Cardiovascular Prevention and Pharmacotherapy

Original Article

The distribution of artificial intelligence–derived retinal cardiovascular risk scores and conventional risk factors in two Korean health screening cohorts: a descriptive study: Jungkyung Cho, Jaewon Seo, Junseok Park, Dongjin Nam, Tae Hyun Park, Sahil Thakur, Tyler Hyungtaek Rim, Beom-hee Choi, Miso Jang; Cardiovasc Prev Pharmacother. 2025;7(3):73-84. Published online July 28, 2025; DOI: https://doi.org/10.36011/cpp.2025.7.e14

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Abstract PDF Supplementary Material: Background
Although retinal imaging–based artificial intelligence (AI) tools have recently been introduced for cardiovascular disease (CVD) risk assessment, little is known about the distribution of these AI-derived scores across the full age spectrum or their associations with traditional cardiometabolic risk factors at different ages.
Methods
We analyzed data from 138,745 participants who underwent routine health examinations at two health screening centers in Seoul, Korea. The AI-based retinal CVD risk score (Dr.Noon CVD), as well as anthropometric, hemodynamic, and metabolic indices and cardiometabolic disease status, were compared across ages 16 to 96 years. In a subgroup of 13,182 individuals who underwent coronary artery calcium scoring (CACS) by cardiac computed tomography, we evaluated the performance of the Dr.Noon CVD score in detecting CACS using receiver operating characteristic curve analysis.
Results
Mean Dr.Noon CVD scores rose steadily with age from 14.2±2.9 (<30 years) to 46.3±6.5 (≥70 years), closely mirroring the increase in traditional cardiovascular risk factors with age. Additional analysis using CACS demonstrated that the Dr.Noon CVD score achieved an area under the curve of 0.80 (95% confidence interval, 0.80–0.81) for detecting any coronary calcification, defined as CACS >0, and an area under the curve of 0.82 (95% confidence interval, 0.81–0.83) for identifying significant calcification burden, defined as CACS >100.
Conclusions
Dr.Noon CVD scores were consistently correlated with age, conventional risk factors, and CACS, suggesting a potential role in broad-based cardiovascular risk stratification and in guiding personalized prevention strategies.

Review Article

The emergence and clinical significance of artificial intelligence–enhanced electrocardiography: Yong-Soo Baek; Cardiovasc Prev Pharmacother. 2024;6(2):41-47. Published online April 26, 2024; DOI: https://doi.org/10.36011/cpp.2024.6.e7

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The integration of artificial intelligence (AI) with electrocardiography (ECG), a technology known as AI-ECG, represents a transformative leap in the field of cardiovascular medicine. This innovative approach has significantly advanced the capabilities of ECG, traditionally used for diagnosing heart diseases. AI-ECG excels in detecting subtle changes and interconnected patterns in cardiac waveforms, offering a level of precision and sensitivity that was previously unattainable with conventional methods. The scope of AI-ECG extends beyond the realm of heart diseases. It has shown remarkable potential in predicting and identifying the impacts of noncardiac conditions on heart health, thereby broadening the diagnostic capabilities of ECG. This is especially valuable given the complex nature of cardiovascular diseases and their interactions with other health conditions. Despite its groundbreaking potential, AI-ECG faces several challenges. One of the primary concerns is the "black box" nature of AI algorithms, which can make the decision-making process opaque and difficult to interpret. This poses a challenge in medical settings where understanding the rationale behind a diagnosis is crucial. Additionally, the effectiveness of AI-ECG is dependent on the quality and diversity of the datasets used to train the algorithms. Limited or biased datasets can lead to inaccuracies and diminish the reliability of the technology. However, the benefits of AI-ECG are significant. It enables faster, more accurate diagnoses and has the potential to greatly enhance the efficiency of cardiovascular care. As research and technology continue to evolve, AI-ECG is poised to become an indispensable tool in the diagnosis and management of heart diseases.

Citations

Citations to this article as recorded by

AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions
Luiza Camelia Nechita, Aurel Nechita, Andreea Elena Voipan, Daniel Voipan, Mihaela Debita, Ana Fulga, Iuliu Fulga, Carmina Liana Musat
Diagnostics.2024; 14(17): 1839. CrossRef

Special Article

Perceptron: Basic Principles of Deep Neural Networks: Eung-Hee Kim, Hun-Sung Kim; Cardiovasc Prev Pharmacother. 2021;3(3):64-72. Published online July 31, 2021; DOI: https://doi.org/10.36011/cpp.2021.3.e9

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Abstract PDF

Big data, artificial intelligence, machine learning, and deep learning have received considerable attention in the medical field. Attempts to use such machine learning in areas where medical decisions are difficult or necessary are continuously being made. To date, there have been many attempts to solve problems associated with the use of machine learning by using deep learning; hence, physicians should also have basic knowledge in this regard. Deep neural networks are one of the most actively studied methods in the field of machine learning. The perceptron is one of these artificial neural network models, and it can be considered as the starting point of artificial neural network models. Perceptrons receive various inputs and produce one output. In a perceptron, various weights (ω) are given to various inputs, and as ω becomes larger, it becomes an important factor. In other words, a perceptron is an algorithm with both input and output. When an input is provided, the output is produced according to a set rule. In this paper, the decision rules of the perceptron and its basic principles are examined. The intent is to provide a wide range of physicians with an understanding of the latest machine-learning methodologies based on deep neural networks.

Citations

Citations to this article as recorded by

Binary Classification of Faba Bean (Vicia faba L.) Cultivars Based on Appearances Using Image Processing Technique and Machine Learning Algorithms
İrem Poyraz, Mevlüt Akçura
Türk Tarım ve Doğa Bilimleri Dergisi.2025; 12(4): 1084. CrossRef

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