Evidence map›Paper›PMID 42312790›Full record

ArticleJACC. Advances2026

A Machine Learning Driven Approach to Quantifying Coronary Artery Tortuosity.

Jose Roberto Tello Ayala, Kelvin Supriami, Siddharth Swaroop, Samuel F Friedman, Ozan Unlu, Jennifer Halford, Mahnaz Maddah, Roukoz Abou-Karam, Eugene Pomerantsev, Patrick T Ellinor and 2 more

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Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

12 authors.

Jose Roberto Tello AyalaHarvard 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.
Kelvin SupriamiCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge Massachusetts, USA; Division of Cardiology, Heart and Vascular Institute, Mass General Brigham, Boston Massachusetts, USA.
Siddharth SwaroopDepartment of Computer Science, University College of London, London, United Kingdom.
Samuel F FriedmanCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge Massachusetts, USA; Division of Cardiology, Heart and Vascular Institute, Mass General Brigham, Boston Massachusetts, USA.
Ozan UnluCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge Massachusetts, USA; Division of Cardiology, Heart and Vascular Institute, Mass General Brigham, Boston Massachusetts, USA.
Jennifer HalfordCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge Massachusetts, USA; Division of Cardiology, Heart and Vascular Institute, Mass General Brigham, Boston Massachusetts, USA.
Mahnaz MaddahCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge Massachusetts, USA.
Roukoz Abou-KaramCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge Massachusetts, USA; Division of Cardiology, Heart and Vascular Institute, Mass General Brigham, Boston Massachusetts, USA.
Eugene PomerantsevDivision of Cardiology, Heart and Vascular Institute, Mass General Brigham, Boston Massachusetts, USA.
Patrick T EllinorCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge Massachusetts, USA; Division of Cardiology, Heart and Vascular Institute, Mass General Brigham, Boston Massachusetts, USA.
Finale Doshi-VelezHarvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston Massachusetts, USA.
Akl C FahedCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge Massachusetts, USA; Division of Cardiology, Heart and Vascular Institute, Mass General Brigham, Boston Massachusetts, USA. Electronic address: afahed@mgb.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe associations between coronary artery tortuosity and age, sex, and cardiovascular risk factors are not fully established. Prior studies enrolled fewer than 1,000 subjects and relied on heuristic metrics.

objectivesThe purpose of this study was to develop and validate an automated right coronary artery (RCA) tortuosity measure using machine learning (ML)-based vessel segmentation and examine its association with sex, age, coronary artery disease (CAD), and cardiovascular risk factors.

methodsWe developed an ML-enabled pipeline to quantify RCA tortuosity as a continuous measure in 38,691 RCA angiograms from 22,334 patients and evaluated concordance with blinded interventional cardiologist review in a subset of 300 angiograms. Regression models were used to study the association of tortuosity with age, sex, hypertension, diabetes, hypercholesterolemia, smoking, and presence of CAD.

resultsTortuosity ranged from 0.007 to 0.289. Blinded interventional cardiologist classification of high tortuosity vs not had 85.6% precision. Tortuosity was higher in women (β per SD = 0.17; P < 0.001) and higher with hypertension (β per SD = 0.06; P = 0.002), but lower with diabetes (β per SD = -0.11, P < 0.001. After adjustment for risk factors, age was not independently associated with tortuosity. Hypercholesterolemia and smoking were not associated. Higher tortuosity was associated with CAD (OR per SD = 1.05; P < 0.001), severe CAD (OR per SD = 1.09; P < 0.001), and higher Gensini score (β per SD = 0.05; P < 0.001), even after adjustment.

conclusionsWe derived a scalable ML-enabled measure of RCA tortuosity from coronary angiography and found associations with sex, hypertension, diabetes, and presence and severity of CAD.

Indexed as

angiographyartificial intelligencecoronary artery diseasetortuosity

Identifiers

PMID42312790
PMCPMC13308244

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