Advancements in Artificial Intelligence Applications for Cardiovascular Disease Research

• URL: http://arxiv.org/abs/2506.03698v1
• Date: Wed, 04 Jun 2025 08:31:28 GMT
• Title: Advancements in Artificial Intelligence Applications for Cardiovascular Disease Research
• Abstract summary: Recent advancements in artificial intelligence (AI) have revolutionized cardiovascular medicine.<n>Deep learning architectures enable automated analysis of medical imaging and physiological signals.<n>Future directions emphasize hybrid models integrating multimodal data and adaptive algorithms to refine personalized cardiovascular care.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in artificial intelligence (AI) have revolutionized cardiovascular medicine, particularly through integration with computed tomography (CT), magnetic resonance imaging (MRI), electrocardiography (ECG) and ultrasound (US). Deep learning architectures, including convolutional neural networks and generative adversarial networks, enable automated analysis of medical imaging and physiological signals, surpassing human capabilities in diagnostic accuracy and workflow efficiency. However, critical challenges persist, including the inability to validate input data accuracy, which may propagate diagnostic errors. This review highlights AI's transformative potential in precision diagnostics while underscoring the need for robust validation protocols to ensure clinical reliability. Future directions emphasize hybrid models integrating multimodal data and adaptive algorithms to refine personalized cardiovascular care.

Related papers

• Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement [81.27196033392559]
  We propose a Collaborative Meta Knowledge Enhancement (COME) framework for dementia etiology diagnosis.<n>COME injects multi-center acquisition semantics, source identifiers, and modality indicators as heterogeneity-aware embeddings into a unified Transformer architecture for scale-up training.<n>Our method achieves state-of-the-art in-domain performance with a mean macro-averaged AUC of 85.62% and a 4.29-point gain over the strongest baseline.
  arXiv  Detail & Related papers  (2026-07-24T04:26:47Z)
• Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis [45.00216485193071]
  Rare cardiac anomalies are difficult to detect from electrocardiograms due to their long-tailed distribution.<n>We developed an AI-assisted two-stage ECG framework integrating self-supervised anomaly detection and demographic-aware representation learning.<n>Our method achieves an AUROC of 94.7% for rare anomalies and reduces the common-rare performance gap by 73%.
  arXiv  Detail & Related papers  (2026-03-20T07:04:00Z)
• Deep Learning-Based Pneumonia Detection from Chest X-ray Images: A CNN Approach with Performance Analysis and Clinical Implications [0.0]
  The study introduces an intricate deep learning system using Conal Neural Networks for automated pneumonia detection from chest Xray images.<n>The proposed CNN architecture integrates sophisticated methods including separable convolutions along with batch normalization and dropout regularization.<n>A convoluted array of evaluation metrics such as accuracy, precision, recall, and F1 score collectively verify the model exceptional performance by recording an accuracy rate of 91.
  arXiv  Detail & Related papers  (2025-09-26T11:58:50Z)
• AICRN: Attention-Integrated Convolutional Residual Network for Interpretable Electrocardiogram Analysis [0.4077139177290857]
  This work proposes a novel deep learning architecture called the attention-integrated convolutional residual network (AICRN) to regress key ECG parameters.<n>Our architecture is specially designed with spatial and channel attention-related mechanisms to address the type and spatial location of the ECG features for regression.<n>The designed system addresses traditional analysis challenges, such as loss of focus due to human errors, and facilitates the fast and easy detection of cardiac events.
  arXiv  Detail & Related papers  (2025-08-16T21:10:45Z)
• Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics [40.044978986425676]
  The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accuracy.<n>We propose a novel unsupervised anomaly generation methodology that takes into account the engine physics model.<n>We propose Signature-Guided Data Augmentation (SGDA), an unsupervised framework that synthesizes physically plausible faults directly in the frequency domain of healthy current signals.
  arXiv  Detail & Related papers  (2025-06-10T03:36:16Z)
• The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective [10.884863227198975]
  We systematically synthesize the latest OCT/A and AO developments, AI/ML approaches, and mHealth/Tele-ophthalmology initiatives.<n>We propose a roadmap for multicenter protocol standardization, prospective validation trials, and seamless incorporation of retinal screening into primary and specialty care pathways.
  arXiv  Detail & Related papers  (2025-05-27T07:19:37Z)
• Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography [0.0]
  This study presents a comprehensive evaluation of radiomics-based and deep learning-based approaches for disease detection in chest radiography.<n>It focuses on COVID-19, lung opacity, and viral pneumonia.<n>The results aim to inform the integration of AI-driven diagnostic tools in clinical practice.
  arXiv  Detail & Related papers  (2025-04-16T16:54:37Z)
• The Impact of Artificial Intelligence on Emergency Medicine: A Review of Recent Advances [0.2544903230401084]
  Artificial Intelligence (AI) is revolutionizing emergency medicine by enhancing diagnostic processes and improving patient outcomes.<n>Machine learning and deep learning are pivotal in interpreting complex imaging data, offering rapid, accurate diagnoses and potentially surpassing traditional diagnostic methods.<n>Despite these advancements, the integration of AI into clinical practice presents challenges such as data privacy, algorithmic bias, and the need for extensive validation across diverse settings.
  arXiv  Detail & Related papers  (2025-03-17T17:45:00Z)
• Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging [0.0]
  The AUTO-PCOS Classification Challenge seeks to advance the diagnostic capabilities of artificial intelligence (AI) in identifying Polycystic Ovary Syndrome (PCOS)<n>This report outlines our methodology for building a robust AI pipeline utilizing transfer learning with the InceptionV3 architecture to achieve high accuracy in binary classification.
  arXiv  Detail & Related papers  (2024-12-30T11:56:11Z)
• Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithms [0.0]
  Machine Learning (ML) and Deep Learning (DL) have opened new prospects in medical sciences for improved diagnosis, prognosis, and treatment of severe health conditions.<n>This paper focuses on the development of an ML model with high predictive accuracy to classify arrhythmic electrocardiogram (ECG) signals.
  arXiv  Detail & Related papers  (2024-12-07T08:29:44Z)
• Explainable Artificial Intelligence for Medical Applications: A Review [42.33274794442013]
  This article reviews recent research grounded in explainable artificial intelligence (XAI)<n>It focuses on medical practices within the visual, audio, and multimodal perspectives.<n>We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.
  arXiv  Detail & Related papers  (2024-11-15T11:31:06Z)
• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  Synthetic Data Generation based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered.<n>This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images.<n>The results show that TIDE-II generates clinically plausible, very realistic WCE images, of improved quality compared to relevant state-of-the-art generative models.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring.<n>As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs.<n>The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis [42.624671531003166]
  We introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks.
  arXiv  Detail & Related papers  (2022-08-19T12:06:46Z)
• Detecting Spurious Correlations with Sanity Tests for Artificial Intelligence Guided Radiology Systems [22.249702822013045]
  A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset. We describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons.
  arXiv  Detail & Related papers  (2021-03-04T14:14:05Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.