Author Correction: Aortic acceleration as a noninvasive index of left ventricular contractility in the mouse

Perez, Jorge Enrique Tovar; Ortiz-Urbina, Jesús; Heredia, Celia Pena; Pham, Thuy T.; Madala, Sridhar; Hartley, Craig J.; Entman, Mark L.; Taffet, George E.; Reddy, Anilkumar K.

doi:10.1038/s41598-021-87101-5

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“…Additionally, the SR mean proves the early myocardial damage that occurs during this stage [50]. Finally, alterations in the AO peak acceleration time average parameter, which is the time between the start of systolic flow and the maximum velocity reached, is considered a non-invasive index of LV contractility in mice [51]. All these changes correspond to the relevant variables obtained by automatic feature selection algorithms.…”

Section: Feature Selectionmentioning

confidence: 99%

Machine Learning-Based Feature Selection and Classification for the Experimental Diagnosis of Trypanosoma cruzi

et al. 2022

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Chagas disease, caused by the Trypanosoma cruzi (T. cruzi) parasite, is the third most common parasitosis worldwide. Most of the infected subjects can remain asymptomatic without an opportune and early detection or an objective diagnostic is not conducted. Frequently, the disease manifests itself after a long time, accompanied by severe heart disease or by sudden death. Thus, the diagnosis is a complex and challenging process where several factors must be considered. In this paper, a novel pipeline is presented integrating temporal data from four modalities (electrocardiography signals, echocardiography images, Doppler spectrum, and ELISA antibody titers), multiple features selection analyses by a univariate analysis and a machine learning-based selection. The method includes an automatic dichotomous classification of animal status (control vs. infected) based on Random Forest, Extremely Randomized Trees, Decision Trees, and Support Vector Machine. The most relevant multimodal attributes found were ELISA (IgGT, IgG1, IgG2a), electrocardiography (SR mean, QT and ST intervals), ascending aorta Doppler signals, and echocardiography (left ventricle diameter during diastole). Concerning automatic classification from selected features, the best accuracy of control vs. acute infection groups was 93.3 ± 13.3% for cross-validation and 100% in the final test; for control vs. chronic infection groups, it was 100% and 100%, respectively. We conclude that the proposed machine learning-based approach can be of help to obtain a robust and objective diagnosis in early T. cruzi infection stages.

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Section: Feature Selectionmentioning

confidence: 99%

Machine Learning-Based Feature Selection and Classification for the Experimental Diagnosis of Trypanosoma cruzi

et al. 2022

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Author Correction: Aortic acceleration as a noninvasive index of left ventricular contractility in the mouse

Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cited by 1 publication

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Machine Learning-Based Feature Selection and Classification for the Experimental Diagnosis of Trypanosoma cruzi

Machine Learning-Based Feature Selection and Classification for the Experimental Diagnosis of Trypanosoma cruzi

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