Machine learning from fetal flow waveforms to predict adverse perinatal outcomes: A study protocol

Background: In Pakistan, stillbirth rates and early neonatal mortality rates are amongst the highest in the world. The aim of this study is to provide proof of concept for using a computational model of fetal haemodynamics, combined with machine learning. This model will be based on Doppler patterns of the fetal cardiovascular, cerebral and placental flows with the goal to identify those fetuses at increased risk of adverse perinatal outcomes such as stillbirth, perinatal mortality and other neonatal morbidities.Methods: This will be prospective one group cohort study which will be conducted in Ibrahim Hyderi, a peri-urban settlement in south east of Karachi. The eligibility criteria include pregnant women between 22-34 weeks who reside in the study area. Once enrolled, in addition to the performing fetal ultrasound to obtain Dopplers, data on socio-demographic, maternal anthropometry, haemoglobin and cardiotocography will be obtained on the pregnant women.Discussion: The machine learning approach for predicting adverse perinatal outcomes obtained from the current study will be validated in a larger population at the next stage. The data will allow for early interventions to improve perinatal outcomes

Septal Flash Assessment on CRT Candidates Based on Statistical Atlases of Motion

International audienceIn this paper, we propose a complete framework for the automatic detection and quantification of abnormal heart motion patterns using Statistical Atlases of Motion built from healthy populations. The method is illustrated on CRT patients with identified cardiac dyssyn-chrony and abnormal septal motion on 2D ultrasound (US) sequences. The use of the 2D US modality guarantees that the temporal resolution of the image sequences is high enough to work under a small displacements hypothesis. Under this assumption, the computed displacement fields can be directly considered as cardiac velocities. Comparison of subjects acquired with different spatiotemporal resolutions implies the reorientation and temporal normalization of velocity fields in a common space of coordinates. Statistics are then performed on the reoriented vector fields. Results show the ability of the method to correctly detect abnormal motion patterns and quantify their distance to normality. The use of local p-values for quantifying abnormal motion patterns is believed to be a promising strategy for computing new markers of cardiac dyssynchrony for better characterizing CRT candidates

Myocardial motion estimation combining tissue Doppler and B-mode echocardiographic images

International audienceWe present a registration framework that combines both tissue Doppler and B-mode echocardiographic sequences. The estimated spatiotemporal transform is diffeomorphic, and calculated by modeling its corresponding velocity field using continuous B-splines. A new cost function using both B-mode image voxel intensities and Doppler velocities is also proposed. Registration accuracy was evaluated on synthetic data with known ground truth. Results showed that our method allows quantifying wall motion with higher accuracy than when using a single modality. On patient data, both displacement and velocity curves were compared with the ones obtained from widely used commercial software using either B-mode images or TDI. Our method demonstrated to be more robust to image noise while being independent from the beam angle

Manifold learning characterization of abnormal myocardial motion patterns: application to CRT-induced changes

International audienceThe present paper aims at quantifying the evolution of a given motion pattern under cardiac resynchronization therapy (CRT). It builds upon techniques for population-based cardiac motion quantifica-tion (statistical atlases, for inter-sequence spatiotemporal alignment and the definition of normal/abnormal motion). Manifold learning is used on spatiotemporal maps of myocardial motion abnormalities to represent a given abnormal pattern and to compare any individual to that pattern. The methodology was applied to 2D echocardiographic sequences in a 4-chamber view from 108 subjects (21 healthy volunteers and 87 CRT candidates) at baseline, with pacing ON, and at 12 months follow-up. Experiments confirmed that recovery of a normal motion pattern is a necessary but not su cient condition for CRT response

A spatiotemporal statistical atlas of motion for the quantification of abnormal myocardial tissue velocities

International audienceIn this paper, we present a new method for the automatic comparison of myocardial motion patterns and the characterization of their degree of abnormality, based on a statistical atlas of motion built from a reference healthy population. Our main contribution is the computation of atlas-based indexes that quantify the abnormality in the motion of a given subject against a reference population, at every location in time and space. The critical computational cost inherent to the construction of an atlas is highly reduced by the definition of myocardial velocities under a small displacements hypothesis. The indexes we propose are of notable interest for the assessment of anomalies in cardiac mobility and synchronicity when applied, for instance, to candidate selection for cardiac resynchronization therapy (CRT). We built an atlas of normality using 2D ultrasound cardiac sequences from 21 healthy volunteers, to which we compared 14 CRT patients with left ventricular dyssynchrony (LVDYS). We illustrate the potential of our approach in characterizing septal flash, a specific motion pattern related to LVDYS and recently introduced as a very good predictor of response to CRT

Heart morphology differences induced by intrauterine growth restriction and premature birth measured on the ECG in pre-adolescents

Pre-adolescents who had suffered from intrauterine growth restriction (IUGR) during their mothers'' pregnancy usually present more spherical hearts (smaller relation between base to apex measure and basal diameter), measured using echocardiograms, which has been associated with long-term cardiac disfunction. The present work aims to analyse these heart morphology changes by means of the surface ECG so as to have an early diagnostic tool of this pathology. The dataset is conformed by 148 pre-adolescents with either preterm or term births, and with or without IUGR. Once QRS and T-wave loops were obtained from the vectorcardiogram, the angles between the dominant vector of the QRS loop and -XY or -YZ planes(FR-XY, FR-YZ) and the difference between FR-XY and the angle between the dominant vector of T-wave loop (FT-XY) and the XY-plane showed different values for pre-adolescents who suffered from premature birth and IUGR than for control subjects (p < 0.05). These characteristics can open the door for a much easier diagnosis and follow-up of candidates for these disfunctions

Characterization of myocardial motion by multiple kernel learning: application to heart failure with preserved ejection fraction

International audienceThe present study aims at improving the characterization of myocardial velocities in the context of heart failure with preserved ejection fraction (HFPEF) by combining multiple descriptors. It builds upon a recent extension of manifold learning known as multiple kernel learning (MKL), which allows the combination of data of different natures towards the learning. Such learning is kept unsupervised, thus benefiting from all the inherent explanatory power of the data without being conditioned by a given class. The methodology was applied to 2D sequences from a stress echocardiography protocol from 33 subjects (21 healthy controls and 12 HFPEF subjects). Our method provides a novel way to tackle the understanding of the HFPEF syndrome, in contrast with the diagnostic issues surrounding it in the current clinical practice. Notably, our results confirm that the characterization of the myocardial functional response to stress in this syndrome is improved by the joint analysis of multiple relevant features

A tomographic microscopy-compatible Langendorff system for the dynamic structural characterization of the cardiac cycle

ntroduction: Cardiac architecture has been extensively investigated ex vivo using a broad spectrum of imaging techniques. Nevertheless, the heart is a dynamic system and the structural mechanisms governing the cardiac cycle can only be unveiled when investigating it as such. Methods: This work presents the customization of an isolated, perfused heart system compatible with synchrotron-based X-ray phase contrast imaging (X-PCI). Results: Thanks to the capabilities of the developed setup, it was possible to visualize a beating isolated, perfused rat heart for the very first time in 4D at an unprecedented 2.75 μm pixel size (10.6 μm spatial resolution), and 1 ms temporal resolution. Discussion: The customized setup allows high-spatial resolution studies of heart architecture along the cardiac cycle and has thus the potential to serve as a tool for the characterization of the structural dynamics of the heart, including the effects of drugs and other substances able to modify the cardiac cycle