Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response.

BACKGROUND: In acute cardiovascular disease management, the delay between the admission in a hospital emergency department and the assessment of the disease from a Delayed Enhancement cardiac MRI (DE-MRI) scan is one of the barriers for an immediate management of patients with suspected myocardial infarction or myocarditis. OBJECTIVES: This work targets patients who arrive at the hospital with chest pain and are suspected of having a myocardial infarction or a myocarditis. The main objective is to classify these patients based solely on clinical data in order to provide an early accurate diagnosis. METHODS: Machine learning (ML) and ensemble approaches have been used to construct a framework to automatically classify the patients according to their clinical conditions. 10-fold cross-validation is used during the model's training to avoid overfitting. Approaches such as Stratified, Over-sampling, Under-sampling, NearMiss, and SMOTE were tested in order to address the imbalance of the data (i.e. proportion of cases per pathology). The ground truth is provided by a DE-MRI exam (normal exam, myocarditis or myocardial infarction). RESULTS: The stacked generalization technique with Over-sampling seems to be the best one providing more than 97% of accuracy corresponding to 11 wrong classifications among 537 cases. Generally speaking, ensemble classifiers such as Stacking provided the best prediction. The five most important features are troponin, age, tobacco, sex and FEVG calculated from echocardiography. CONCLUSION: Our study provides a reliable approach to classify the patients in emergency department between myocarditis, myocardial infarction or other patient condition from only clinical information, considering DE-MRI as ground-truth. Among the different machine learning and ensemble techniques tested, the stacked generalization technique is the best one providing an accuracy of 97.4%. This automatic classification could provide a quick answer before imaging exam such as cardiovascular MRI depending on the patient's condition.

Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge.

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed 10 min after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge's main objectives were twofold. First, to evaluate if deep learning methods can distinguish between non-infarct and pathological exams, i.e. exams with or without hyperenhanced area. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases without any hyperenhanced area after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures.

Prediction of Myocardial Infarction From Patient Features With Machine Learning.

This study proposes machine learning-based models to automatically evaluate the severity of myocardial infarction (MI) from physiological, clinical, and paraclinical features. Two types of machine learning models are investigated for the MI assessment: the classification models classify the presence of the infarct and the persistent microvascular obstruction (PMO), and the regression models quantify the Percentage of Infarcted Myocardium (PIM) of patients suspected of having an acute MI during their reception in the emergency department. The ground truth labels for these supervised models are derived from the corresponding Delayed Enhancement MRI (DE-MRI) exams and manual annotations of the myocardium and scar tissues. Experiments were conducted on 150 cases and evaluated with cross-validation. Results showed that for the MI (PMO inclusive) and the PMO (infarct exclusive), the best models obtained respectively a mean error of 0.056 and 0.012 for the quantification, and 88.67 and 77.33% for the classification accuracy of the state of the myocardium. The study of the features' importance also revealed that the troponin value had the strongest correlation to the severity of the MI among the 12 selected features. For the proposal's translational perspective, in cardiac emergencies, qualitative and quantitative analysis can be obtained prior to the achievement of MRI by relying only on conventional tests and patient features, thus, providing an objective reference for further treatment by physicians.

Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI.

Delayed Enhancement cardiac MRI (DE-MRI) has become indispensable for the diagnosis of myocardial diseases. However, to quantify the disease severity, doctors need time to manually annotate the scar and myocardium. To address this issue, in this paper we propose an automatic myocardial infarction segmentation approach on the left ventricle from short-axis DE-MRI based on Convolutional Neural Networks (CNN). The objective is to segment myocardial infarction on short-axis DE-MRI images of the left ventricle acquired 10 min after the injection of a gadolinium-based contrast agent. The segmentation of the infarction area is realized in two stages: a first CNN model finds the contour of myocardium and a second CNN model segments the infarction. Compared to the manual intra-observer and inter-observer variations for the segmentation of myocardial infarction, and to the automatic segmentation with Gaussian Mixture Model, our proposal achieves satisfying segmentation results on our dataset of 904 DE-MRI slices.

Ancestral Reconstruction and Investigations of Genomic Recombination on some Pentapetalae Chloroplasts.

In this article, we propose a semi-automated method to rebuild genome ancestors of chloroplasts by taking into account gene duplication. Two methods have been used in order to achieve this work: a naked eye investigation using homemade scripts, whose results are considered as a basis of knowledge, and a dynamic programming based approach similar to Needleman-Wunsch. The latter fundamentally uses the Gestalt pattern matching method of sequence matcher to evaluate the occurrences probability of each gene in the last common ancestor of two given genomes. The two approaches have been applied on chloroplastic genomes from Apiales, Asterales, and Fabids orders, the latter belonging to Pentapetalae group. We found that Apiales species do not undergo indels, while they occur in the Asterales and Fabids orders. A series of experiments was then carried out to extensively verify our findings by comparing the obtained ancestral reconstruction results with the latest released approach called MLGO (Maximum Likelihood for Gene-Order analysis).

On the reconstruction of the ancestral bacterial genomes in genus Mycobacterium and Brucella.

