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Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.
Int J Cardiovasc Imaging
December 2024
Division of Cardiothoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, Clinical Science Building, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC, 29425, USA.
Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scoring (CACS), but simultaneous demonstration of improved efficiency, accuracy, and clinical stability have not been demonstrated. 148 sequential patients who underwent routine calcium-scoring computed tomography were retrospectively evaluated using a previously validated AI model (syngo. CT CaScoring VB60, Siemens Healthineers, Forscheim, Germany).
View Article and Find Full Text PDFMachine learning based anoikis signature predicts personalized treatment strategy of breast cancer.
Front Immunol
December 2024
Research Laboratory Center, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
Background: Breast cancer remains a leading cause of mortality among women worldwide, emphasizing the urgent need for innovative prognostic tools to improve treatment strategies. Anoikis, a form of programmed cell death critical in preventing metastasis, plays a pivotal role in breast cancer progression.
Methods: This study introduces the Artificial Intelligence-Derived Anoikis Signature (AIDAS), a novel machine learning-based prognostic tool that identifies key anoikis-related gene patterns in breast cancer.
Creating New Proteins for Medical Therapeutics: Short title: Designing Therapeutic Proteins.
Am J Med
December 2024
Department of Medicine, University of California San Francisco Medical Center, San Francisco, CA. Electronic address:
Recent applications of artificial intelligence-derived methods of computational design have permitted de novo creation of proteins that do not exist in nature but have potent effects on human cells and organ systems. These rapid procedures also allow in one step protein modifications that optimize function, potency, stability, resistance to biodegradation, cellular and tissue distribution and biological half-time. Such proteins generated to date include cytokines, antibodies, inhibitors of cell death proteins and antagonists of extracellular receptors for growth factors and viruses.
View Article and Find Full Text PDFArtificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study.
Eur Heart J
November 2024
Division of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.
Background And Aims: Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk.
Methods: An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants).
Regulatory T cells-related gene in primary sclerosing cholangitis: evidence from Mendelian randomization and transcriptome data.
Genes Immun
December 2024
Department of Gastroenterology, The Central Hospital of Hengyang City, Hengyang, Hunan Province, PR China.
The present study utilized large-scale genome-wide association studies (GWAS) summary data (731 immune cell subtypes and three primary sclerosing cholangitis (PSC) GWAS datasets), meta-analysis, and two PSC transcriptome data to elucidate the pivotal role of Tregs proportion imbalance in the occurrence of PSC. Then, we employed weighted gene co-expression network analysis (WGCNA), differential analysis, and 107 combinations of 12 machine-learning algorithms to construct and validate an artificial intelligence-derived diagnostic model (Tregs classifier) according to the average area under curve (AUC) (0.959) in two cohorts.
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