Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST).

Journal: PloS one

PMID: 32745082

Abstract

BACKGROUND: The National Lung Screening Trial (NLST) demonstrated that annual screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. Nonetheless, the leading cause of mortality in the study was from cardiovascular diseases.

Authors

Amos Stemmer

Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
Ran Shadmi

Zebra Medical Vision, Ltd, Shfayim, Israel.
Orna Bregman-Amitai

Zebra Medical Vision, Shfayim, Israel.
David Chettrit

Zebra Medical Vision, Shfayim, Israel.
Denitza Blagev

Pulmonary and Critical Care Division, Intermountain Medical Center, Murray, Utah, United States of America.
Mila Orlovsky

Zebra Medical Vision, Shfayim, Israel.
Lisa Deutsch

Zebra Medical Vision, Ltd, Shfayim, Israel.
Eldad Elnekave

Zebra Medical Vision LTD, Shfayim, Israel. Eldad@zebra-med.com.

Keywords

Cardiovascular Diseases Cigarette Smoking Clinical Trials, Phase III as Topic Coronary Vessels Early Detection of Cancer Emphysema Fatty Liver Female Humans Liver Lung Lung Neoplasms Machine Learning Male Mass Screening Middle Aged National Cancer Institute (U.S.) Radiographic Image Interpretation, Computer-Assisted Randomized Controlled Trials as Topic Retrospective Studies Risk Assessment Risk Factors Tomography, X-Ray Computed United States

External Resources

View on PubMed Access via DOI PubMed (32745082)

Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST).

Abstract

Authors

Keywords

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