BACKGROUND: To reconstruct the evolution history of DNA sequences, novel models of increasing complexity regarding the number of free parameters taken into account in the sequence evolution, as well as faster and more accurate algorithms, and statistical and computational methods, are needed. More particularly, as the principal forces that have led to major structural changes are genome rearrangements (such as translocations, fusions, and so on), understanding their underlying mechanisms, among other things via the ancestral genome reconstruction, are essential. In this problem, since finding the ancestral genomes that minimize the number of rearrangements in a phylogenetic tree is known to be NP-hard for three or more genomes, heuristics are commonly chosen to obtain approximations of the exact solution. The aim of this work is to show that another path is possible. RESULTS: Various algorithms and software already deal with the difficult nature of the problem of reconstruction of the ancestral genome, but they do not function with precision, in particular when indels or single nucleotide polymorphisms fall into repeated sequences. In this article, and despite the theoretical NP-hardness of the ancestral reconstruction problem, we show that an exact solution can be found in practice in various cases, encompassing organelles and some bacteria. A practical example proves that an accurate reconstruction, which also allows to highlight homoplasic events, can be obtained. This is illustrated by the reconstruction of ancestral genomes of two bacterial pathogens, belonging in Mycobacterium and Brucella genera. CONCLUSIONS: By putting together automatically reconstructed ancestral regions with handmade ones for problematic cases, we show that an accurate reconstruction of ancestors of the Brucella genus and of the Mycobacterium tuberculosis complex is possible. By doing so, we are able to investigate the evolutionary history of each pathogen by computing their common ancestors. They can be investigated extensively, by studying the gene content evolution over time, the resistance acquisition, and the impacts of mobile elements on genome plasticity.

Comparison of metaheuristics to measure gene effects on phylogenetic supports and topologies.

BACKGROUND: A huge and continuous increase in the number of completely sequenced chloroplast genomes, available for evolutionary and functional studies in plants, has been observed during the past years. Consequently, it appears possible to build large-scale phylogenetic trees of plant species. However, building such a tree that is well-supported can be a difficult task, even when a subset of close plant species is considered. Usually, the difficulty raises from a few core genes disturbing the phylogenetic information, due for example from problems of homoplasy. Fortunately, a reliable phylogenetic tree can be obtained once these problematic genes are identified and removed from the analysis.Therefore, in this paper we address the problem of finding the largest subset of core genomes which allows to build the best supported tree. RESULTS: As an exhaustive study of all core genes combination is untractable in practice, since the combinatorics of the situation made it computationally infeasible, we investigate three well-known metaheuristics to solve this optimization problem. More precisely, we design and compare distributed approaches using genetic algorithm, particle swarm optimization, and simulated annealing. The latter approach is a new contribution and therefore is described in details, whereas the two former ones have been already studied in previous works. They have been designed de novo in a new platform, and new experiments have been achieved on a larger set of chloroplasts, to compare together these three metaheuristics. CONCLUSIONS: The ways genes affect both tree topology and supports are assessed using statistical tools like Lasso or dummy logistic regression, in an hybrid approach of the genetic algorithm. By doing so, we are able to provide the most supported trees based on the largest subsets of core genes.

Development of a 4D numerical chest phantom with customizable breathing.

Respiratory movement information is useful for radiation therapy, and is generally obtained using 4D scanners (4DCT). In the interest of patient safety, reducing the use of 4DCT could be a significant step in reducing radiation exposure, the effects of which are not well documented. The authors propose a customized 4D numerical phantom representing the organ contours. Firstly, breathing movement can be simulated and customized according to the patient's anthroporadiametric data. Using learning sets constituted by 4D scanners, artificial neural networks can be trained to interpolate the lung contours corresponding to an unknown patient, and then to simulate its respiration. Lung movement during the breathing cycle is modeled by predicting the lung contours at any respiratory phases. The interpolation is validated comparing the obtained lung contours with 4DCT via Dice coefficient. Secondly, a preliminary study of cardiac and œsophageal motion is also presented to demonstrate the flexibility of this approach. The application may simulate the position and volume of the lungs, the œsophagus and the heart at every phase of the respiratory cycle with a good accuracy: the validation of the lung modeling gives a Dice index greater than 0.93 with 4DCT over a breath cycle.

Neural networks and chaos: construction, evaluation of chaotic networks, and prediction of chaos with multilayer feedforward networks.

Many research works deal with chaotic neural networks for various fields of application. Unfortunately, up to now, these networks are usually claimed to be chaotic without any mathematical proof. The purpose of this paper is to establish, based on a rigorous theoretical framework, an equivalence between chaotic iterations according to Devaney and a particular class of neural networks. On the one hand, we show how to build such a network, on the other hand, we provide a method to check if a neural network is a chaotic one. Finally, the ability of classical feedforward multilayer perceptrons to learn sets of data obtained from a dynamical system is regarded. Various boolean functions are iterated on finite states. Iterations of some of them are proven to be chaotic as it is defined by Devaney. In that context, important differences occur in the training process, establishing with various neural networks that chaotic behaviors are far more difficult to learn.