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עמוד בית
Thu, 21.11.24

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November 2024
Ronit Lev Kolnik MD, Idan Bergman MD, Avishay Elis MD

Background: The Agatston coronary artery calcium (CAC) score is a decision-guiding aid for risk assessment and personalized management in the primary prevention of atherosclerotic cardiovascular disease.

Objectives: To explore the real-life clinical experience of CAC testing by characterizing its indications, significance of scores, and corresponding lipid-lowering treatments.

Methods: A retrospective descriptive study of patients treated at the lipids clinic at Rabin Medical Center (Beilinson Campus), who underwent CAC score evaluation between 2017 and 2022 was conducted. The data collected from electronic medical files included demographics, co-morbidities, indications for the test, CAC score levels, and the recommended therapeutic regimen.

Results: The study cohort included 88 patients. The main indication was assessment of the existence of atherosclerosis in cases where there was no clear indication for lipid lowering treatment (65, 74%). In most patients, there was no evidence of atherosclerosis (CAC = 0 AU, n=30) or only mild disease (CAC=1–99 AU, n=35). As the CAC score increased, more patients were prescribed lipid lowering treatments, from very few prescriptions in those with a CAC score of 0 AU and almost 100% among those with score of ≥ 400 AU. The factors that predicted CAC > 0 AU were male sex and older age.

Conclusions: CAC scores should be used more often to determine risk assessment. Further analysis of the implications of scores between 0–400 AU is needed.

February 2024
Orit Wimpfheimer MD, Yotam Kimmel BSc

Medical imaging data has been at the frontier of artificial intelligence innovation in medicine with many clinical applications. There have been many challenges, including patient data protection, algorithm performance, radiology workflow, user interface, and IT integration, which have been addressed and mitigated over the last decade. The AI products in imaging now fall into three main categories: triage artificial intelligence (AI), productivity AI, and augmented AI, each providing a different utility for radiologists, clinicians, and patients. Adoption of AI products into the healthcare system has been slow, but it is growing. It is typically dictated by return on investment, which can be demonstrated in each use case. It is expected to lead to wider adoption of AI products in imaging into the clinical workflow in the future.

April 2017
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