ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases of Trypanosoma cruzi Infection in a Murine Model.

Chagas disease (CD) is a neglected parasitic disease caused by the protozoan Trypanosoma cruzi (T. cruzi). The disease has two clinical phases: acute and chronic. In the acute phase, the parasite circulates in the blood. The infection can be asymptomatic or can cause unspecific clinical symptoms. During the chronic phase, the infection can cause electrical conduction abnormalities and progress to cardiac failure. The use of an electrocardiogram (ECG) has been a methodology for diagnosing and monitoring CD, but it is necessary to study the ECG signals to better understand the behavior of the disease. The aim of this study is to analyze different ECG markers using machine-learning-based algorithms for the classification of the acute and chronic phases of T. cruzi infection in a murine experimental model. The presented methodology includes a statistical analysis of control vs. infected models in both phases, followed by an automatic selection of ECG descriptors and the implementation of several machine learning algorithms for the automatic classification of control vs. infected mice in acute and/or chronic phases (binomial classification), as well as a multiclass classification strategy (control vs. the acute group vs. the chronic group). Feature selection analysis showed that P wave duration, R and P wave voltages, and the QRS complex are some of the most important descriptors. The classifiers showed good results in detecting the acute phase of infection (with an accuracy of 87.5%), as well as in multiclass classification (control vs. the acute group vs. the chronic group), with an accuracy of 91.3%. These results suggest that it is possible to detect infection at different phases, which can help in experimental and clinical studies of CD.

Administración de surfactante mediante técnica mínimamente invasiva en neonatos / Administration of Surfactants by Minimally Invasive Technique in Neonates

Introducción: La administración de surfactante pulmonar tradicionalmente se realiza mediante un tubo endotraqueal, pero desde hace años existen técnicas menos invasivas como la administración mediante másscara laríngea, aerosolización y cateterización traqueal. Objetivos: Demostrar la evolución de tres neonatos que recibieron surfactante pulmonar mediante una cateterización traqueal y describir la técnica empleada para su administración. Presentación de casos: Se atendieron tres recién nacidos de muy bajo peso al nacer, que ingresaron en la unidad de cuidados intensivos neonatales del Hospital General Docente Iván Portuondo, San Antonio de los Baños, con síndrome de dificultad respiratoria del prematuro. Todos se trataron con surfactante pulmonar exógeno, Surfacen®, el cual se administró mediante cateterización traqueal empleando un catéter umbilical. Se trata de una técnica mínimamente invasiva que se realizó sin dificultades y siempre en el primer intento. Los tres pacientes mostraron mejoría clínica, gasométrica y radiográfica con esta forma de administración y solo uno de ellos tuvo una complicación durante el proceder, que no constituyó una limitante para su realización. Este método permitió mantener una ventilación no invasiva, y fue innecesaria la intubación endotraqueal en los neonatos. Los profesionales encargados de la ejecución de esta técnica recibieron entrenamiento previo. Conclusiones: La administración mínimamente invasiva de surfactante pulmonar resultó un método eficaz con el que se consiguió la resolución total del cuadro de dificultad respiratoria en los neonatos. El procedimiento empleado permitió una administración rápida y segura del Surfacen®(AU)

Introduction: Pulmonary surfactant administration is traditionally performed by endotracheal tube, but for years there have been less invasive techniques such as administration by laryngeal mask, aerosolization and tracheal catheterization. Objectives: To demonstrate the evolution of three neonates who received pulmonary surfactant via tracheal catheterization and to describe the technique used for its administration. Case presentation: Three very low birth weight newborns were attended and admitted to the neonatal intensive care unit of Iván Portuondo General Teaching Hospital, at San Antonio de los Baños municipality, with preterm respiratory distress syndrome. All were treated with exogenous pulmonary surfactant, Surfacen®, which was administered by tracheal catheterization using an umbilical catheter. This is a minimally invasive technique that was performed without difficulty and always on the first attempt. The three patients showed clinical, gasometric and radiographic improvement with this form of administration and only one of them had a complication during the procedure, which did not constitute a limitation for its performance. This method allowed maintaining non-invasive ventilation, and endotracheal intubation was unnecessary in neonates. The professionals in charge of performing this technique received previous training. Conclusions: Minimally invasive administration of pulmonary surfactant was an effective method that achieved total resolution of respiratory distress in neonates. The procedure used allowed rapid and safe administration of Surfacen®(AU)

Multimodal Early Birth Weight Prediction Using Multiple Kernel Learning.

