Why the study?
Epicardial adipose tissue is key in cardiac pathophysiology, prompting the development of a deep learning model for its fully automatic extraction and quantification on pulmonary computed tomography venography images.
Does a deep learning model provide consistent and accurate automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography compared to manual segmentation in patients with atrial fibrillation?
Does a deep learning model provide consistent and accurate automatic epicardial adipose tissue segmentation in pulmonary computed tomography venography compared to manual segmentation in patients with atrial fibrillation?
A deep learning model can accurately and automatically segment epicardial adipose tissue from pulmonary CT venography images in patients with atrial fibrillation, performing consistently with manual methods.
Answers are grounded in this paper and cite their sources.
May facilitate automated EAT quantification in AF CT venography; leaves open prospective validation for clinical workflows.
Background: Epicardial adipose tissue (EAT) is a key aspect in the investigation of cardiac pathophysiology. We sought to develop a deep learning (DL) model for fully automatic extraction and quantification of EAT through pulmonary computed tomography venography (PCTV) images. Methods: In this retrospective study, we included 128 patients with atrial fibrillation and PCTV from 2 hospitals. A DL model for automated EAT segmentation was developed from a training set of 51 patients and a validation set of 13 patients from hospital A. The algorithm was further validated using an internal test set of 16 patients from hospital A and an external test set of 48 patients from hospital B. The consistency and measurement agreement of EAT quantification were compared between the DL model and the conventional manual protocol using the Dice score coefficient (DSC), Hausdorff distance (HD95), Pearson correlation coefficient, and Bland-Altman plot. Results: 106.20±15.90 min; P<0.001). The EAT segmented with the DL model had good consistency with manual segmentation (the DSC of the internal and external test sets were 0.92±0.02 and 0.88±0.03, respectively). The quantification of EAT evaluated with the 2 methods showed excellent correlation (all correlation coefficients >0.9; all P values <0.001) and minimal measurement difference. Conclusions: The proposed DL model achieved fully automatic quantification of EAT from PCTV images. The yielded results were highly consistent with those of manual quantification.
No takes yet. Share an insight, caveat, or question.
Hu et al. (2023) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: