PCI

Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks.

Blood coagulation, which involves a group of complex biochemical reactions, is a crucial step in hemostasis to stop bleeding at the injury site of a blood vessel. Coagulation abnormalities, such as hypercoagulation and hypocoagulation, could either cause thrombosis or hemorrhage, resulting in severe clinical consequences. Mathematical models of blood coagulation have been widely used to improve th...

Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals.

This research examines the application of non-invasive acoustic analysis for detecting obstructions in vascular access (fistulas) used by kidney dialysis patients. Obstructions in these fistulas can interrupt essential dialysis treatment. In this study, we utilized a condenser microphone to capture the blood flow sounds before and after angioplasty surgery, analyzing 3819 sound samples from 119 di...

Construction and verification of a machine learning-based prediction model of deep vein thrombosis formation after spinal surgery.

BACKGROUND: Deep vein thromboembolism (DVT) is a common postoperative complication with high morbidity and mortality rates. However, the safety and ef...

Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning.

Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting,...

Establishing a Classification System for Predicting Flow-Limiting Dissection After Balloon Angioplasty Using Explainable Machine-Learning Models: A Multicenter Retrospective Cohort Study.

OBJECTIVE: Percutaneous transluminal angioplasty (PTA) is the primary method for treatment in peripheral arterial disease. However, some patients expe...

Construction of Risk-Prediction Models for Autogenous Arteriovenous Fistula Thrombosis in Patients on Maintenance Hemodialysis.

INTRODUCTION: Autogenous arteriovenous fistula (AVF) is the preferred vascular access in patients undergoing maintenance hemodialysis (MHD). However, ...

Early Detection of Pulmonary Embolism in a General Patient Population Immediately Upon Hospital Admission Using Machine Learning to Identify New, Unidentified Risk Factors: Model Development Study.

BACKGROUND: Under- or late identification of pulmonary embolism (PE)-a thrombosis of 1 or more pulmonary arteries that seriously threatens patients' l...

Machine Learning Predicts Peripherally Inserted Central Catheters-Related Deep Vein Thrombosis Using Patient Features and Catheterization Technology Features.

This study aims to use patient feature and catheterization technology feature variables to train the corresponding machine learning (ML) models to pre...

Machine learning-based prediction model of lower extremity deep vein thrombosis after stroke.

This study aimed to apply machine learning (ML) techniques to develop and validate a risk prediction model for post-stroke lower extremity deep vein t...

Deep learning reconstruction for coronary CT angiography in patients with origin anomaly, stent or bypass graft.

PURPOSE: To develop and validate a deep learning (DL)-model for automatic reconstruction for coronary CT angiography (CCTA) in patients with origin an...

Predictive modeling of lower extreme deep vein thrombosis following radical gastrectomy for gastric cancer: based on multiple machine learning methods.

Postoperative venous thromboembolic events (VTEs), such as lower extremity deep vein thrombosis (DVT), are major risk factors for gastric cancer (GC) ...

Machine learning-based Cerebral Venous Thrombosis diagnosis with clinical data.

OBJECTIVES: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT r...

Machine learning-based model development for predicting risk factors of prolonged intra-aortic balloon pump therapy in patients with coronary artery bypass grafting.

Machine learning algorithms are frequently used to clinical risk prediction. Our study was designed to predict risk factors of prolonged intra-aortic ...

Risk prediction of cholangitis after stent implantation based on machine learning.

The risk of cholangitis after ERCP implantation in malignant obstructive jaundice patients remains unknown. To develop models based on artificial inte...

Can Artificial Intelligence Accurately Detect Urinary Stones? A Systematic Review.

To perform a systematic review on artificial intelligence (AI) performances to detect urinary stones. A PROSPERO-registered (CRD473152) systematic s...

Thrombosed Persistent Median Artery with Coexisting Bifid Median Nerve in a Robotic Arthroplasty Surgeon: A Case Report.

CASE: A 47-year-old orthopaedic surgeon presented with acute volar left wrist pain. He performed over 250 robot-assisted knee arthroplasties each year...

On the relationship between various anticoagulants and robot-assisted radical prostatectomy: a single-surgeon serial analysis.

Prostate cancer patients often have other health conditions and take anticoagulants. It was believed that surgery under anticoagulants could worsen su...

Predictive modeling of deep vein thrombosis risk in hospitalized patients: A Q-learning enhanced feature selection model.

Deep vein thrombosis (DVT) represents a critical health concern due to its potential to lead to pulmonary embolism, a life-threatening complication. E...

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review.

Khorana score (KS) is an established risk assessment model for predicting cancer-associated thrombosis. However, it ignores several risk factors and h...

Transcatheter Aortic Valve Replacement and Coronary Protection Guided by Deep Learning and 3-Dimensional Printing.

OBJECTIVE: In this case report, the auxiliary role of deep learning and 3-dimensional printing technology in the perioperative period was discussed to...