PCI

AI-Driven Personalization of Dual Antiplatelet Therapy Duration Post-PCI: A Novel Approach Balancing Ischemic and Bleeding Risks

Precision-guided dual antiplatelet therapy (DAPT) duration post-percutaneous coronary intervention (PCI) remains a clinical challenge. Current risk stratification methods lack personalization, underscoring the need for advanced predictive tools. We developed and validated an artificial intelligence (AI) framework (LightGBM, random forest [RF], logistic regression [LR]) to optimize DAPT duration us...

Two-Stage Machine Learning Based Prediction of Thrombophilia Management

Thrombophilia diagnosis and management rely on the nuanced interpretation of clinical history, risk factors, and laboratory data, yet significant variability exists in clinical practice due to subjective assessment, institutional differences, and limited consensus guidelines. In this retrospective study, we evaluated the use of a two-stage machine learning (ML) approach to predict thrombophilia di...

Modeling In-Hospital Mortality Among Patients Undergoing Percutaneous Coronary Intervention with Acute Myocardial Infarction Complicated by Cardiogenic Shock Receiving Mechanical Circulatory Support

Acute myocardial infarction complicated by cardiogenic shock (AMI-CS) is a heterogeneous clinical syndrome associated with substantial morbidity and m...

Machine learning model predicts new-onset lower extremity deep vein thrombosis after pelvic fracture surgery and targeted diagnosis

Postoperative new-onset deep vein thrombosis (PNO-DVT) of the lower extremities represents a prevalent and serious clinical complication following pel...

A Novel Generative Multi-Task Representation Learning Approach for Predicting Postoperative Complications in Cardiac Surgery Patients

Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identif...

Prognostic Effect of Trigeminal Neuralgia Treated With Percutaneous Balloon Compression by Machine Learning-based Modeling of Radiomic Morphological Features.

BACKGROUND: Trigeminal neuralgia (TN) is defined as spontaneous pain in the region of the trigeminal nerve that seriously affects a patient's quality ...

Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT

Venous thromboembolism (VTE) is a critical cardiovascular condition, encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). Accurate a...

AniBalloons: Animated Chat Balloons as Affective Augmentation for Social Messaging and Chatbot Interaction

Despite being prominent and ubiquitous, message-based interaction is limited in nonverbally conveying emotions. Besides emoticons or stickers, messa...

A preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.

OBJECTIVES: To investigate the usefulness of super-resolution deep learning reconstruction (SR-DLR) with cardiac option in the assessment of image qua...

Machine Learning Constructed Based on Patient Plaque and Clinical Features for Predicting Stent Malapposition: A Retrospective Study.

BACKGROUND: Stent malapposition (SM) following percutaneous coronary intervention (PCI) for myocardial infarction continues to present significant cli...

Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.

Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual medi...

Improved stent sharpness evaluation with super-resolution deep learning reconstruction in coronary CT angiography.

OBJECTIVES: This study aimed to assess the impact of super-resolution deep learning reconstruction (SR-DLR) on coronary CT angiography (CCTA) image qu...

Time Series Forecasting of Cardiovascular Mortality: Machine Learning Based on State Economic and Local Medical Data.

This study focuses on the complex interplay of healthcare, economic factors, and population dynamics, addressing a research gap in regional-level mode...

Enhancing Thrombophilia Risk Prediction Through AI-Based Methodologies.

Thrombophilia, a predisposition to thrombosis, poses significant diagnostic challenges due to its multi-factorial nature, encompassing genetic and acq...

Patient-Reported Outcomes for Robot-Assisted Laparoscopic Extravascular Renal Vein Stent Placements for Nutcracker Syndrome.

Nutcracker phenomenon is the compression of the left renal vein between the superior mesenteric artery (SMA) and the abdominal aorta. Nutcracker synd...

Comparison of machine learning models to predict complications of bariatric surgery: A systematic review.

Due to changes in lifestyle, bariatric surgery is expanding worldwide. However, this surgery has numerous complications, and early identification of ...

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

BACKGROUND: Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or e...

Transurethral Cylindrical Water Sac Prostate Enlargement Surgery for the Treatment of Small-Volume Benign Prostatic Hyperplasia: A Retrospective Analysis.

AIM: To investigate the clinical efficacy of transurethral columnar balloon dilation of prostate (TUCBDP) in the treatment of small-volume benign pros...

Changes of plasma Rap1A levels in patients with in-stent restenosis after percutaneous coronary intervention and the underlying mechanisms.

OBJECTIVES: Percutaneous coronary intervention (PCI) is one of the most important treatments for coronary artery disease (CAD). However, in-stent rest...

[A Case of Rectal Cancer with Bladder Invasion Performed with Robot-Assisted Low Anterior Resection after TNT].

A 73-year-old woman was admitted with a chief complaint of weight loss. Colonoscopy revealed rectal cancer. After the placement of a colonic stent, th...