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Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...

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A Machine Learning Driven Approach to Quantifying Coronary Artery Tortuosity.

Jose Roberto Tello Ayala1, Kelvin Supriami2, Siddharth Swaroop3

  • 1Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge Massachusetts, USA; Division of Cardiology, Heart and Vascular Institute, Mass General Brigham, Boston Massachusetts, USA.

JACC. Advances
|June 18, 2026
PubMed
Summary
This summary is machine-generated.

A new machine learning tool accurately measures right coronary artery (RCA) tortuosity. This automated method reveals associations between RCA tortuosity, sex, hypertension, diabetes, and coronary artery disease (CAD) severity.

Keywords:
angiographyartificial intelligencecoronary artery diseasetortuosity

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Published on: September 22, 2023

Area of Science:

  • Cardiovascular Imaging and Diagnostics
  • Machine Learning in Medicine
  • Biomedical Engineering

Background:

  • Coronary artery tortuosity associations with cardiovascular risk factors are not well-established.
  • Previous studies were limited by small sample sizes and heuristic metrics.

Purpose of the Study:

  • To develop and validate an automated machine learning (ML)-based measure for right coronary artery (RCA) tortuosity.
  • To investigate the associations of RCA tortuosity with patient demographics, cardiovascular risk factors, and coronary artery disease (CAD).

Main Methods:

  • Developed an ML-enabled pipeline to quantify RCA tortuosity from 38,691 coronary angiograms.
  • Validated the ML measure against interventional cardiologist review in 300 cases.
  • Utilized regression models to assess associations with age, sex, hypertension, diabetes, hypercholesterolemia, smoking, and CAD.

Main Results:

  • The ML tool provided a scalable, continuous measure of RCA tortuosity.
  • Higher tortuosity was observed in women, and with hypertension, but inversely with diabetes.
  • Increased tortuosity correlated with the presence and severity of CAD, including higher Gensini scores.

Conclusions:

  • A scalable, ML-enabled measure of RCA tortuosity was successfully developed.
  • Significant associations were found between RCA tortuosity and sex, hypertension, diabetes, and CAD presence/severity.