AI Article Synopsis
Article Abstract
: Aortic dissection (AD) and aortic intramural hematoma (IMH) are fatal diseases with similar clinical characteristics. Immediate computed tomography (CT) with a contrast medium is required to confirm the presence of AD or IMH. This retrospective study aimed to use CT images to differentiate AD and IMH from normal aorta (NA) using a deep learning algorithm. : A 6-year retrospective study of non-contrast chest CT images was conducted at a university hospital in Seoul, Republic of Korea, from January 2016 to July 2021. The position of the aorta was analyzed in each CT image and categorized as NA, AD, or IMH. The images were divided into training, validation, and test sets in an 8:1:1 ratio. A deep learning model that can differentiate between AD and IMH from NA using non-contrast CT images alone, called YOLO (You Only Look Once) v4, was developed. The YOLOv4 model was used to analyze 8881 non-contrast CT images from 121 patients. : The YOLOv4 model can distinguish AD, IMH, and NA from each other simultaneously with a probability of over 92% using non-contrast CT images. : This model can help distinguish AD and IMH from NA when applying a contrast agent is challenging.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11594775 | PMC |
| http://dx.doi.org/10.3390/jcm13226868 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Deep Learning-Driven Optimization of Antihypertensive Properties from Whey Protein Hydrolysates: A Multienzyme Approach.
J Agric Food Chem
December 2024
College of Food Science and Light Industry, Nanjing Tech University, Nanjing 211816, China.
This study utilized deep learning to optimize antihypertensive peptides from whey protein hydrolysate. Using the Large Language Models (LLMs), we identified an optimal multienzyme combination (MC5) with an ACE inhibition rate of 89.08% at a concentration of 1 mg/mL, significantly higher than single-enzyme hydrolysis.
View Article and Find Full Text PDFDeep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery.
J Chem Inf Model
December 2024
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Enzymes are ubiquitous catalysts with enormous application potential in biomedicine, green chemistry, and biotechnology. However, accurately predicting whether a molecule serves as a substrate for a specific enzyme, especially for novel entities, remains a significant challenge. Compared with traditional experimental methods, computational approaches are much more resource-efficient and time-saving, but they often compromise on accuracy.
View Article and Find Full Text PDFA machine learning model for predicting abnormal liver function induced by a Chinese herbal medicine preparation (Zhengqing Fengtongning) in patients with rheumatoid arthritis based on real-world study.
J Integr Med
December 2024
Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China. Electronic address:
Objective: Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients' quality of life. Zhengqing Fengtongning (ZF) is a traditional Chinese medicine preparation used to treat RA. ZF may cause liver injury.
View Article and Find Full Text PDFConcordance-based Predictive Uncertainty (CPU)-Index: Proof-of-concept with application towards improved specificity of lung cancers on low dose screening CT.
Artif Intell Med
December 2024
Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America; Medical Physics Graduate Program, Duke University, Durham, NC, United States of America; Department of Radiology, Duke University, Durham, NC, United States of America; Department of Biomedical Engineering, Duke University, Durham, NC, United States of America; Department of Radiation Oncology, Duke University, Durham, NC, United States of America; Department of Pathology, Duke University, Durham, NC, United States of America. Electronic address:
In this paper, we introduce a novel concordance-based predictive uncertainty (CPU)-Index, which integrates insights from subgroup analysis and personalized AI time-to-event models. Through its application in refining lung cancer screening (LCS) predictions generated by an individualized AI time-to-event model trained with fused data of low dose CT (LDCT) radiomics with patient demographics, we demonstrate its effectiveness, resulting in improved risk assessment compared to the Lung CT Screening Reporting & Data System (Lung-RADS). Subgroup-based Lung-RADS faces challenges in representing individual variations and relies on a limited set of predefined characteristics, resulting in variable predictions.
View Article and Find Full Text PDFDLBCL cells with ferroptosis morphology can be detected with a deep convolutional neural network.
Biomed Pharmacother
December 2024
Medical Research Center, Oulu University Hospital, Oulu, Finland; Department of Internal Medicine, Länsi-Pohja Central Hospital, Kemi, Finland; Biomedicine and Internal Medicine Research Unit, University of Oulu, Oulu, Finland.
It has been demonstrated that diffuse large B-cell lymphoma (DLBCL) is especially sensitive to ferroptosis. Currently, confirming the presence of ferroptosis requires flow cytometry, which is a time consuming and labor-intensive task. Blistering of the cell membrane has been shown to be a ferroptosis-specific morphological change.
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!