Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence

Abstract

Background Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development. Methods We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple domain-specific quantitative performance evaluation metrics, and conventional methodology performed by a core laboratory (CORE). Each of the DL-RVEF approaches was compared against CORE-RVEF reference values using linear regression and Bland–Altman analyses. Additionally, RVEF values were classified into 3 categories: ≤ 35%, 35–50%, and ≥ 50%. Agreement between RVEF classifications made by the DL approaches and the CORE measurements was tested. Results CORE-RVEF and DL-RVEFs were obtained in all patients (feasibility of 100%). DL2-RVEF correlated with CORE-RVEF better than DL1-RVEF (r = 0.87 vs. r = 0.42), with narrower limits of agreement. As a result, DL2 algorithm also showed increasing accuracy from 0.53 to 0.80 for categorizing RV function. Conclusions The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.

Papillary and Trabecular Muscles Have Substantial Impact on Quantification of Left Ventricle in Patients with Hypertrophic Obstructive Cardiomyopathy

“…Lately, an algorithm based on deep learning (DL) was employed to automatically quantify right ventricular (RV) ejection fraction (RVEF) from CMR images. The authors reported that the DL algorithm gave rise to substantial and clinically important improvements in patients whose images were difficult to analyze with a previous algorithm [ 28 ]. Considering that the geometry of the LV is less complex than that of RV, the artificial intelligence-based deep learning technology may achieve better performance in measuring the LV structure and function in patients with HOCM.…”

The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance—A Comprehensive Review

“…Automated segmentation methods based on ML and DL have been developed, but occasionally, manual correction is still necessary. As seen in the works on the use of artificial intelligence in the segmentation process, is more difficult to delineate the contours of the right ventricle than those of the left ventricle because of the right ventricle’s smaller wall thickness and irregular shape, with the presence of greater trabeculae [ 62 , 63 , 64 ].…”

Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective

“… 64 present a DL system for automated prescription of imaging planes by using a U-Net to localize anatomic landmarks. A series of recent contributions have demonstrated automated segmentation of anatomical structures in CMR images, 65 , 66 , 67 with future similar disease-specific work sure to follow. The automated cardiac diagnosis challenge (ACDC) dataset is a publicly available dataset of 150 CMR studies with manual segmentation labels as well as five diagnostic labels.…”