In this work, a novel multimodal learning approach for early prediction of birth weight is presented. Fetal weight is one of the most relevant indicators in the assessment of fetal health status. The aim is to predict early birth weight using multimodal maternal-fetal variables from the first trimester of gestation (Anthropometric data, as well as metrics obtained from Fetal Biometry, Doppler and Maternal Ultrasound). The proposed methodology starts with the optimal selection of a subset of multimodal features using an ensemble-based approach of feature selectors. Subsequently, the selected variables feed the nonparametric Multiple Kernel Learning regression algorithm. At this stage, a set of kernels is selected and weighted to maximize performance in birth weight prediction. The proposed methodology is validated and compared with other computational learning algorithms reported in the state of the art. The obtained results (absolute error of 234 g) suggest that the proposed methodology can be useful as a tool for the early evaluation and monitoring of fetal health status through indicators such as birth weight.

The use of conformal lattice metamaterials for relieving stress in lower limb sockets: A numerical and exploratory study.

Selecting the correct material for each application has always been important. Now, with lattice metamaterials engineers can take advantage of the properties of these metamaterials to best suit a specific application. This paper investigates transtibial lower limb socket stress reduction through the implementation of conformal lattice metamaterials. In this work, a model was obtained with a 3D scanner from a plaster cast taken from a participant with a trans-tibial amputation. Then a 3D socket model was created and two conformal patterns were added to the surface of the socket using nTopology®. Parametric studies to relate the lattice metamaterials constituent elements to their effective structural properties, when such are loaded in-plane and out-of-plane were also included. Pressure test simulations were performed to determine the stresses produced in the sockets. This study concludes with discussion of the results and provides information on how surface conformal patterns can improve socket performance, showing that surface-vertex-centroid patterns increase stiffness and relieve stresses.

Estimating Echocardiographic Myocardial Strain of Left Ventricle with Deep Learning.

The global longitudinal strain of the myocardial tissue has been shown to be a better indicator of cardiac pathologies in the subclinical stage than other indices, such as the ejection fraction. This article presents a new deep learning approach for strain estimation in 2D echocardiograms. The proposed method improves the performance of the state of the art without losing stability with noisy echocardiograms and achieved an average end point error of 0.14 ± 0.17 pixels in the estimation of the optical flow in the myocardium and an error of 1.34 ± 2.34 % in the estimation of the global longitudinal strain indicator when evaluated in a synthetic echocardiographic dataset. Further research will validate the proposed method by a clinical in-vivo dataset. Clinical relevance- This paper presents a method to estimate the global longitudinal strain index in noisy echocardiograms, which promises to be a better indicator of cardiac pathologies in the subclinical stage than other indices such as the ejection fraction.

Mortalidad en neonatos de muy bajo peso al nacer / Mortality in very-low-birth-weight infants

Introducción: Los neonatos de muy bajo peso (menores de 1500 g) presentan una baja incidencia, pero una elevada morbilidad y mortalidad. Objetivo: Determinar las principales condiciones asociadas a la mortalidad en neonatos de muy bajo peso. Métodos: Estudio analítico retrospectivo (caso-control) realizado en el Hospital General Docente Iván Portuondo. Se incluyeron todos los neonatos con muy bajo peso al nacer. Se estudió el comportamiento de las variables edad materna, vía de nacimiento, género, edad gestacional, peso, puntuación de Apgar, valoración nutricional y causas de muerte, las cuales permitieron comparar vivos y fallecidos. Para la determinación de los principales riesgos se empleó el odds ratio con un intervalo de confianza del 95 por ciento. Se consideró la significación estadística con valor de odds ratio >1,0 y p < 0,05. Resultados: Del total de ingresados (156) fallecieron 22 pacientes (14,1 por ciento). El mayor riesgo de muerte estuvo en los neonatos con peso menor a 1000 gramos (odds ratio: 17,91) y edad gestacional inferior a 30 semanas (odds ratio: 3,82). Presentaron mayor riesgo de mortalidad los neonatos con hemorragia pulmonar (odds ratio: 13,3), hemorragia intraventricular (odds ratio: 9,67) y enterocolitis necrosante (odds ratio: 4,03). La principal causa de muerte en estos pacientes de alto riesgo fue la hemorragia intraventricular (27,3 por ciento). Conclusiones: La prematuridad y el bajo peso extremo constituyen los principales determinantes relacionados con mortalidad en los neonatos de muy bajo peso. La presencia de hemorragia pulmonar e intraventricular aumentan significativamente el riesgo de muerte en estos neonatos(AU)

