AI Article Synopsis

  • Personalised medicine aims to tailor treatments based on the unique characteristics of each patient's body, particularly in cardiology where individual cardiovascular traits are crucial for personalized care.
  • A key method for achieving this involves using computational analysis of various imaging data to better understand these unique properties.
  • The Cardiac Electro-Mechanics Research Group Application (CemrgApp) has been developed as a versatile platform that incorporates custom image processing and machine learning tools for studying cardiac physiology, diagnoses, and treatments within a unified graphical interface.

Article Abstract

Personalised medicine is based on the principle that each body is unique and will respond to therapies differently. In cardiology, characterising patient specific cardiovascular properties would help in personalising care. One promising approach for characterising these properties relies on performing computational analysis of multimodal imaging data. An interactive cardiac imaging environment, which can seamlessly render, manipulate, derive calculations, and otherwise prototype research activities, is therefore sought-after. We developed the Cardiac Electro-Mechanics Research Group Application (CemrgApp) as a platform with custom image processing and computer vision toolkits for applying statistical, machine learning and simulation approaches to study physiology, pathology, diagnosis and treatment of the cardiovascular system. CemrgApp provides an integrated environment, where cardiac data visualisation and workflow prototyping are presented through a common graphical user interface.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610963PMC
http://dx.doi.org/10.1016/j.softx.2020.100570DOI Listing

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