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The Role of Machine Learning in Cardiovascular Pathology.

Publication ,  Journal Article
Glass, C; Lafata, KJ; Jeck, W; Horstmeyer, R; Cooke, C; Everitt, J; Glass, M; Dov, D; Seidman, MA
Published in: Can J Cardiol
February 2022

Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole-slide images. Machine learning tools have incredible potential to standardise, and likely even improve, histopathologic diagnoses, but they are not yet widely used in clinical practice. We describe the principles of these tools and technologies and some successful preclinical and pretranslational efforts in cardiovascular pathology, as well as a roadmap for moving forward. In nonhuman animal models, one proof-of-principle application is in rodent progressive cardiomyopathy, which is of particular significance to drug toxicity studies. Basic science successes include screening the quality of differentiated stem cells and characterising cardiomyocyte developmental stages, with potential applications for research and toxicology/drug safety screening using derived or native human pluripotent stem cells differentiated into cardiomyocytes. Translational studies of particular note include those with success in diagnosing the various forms of heart allograft rejection. For fully realising the value of these tools in clinical cardiovascular pathology, we identify 3 essential challenges. First is image quality standardisation to ensure that algorithms can be developed and implemented on robust, consistent data. The second is consensus diagnosis; experts don't always agree, and thus "truth" may be difficult to establish, but the algorithms themselves may provide a solution. The third is the need for large-enough data sets to facilitate robust algorithm development, necessitating large cross-institutional shared image databases. The power of histopathology-based machine learning technologies is tremendous, and we outline the next steps needed to capitalise on this power.

Duke Scholars

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Published In

Can J Cardiol

DOI

EISSN

1916-7075

Publication Date

February 2022

Volume

38

Issue

2

Start / End Page

234 / 245

Location

England

Related Subject Headings

  • Pathology, Clinical
  • Machine Learning
  • Image Processing, Computer-Assisted
  • Humans
  • Cardiovascular System & Hematology
  • Cardiovascular Diseases
  • Cardiology
  • Animals
  • Algorithms
  • 3201 Cardiovascular medicine and haematology
 

Citation

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Glass, C., Lafata, K. J., Jeck, W., Horstmeyer, R., Cooke, C., Everitt, J., … Seidman, M. A. (2022). The Role of Machine Learning in Cardiovascular Pathology. Can J Cardiol, 38(2), 234–245. https://doi.org/10.1016/j.cjca.2021.11.008
Glass, Carolyn, Kyle J. Lafata, William Jeck, Roarke Horstmeyer, Colin Cooke, Jeffrey Everitt, Matthew Glass, David Dov, and Michael A. Seidman. “The Role of Machine Learning in Cardiovascular Pathology.Can J Cardiol 38, no. 2 (February 2022): 234–45. https://doi.org/10.1016/j.cjca.2021.11.008.
Glass C, Lafata KJ, Jeck W, Horstmeyer R, Cooke C, Everitt J, et al. The Role of Machine Learning in Cardiovascular Pathology. Can J Cardiol. 2022 Feb;38(2):234–45.
Glass, Carolyn, et al. “The Role of Machine Learning in Cardiovascular Pathology.Can J Cardiol, vol. 38, no. 2, Feb. 2022, pp. 234–45. Pubmed, doi:10.1016/j.cjca.2021.11.008.
Glass C, Lafata KJ, Jeck W, Horstmeyer R, Cooke C, Everitt J, Glass M, Dov D, Seidman MA. The Role of Machine Learning in Cardiovascular Pathology. Can J Cardiol. 2022 Feb;38(2):234–245.
Journal cover image

Published In

Can J Cardiol

DOI

EISSN

1916-7075

Publication Date

February 2022

Volume

38

Issue

2

Start / End Page

234 / 245

Location

England

Related Subject Headings

  • Pathology, Clinical
  • Machine Learning
  • Image Processing, Computer-Assisted
  • Humans
  • Cardiovascular System & Hematology
  • Cardiovascular Diseases
  • Cardiology
  • Animals
  • Algorithms
  • 3201 Cardiovascular medicine and haematology