AI Article Synopsis
- The study aimed to assess how effective automated deep learning is in detecting coronary artery disease (CAD) using photon-counting coronary CT angiography (PC-CCTA) images.
- A total of 140 patients were analyzed, showing that the AI models had high sensitivity and specificity, with deep learning achieving a notable accuracy in diagnosing significant CAD.
- The conclusion suggests that AI can significantly assist human experts in clinical practice by enhancing the evaluation of CAD through traditional imaging methods.
Article Abstract
Purpose: The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA).
Materials And Methods: Consecutive patients with suspected CAD who underwent PC-CCTA between January 2022 and December 2023 were included in this retrospective, single-center study. Non-ultra-high resolution (UHR) PC-CCTA images were analyzed by artificial intelligence using two deep learning models (CorEx, Spimed-AI), and compared to human expert reader assessment using UHR PC-CCTA images. Diagnostic performance for global CAD assessment (at least one significant stenosis ≥ 50 %) was estimated at patient and vessel levels.
Results: A total of 140 patients (96 men, 44 women) with a median age of 60 years (first quartile, 51; third quartile, 68) were evaluated. Significant CAD on UHR PC-CCTA was present in 36/140 patients (25.7 %). The sensitivity, specificity, accuracy, positive predictive value), and negative predictive value of deep learning-based CAD were 97.2 %, 81.7 %, 85.7 %, 64.8 %, and 98.9 %, respectively, at the patient level and 96.6 %, 86.7 %, 88.1 %, 53.8 %, and 99.4 %, respectively, at the vessel level. The area under the receiver operating characteristic curve was 0.90 (95 % CI: 0.83-0.94) at the patient level and 0.92 (95 % CI: 0.89-0.94) at the vessel level.
Conclusion: Automated deep learning shows remarkable performance for the diagnosis of significant CAD on non-UHR PC-CCTA images. AI pre-reading may be of supportive value to the human reader in daily clinical practice to target and validate coronary artery stenosis using UHR PC-CCTA.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.diii.2024.09.012 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Learning the language of antibody hypervariability.
Proc Natl Acad Sci U S A
January 2025
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.
View Article and Find Full Text PDFUnveiling hidden reaction kinetics of carbon dioxide in supercritical aqueous solutions.
Proc Natl Acad Sci U S A
January 2025
Department of Physics, The Hong Kong University of Science and Technology, Hong Kong, China.
Dissolution of CO in water followed by the subsequent hydrolysis reactions is of great importance to the global carbon cycle, and carbon capture and storage. Despite numerous previous studies, the reactions are still not fully understood at the atomistic scale. Here, we combined ab initio molecular dynamics (AIMD) simulations with Markov state models to elucidate the reaction mechanisms and kinetics of CO in supercritical water both in the bulk and nanoconfined states.
View Article and Find Full Text PDFQwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study.
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFThe role of chromatin state in intron retention: A case study in leveraging large scale deep learning models.
PLoS Comput Biol
January 2025
Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America.
Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources.
View Article and Find Full Text PDFA reliable unmanned aerial vehicle multi-ship tracking method.
PLoS One
January 2025
North China Institute of Aerospace Engineering, Langfang, China.
As the global economy expands, waterway transportation has become increasingly crucial to the logistics sector. This growth presents both significant challenges and opportunities for enhancing the accuracy of ship detection and tracking through the application of artificial intelligence. This article introduces a multi-object tracking system designed for unmanned aerial vehicles (UAVs), utilizing the YOLOv7 and Deep SORT algorithms for detection and tracking, respectively.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!