Introduction: Very low weight neonates (less than 1500 g) have a low incidence, but a high morbidity and mortality. Objective: Determine the main conditions associated with mortality in very low weight neonates. Methods: Retrospective analytical study (case-control) carried out at Iván Portuondo General Teaching Hospital. All neonates with very low birth weight were included, comparisons were made between living and deceased. The variables maternal age, birth route, gender, gestational age, weight, Apgar score, nutritional assessment and causes of death were studied. For the determination of the main risks, the odds ratio with a 95percent confidence interval was used. Statistical significance was considered with the value of odds ratio >1.0 and p < 0.05. Results: Of 156 very low birth weight infants, 22 patients died (14.1percent), with a higher risk of death in neonates weighing less than 1000 grams (odds ratio: 17.91) and gestational age less than 30 weeks (odds ratio: 3.82). Infants with pulmonary haemorrhage (odds ratio: 13.3), intraventricular haemorrhage (odds ratio: 9.67) and necrotizing enterocolitis (odds ratio: 4.03) presented a higher risk of mortality. The leading cause of death in these high-risk patients was intraventricular hemorrhage (27.3percent). Conclusions: Prematurity and extreme low weight are the main determinants related to mortality in very low weight neonates. The presence of pulmonary and intraventricular hemorrhage significantly increases the risk of death in these infants(AU)

ARACAM: A RGB-D Multi-View Photogrammetry System for Lower Limb 3D Reconstruction Applications.

In the world, there is a growing need for lower limb prostheses due to a rising number of amputations caused primarily, by diabetic foot. Researchers enable functional and comfortable prostheses through prosthetic design by integrating new technologies applied to the traditional handcrafted method for prosthesis fabrication that is still current. That is why computer vision shows to be a promising tool for the integration of 3D reconstruction that may be useful for prosthetic design. This work has the objective to design, prototype, and test a functional system to scan plaster cast molds, which may serve as a platform for future technologies for lower limb reconstruction applications. The image capture system comprises 5 stereoscopic color and depth cameras, each with 4 DOF mountings on an enveloping frame, as well as algorithms for calibration, segmentation, registration, and surface reconstruction. The segmentation metrics of dice coefficient and Hausdorff distance (HD) show strong visual similarity with an average similarity of 87% and average error of 6.40 mm, respectively. Moving forward, the system was tested on a known 3D printed model obtained from a computer tomography scan to which comparison results via HD show an average error of ≤1.93 mm thereby making the system competitive against the systems reviewed from the state-of-the-art.

Mild cognitive impairment classification using combined structural and diffusion imaging biomarkers.

Alzheimer's disease is a multifactorial neurodegenerative disorder preceded by a prodromal stage called mild cognitive impairment (MCI). Early diagnosis of MCI is crucial for delaying the progression and optimizing the treatment. In this study we propose a random forest (RF) classifier to distinguish between MCI and healthy control subjects (HC), identifying the most relevant features computed from structural T1-weighted and diffusion-weighted magnetic resonance images (sMRI and DWI), combined with neuro-psychological scores. To train the RF we used a set of 60 subjects (HC = 30, MCI = 30) drawn from the Alzheimer's disease neuroimaging initiative database, while testing with unseen data was carried out on a 23-subjects Mexican cohort (HC = 12, MCI = 11). Features from hippocampus, thalamus and amygdala, for left and right hemispheres were fed to the RF, with the most relevant being previously selected by applying extra trees classifier and the mean decrease in impurity index. All the analyzed brain structures presented changes in sMRI and DWI features for MCI, but those computed from sMRI contribute the most to distinguish from HC. However, sMRI+DWI improves classification performance in training area under the receiver operating characteristic curve (AUROC = 93.5 ± 8%, accuracy = 88.8 ± 9%) and testing with unseen data (AUROC = 93.79%, accuracy = 91.3%), having a better performance when neuro-psychological scores were included. Compared to other classifiers the proposed RF provide the best performance for HC/MCI discrimination and the application of a feature selection step improves its performance. These findings imply that multimodal analysis gives better results than unimodal analysis and hence may be a useful tool to assist in early MCI diagnosis.

