ArticleSensors (Basel, Switzerland)2021
Deep-Learning-Based Coronary Artery Calcium Detection from CT Image.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Opportunistic automated aortic valve calcification assessment in low-cost, screening CT calcium score exams.Scientific reports · 2026Article
- Classification of Chronic Obstructive Pulmonary Disease (COPD) Through Respiratory Pattern Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Decalcify cardiac CT: unveiling clearer images with deep convolutional neural networks.Frontiers in medicine · 2025Article
- Deep learning for automatic calcium detection in echocardiography.BioData mining · 2024Article
- Use of Artificial Intelligence in Coronary Artery Calcium Scoring.Oman medical journal · 2023Review
- Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules.Medical science monitor : international medical journal of experimental and clinical research · 2022Article
- Deep Learning in Multi-Class Lung Diseases' Classification on Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.