nevertheless, most neonatal medicine practice is based on MAs, observational studies and clinician experience rather than definitive RCTs

Optimisation and application of a novel method to identify bacteriophage in maternal milk and infant stool identifies host-phage communities within preterm infant gut

Human milk oligosaccharides, proteins such as lactoferrin, and bacteria represent just some of the bioactive components of mothers’ breast milk (BM). Bacteriophages (viruses that infect bacteria) are an often-overlooked component of BM that can cause major changes in microbial composition and metabolism. BM bacteriophage composition has been explored in term and healthy infants, suggesting vertical transmission of bacteriophages occurs between mothers and their infants. Several important differences between term and very preterm infants (< 30 weeks gestational age) may limit this phenomenon in the latter. To better understand the link between BM bacteriophages and gut microbiomes of very preterm infants in health and disease, standardised protocols are required for isolation and characterisation from BM. In this study we use isolated nucleic acid content, bacteriophage richness and Shannon diversity to validate several parameters applicable during bacteriophage isolation from precious BM samples. Parameters validated include sample volume required; centrifugal sedimentation of microbes; hydrolysis of milk samples with digestive enzymes; induction of temperate bacteriophages and concentration / purification of isolated bacteriophage particles in donor milk (DM). Our optimised method enables characterisation of bacteriophages from as little as 0.1 mL BM. We identify viral families that were exclusively identified with inclusion of induction of temperate bacteriophages (Inoviridae) and hydrolysis of milk lipid processes (Iridoviridae and Baculoviridae). Once applied to a small clinical cohort we demonstrate vertical transmission of bacteriophages from mothers BM to the gut of very preterm infants at the species level. This optimised method will enable future research characterising the bacteriophage composition of BM in very preterm infants to determine their clinical relevance in the development of a healthy preterm infant gut microbiome