Evaluation of Brain Tortuosity Measurement for the Automatic Multimodal Classification of Subjects with Alzheimer's Disease.

The 3D tortuosity determined in several brain areas is proposed as a new morphological biomarker (BM) to be considered in early detection of Alzheimer's disease (AD). It is measured using the sum of angles method and it has proven to be sensitive to anatomical changes that appear in gray and white matter and temporal and parietal lobes during mild cognitive impairment (MCI). Statistical analysis showed significant differences (p < 0.05) between tortuosity indices determined for healthy controls (HC) vs. MCI and HC vs. AD in most of the analyzed structures. Other clinically used BMs have also been incorporated in the analysis: beta-amyloid and tau protein CSF and plasma concentrations, as well as other image-extracted parameters. A classification strategy using random forest (RF) algorithms was implemented to discriminate between three samples of the studied populations, selected from the ADNI database. Classification rates considering only image-extracted parameters show an increase of 9.17%, when tortuosity is incorporated. An enhancement of 1.67% is obtained when BMs measured from several modalities are combined with tortuosity.

Probabilistic Learning Coherent Point Drift for 3D Ultrasound Fetal Head Registration.

Quantification of brain growth is crucial for the assessment of fetal well being, for which ultrasound (US) images are the chosen clinical modality. However, they present artefacts, such as acoustic occlusion, especially after the 18th gestational week, when cranial calcification appears. Fetal US volume registration is useful in one or all of the following cases: to monitor the evolution of fetometry indicators, to segment different structures using a fetal brain atlas, and to align and combine multiple fetal brain acquisitions. This paper presents a new approach for automatic registration of real 3D US fetal brain volumes, volumes that contain a considerable degree of occlusion artefacts, noise, and missing data. To achieve this, a novel variant of the coherent point drift method is proposed. This work employs supervised learning to segment and conform a point cloud automatically and to estimate their subsequent weight factors. These factors are obtained by a random forest-based classification and are used to appropriately assign nonuniform membership probability values of a Gaussian mixture model. These characteristics allow for the automatic registration of 3D US fetal brain volumes with occlusions and multiplicative noise, without needing an initial point cloud. Compared to other intensity and geometry-based algorithms, the proposed method achieves an error reduction of 7.4% to 60.7%, with a target registration error of only 6.38 ± 3.24 mm. This makes the herein proposed approach highly suitable for 3D automatic registration of fetal head US volumes, an approach which can be useful to monitor fetal growth, segment several brain structures, or even compound multiple acquisitions taken from different projections.

Spatial Compounding of 3-D Fetal Brain Ultrasound Using Probabilistic Maps.

A new method to address the problem of shadowing in fetal brain ultrasound volumes is presented. The proposed approach is based on the spatial composition of multiple 3-D fetal head projections using the weighted Euclidean norm as an operator. A support vector machine, which is trained with optimal textural features, was used to assign weighting according to the posterior probabilities of brain tissue and shadows. Both phantom and real fetal head ultrasound volumes were compounded using previously reported operators and compared with the proposed composition method to validate it. The quantitative evaluations revealed increases in signal-to-noise ratio ≤35% and in contrast-to-noise ratio ≤135% using real data. Qualitative comparisons made by obstetricians indicated that this novel method adequately recovers brain tissue and improves the visibility of the main cerebral structures. This may prove useful both for fetal monitoring and in the diagnosis of brain defects. Overall this new approach outperforms spatial composition methods previously reported.

Description and classification of normal and pathological aging processes based on brain magnetic resonance imaging morphology measures.

We present a discrete compactness (DC) index, together with a classification scheme, based both on the size and shape features extracted from brain volumes, to determine different aging stages: healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). A set of 30 brain magnetic resonance imaging (MRI) volumes for each group was segmented and two indices were measured for several structures: three-dimensional DC and normalized volumes (NVs). The discrimination power of these indices was determined by means of the area under the curve (AUC) of the receiver operating characteristic, where the proposed compactness index showed an average AUC of 0.7 for HC versus MCI comparison, 0.9 for HC versus AD separation, and 0.75 for MCI versus AD groups. In all cases, this index outperformed the discrimination capability of the NV. Using selected features from the set of DC and NV measures, three support vector machines were optimized and validated for the pairwise separation of the three classes. Our analysis shows classification rates of up to 98.3% between HC and AD, 85% between HC and MCI, and 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indices to classify different aging stages.