Applications of artificial intelligence in cardiovascular imaging

Sermesant, Maxime; Delingette, Hervé; Cochet, Hubert; Jaı̈s, Pierre; Ayache, Nicholas

doi:10.1038/s41569-021-00527-2

Cited by 107 publications

(55 citation statements)

References 145 publications

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“…ML models have been utilized successfully and extensively in arrhythmia risk assessment (Feeny et al, 2020 ; Krittanawong et al, 2020 ) and in cardiovascular imaging, to diverse ends (Prakosa et al, 2013 ; Bernard et al, 2018 ; Sermesant et al, 2021 ). More recently, compound, explainable ML models have demonstrated improved risk prediction for ventricular arrhythmias as compared to traditional biomarkers (i.e., left ventricular ejection fraction, LVEF), as validated retrospectively in large clinical cohorts, (e.g., Ly et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…Many studies have also successfully employed ML in arrhythmia risk stratification, including advanced ML-enabled image analysis (Feeny et al, 2020 ; Krittanawong et al, 2020 ; Trayanova, 2021 ). Recently, ML models have been combined with biophysical modeling to assess risk for dangerous arrhythmia as well as to uncover mechanisms of rhythm disturbances and to manage treatment for affected patients (Prakosa et al, 2013 ; Bernard et al, 2018 ; Lozoya et al, 2019 ; Shade et al, 2020 ; Banus et al, 2021 ; Monaci et al, 2021 ; Sermesant et al, 2021 ; Trayanova, 2021 ). Biophysical cardiac computational modeling and ML have also increasingly been combined to focus on drug-induced proarrhythmic risk assessment, as in e.g., Yang et al ( 2020 ) and Sahli-Costabal et al ( 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients

Maleckar

Myklebust

et al. 2021

Front. Physiol.

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Background: Remodeling due to myocardial infarction (MI) significantly increases patient arrhythmic risk. Simulations using patient-specific models have shown promise in predicting personalized risk for arrhythmia. However, these are computationally- and time- intensive, hindering translation to clinical practice. Classical machine learning (ML) algorithms (such as K-nearest neighbors, Gaussian support vector machines, and decision trees) as well as neural network techniques, shown to increase prediction accuracy, can be used to predict occurrence of arrhythmia as predicted by simulations based solely on infarct and ventricular geometry. We present an initial combined image-based patient-specific in silico and machine learning methodology to assess risk for dangerous arrhythmia in post-infarct patients. Furthermore, we aim to demonstrate that simulation-supported data augmentation improves prediction models, combining patient data, computational simulation, and advanced statistical modeling, improving overall accuracy for arrhythmia risk assessment.Methods: MRI-based computational models were constructed from 30 patients 5 days post-MI (the “baseline” population). In order to assess the utility biophysical model-supported data augmentation for improving arrhythmia prediction, we augmented the virtual baseline patient population. Each patient ventricular and ischemic geometry in the baseline population was used to create a subfamily of geometric models, resulting in an expanded set of patient models (the “augmented” population). Arrhythmia induction was attempted via programmed stimulation at 17 sites for each virtual patient corresponding to AHA LV segments and simulation outcome, “arrhythmia,” or “no-arrhythmia,” were used as ground truth for subsequent statistical prediction (machine learning, ML) models. For each patient geometric model, we measured and used choice data features: the myocardial volume and ischemic volume, as well as the segment-specific myocardial volume and ischemia percentage, as input to ML algorithms. For classical ML techniques (ML), we trained k-nearest neighbors, support vector machine, logistic regression, xgboost, and decision tree models to predict the simulation outcome from these geometric features alone. To explore neural network ML techniques, we trained both a three - and a four-hidden layer multilayer perceptron feed forward neural networks (NN), again predicting simulation outcomes from these geometric features alone. ML and NN models were trained on 70% of randomly selected segments and the remaining 30% was used for validation for both baseline and augmented populations.Results: Stimulation in the baseline population (30 patient models) resulted in reentry in 21.8% of sites tested; in the augmented population (129 total patient models) reentry occurred in 13.0% of sites tested. ML and NN models ranged in mean accuracy from 0.83 to 0.86 for the baseline population, improving to 0.88 to 0.89 in all cases.Conclusion: Machine learning techniques, combined with patient-specific, image-based computational simulations, can provide key clinical insights with high accuracy rapidly and efficiently. In the case of sparse or missing patient data, simulation-supported data augmentation can be employed to further improve predictive results for patient benefit. This work paves the way for using data-driven simulations for prediction of dangerous arrhythmia in MI patients.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients

Maleckar

Myklebust

et al. 2021

Front. Physiol.

View full text Add to dashboard Cite

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“…There is a growing interest in applications of artificial intelligence in cardiovascular diseases [ 4 , 9 , 10 , 11 ]. Emerging techniques, including machine learning (ML), and deep learning (DL), especially convolutional neural networks (CNN), have brought new insights into cardiovascular image segmentation [ 4 , 9 , 11 , 12 , 13 , 14 , 15 ] and could be applied to develop a fully automatic software. Synthetic data refer to data that are generated by a computer program, instead of being extracted from direct measurement by a human.…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

Lareyre

Adam

Carrier

et al. 2021

JCM

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Background: Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree. Methods: We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert. Results: The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, p = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, p < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, p = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, p < 0.0001). Conclusions: By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.

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“…Cardiac CT has been used to evaluate coronary stenosis, identify hemodynamically significant stenosis, elucidate the pathology in structural heart disease, and measure cardiac function [4][5][6][7]. Some excellent reviews on the application of AI in cardiovascular imaging have been published recently [3,[8][9][10][11]. Although these studies included cardiac CT, the subject of discussion was multimodality imaging, including echocardiography, nuclear imaging, and cardiac magnetic resonance imaging (MRI).…”

Section: Introductionmentioning

confidence: 99%

“…Although these studies included cardiac CT, the subject of discussion was multimodality imaging, including echocardiography, nuclear imaging, and cardiac magnetic resonance imaging (MRI). These studies also addressed how clinicians could apply AI in the clinical workflow with multimodality imaging, such as patient screening, decision support, prognostication, and follow-up [3,9,11]. Although these discussions are worthwhile, a more focused review of the CT imaging workflow is also meaningful from the perspective of the radiologist.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence to Cardiovascular Computed Tomography

Yang

2021

Korean J Radiol

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Cardiovascular computed tomography (CT) is among the most active fields with ongoing technical innovation related to image acquisition and analysis. Artificial intelligence can be incorporated into various clinical applications of cardiovascular CT, including imaging of the heart valves and coronary arteries, as well as imaging to evaluate myocardial function and congenital heart disease. This review summarizes the latest research on the application of deep learning to cardiovascular CT. The areas covered range from image quality improvement to automatic analysis of CT images, including methods such as calcium scoring, image segmentation, and coronary artery evaluation.

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Applications of artificial intelligence in cardiovascular imaging

Cited by 107 publications

References 145 publications

Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients

Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

Application of Artificial Intelligence to Cardiovascular Computed Tomography

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