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. 2023 Dec 8;1(2):qyad043.
doi: 10.1093/ehjimp/qyad043. eCollection 2023 Sep.

Deep learning-based computed tomography quantification of left ventricular mass

Amro Sehly  1 , Albert He  1 , Jacob Agris  2 , John Konstantopoulos  2 , Jack Joyner  2 , Julien Flack  2 , Simon Kwok  2 , Benjamin J W Chow  3 , Brian Ko  4 , Michael Ridner  5 , Abdul Rahman Ihdayhid  1   6   7 , Girish Dwivedi  1   6   8
Affiliations

Deep learning-based computed tomography quantification of left ventricular mass

Amro Sehly et al. Eur Heart J Imaging Methods Pract. .
No abstract available

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Conflict of interest statement

Conflict of interest. J.A., J.K., J.J., J.F., A.R.I., and G.D. have equity in Artrya Ltd. J.J., J.F., and S.K. are employees of Artrya Ltd. A.R.I. has speaker bureau from Boston Scientific, Abbott Medical, and Janssen. G.D. has speaker bureau for Janssen, Amgen, Pfizer, and AstraZeneca. For the remaining authors, none were declared.

Figures

Figure 1
Figure 1
(A) Linear regression plot of LV mass detected by the DL system. Line of equality (y = x) represented by dashed line. (B) Bland–Altman plot of the ground truth as compared with the DL system for LV mass detection, with 95% limits of agreement.
Figure 2
Figure 2
Cardiac CT four-chamber views of the LV with the ground truth (blue outline) and DL model (red outline) segmentation contours overlayed. The left image (A) demonstrates an example of good correlation of the DL model with ground truth, whereas the right image (B) demonstrates an example of poor correlation.
See this image and copyright information in PMC

References

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