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Aortic Aneurysm, Abdominal

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Integration of bulk/scRNA-seq and multiple machine learning algorithms identifies PIM1 as a biomarker associated with cuproptosis and ferroptosis in abdominal aortic aneurysm.

Frontiers in immunology
BACKGROUND: Abdominal aortic aneurysm (AAA) is a serious life-threatening vascular disease, and its ferroptosis/cuproptosis markers have not yet been characterized. This study was aiming to identify markers associated with ferroptosis/cuproptosis in ...

Automatic CTA analysis for blood vessels and aneurysm features extraction in EVAR planning.

Scientific reports
Endovascular Aneurysm Repair (EVAR) is a minimally invasive procedure crucial for treating abdominal aortic aneurysms (AAA), where precise pre-operative planning is essential. Current clinical methods rely on manual measurements, which are time-consu...

Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi-omics data and machine learning analysis.

IET systems biology
Abdominal aortic aneurysm (AAA) is a severe vascular condition, marked by the progressive dilation of the abdominal aorta, leading to rupture if untreated. The objective of this study was to identify key biomarkers and decipher the immune mechanisms ...

Prediction of Two Year Survival Following Elective Repair of Abdominal Aortic Aneurysms at A Single Centre Using A Random Forest Classification Algorithm.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
OBJECTIVE: The decision to electively repair an abdominal aortic aneurysm (AAA) involves balancing the risk of rupture, peri-procedural death, and life expectancy. Random forest classifiers (RFCs) are powerful machine learning algorithms. The aim of ...

Design of a Cost-Effective Ultrasound Force Sensor and Force Control System for Robotic Extra-Body Ultrasound Imaging.

Sensors (Basel, Switzerland)
Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering...

Deep learning-aided diagnosis of acute abdominal aortic dissection by ultrasound images.

Emergency radiology
PURPOSE: Acute abdominal aortic dissection (AD) is a serious disease. Early detection based on ultrasound (US) can improve the prognosis of AD, especially in emergency settings. We explored the ability of deep learning (DL) to diagnose abdominal AD i...

Prediction of Aneurysm Sac Shrinkage After Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis.

The Journal of surgical research
INTRODUCTION: A simple risk stratification model to predict aneurysm sac shrinkagein patients undergoing endovascular aortic repair (EVAR) for abdominal aortic aneurysms (AAA) was developed using machine learning-based decision tree analysis.

Identification and Experimental Validation of NETosis-Mediated Abdominal Aortic Aneurysm Gene Signature Using Multi-omics, Machine Learning, and Mendelian Randomization.

Journal of chemical information and modeling
Abdominal aortic aneurysm (AAA) is a life-threatening disorder with limited therapeutic options. Neutrophil extracellular traps (NETs) are formed by a process known as "NETosis" that has been implicated in AAA pathogenesis, yet the roles and prognost...

Evaluating the role of AI chatbots in patient education for abdominal aortic aneurysms: a comparison of ChatGPT and conventional resources.

ANZ journal of surgery
BACKGROUNDS: Abdominal aortic aneurysms (AAA) carry significant risks, yet patient understanding is often limited, with online resources typically low quality. ChatGPT, an artificial intelligence (AI) chatbot, presents a new frontier in patient educa...

Predicting outcomes following open abdominal aortic aneurysm repair using machine learning.

Scientific reports
Patients undergoing open surgical repair of abdominal aortic aneurysm (AAA) have a high risk of post-operative complications. However, there are no widely used tools to predict surgical risk in this population. We used machine learning (ML) technique...