Surgery

The Year in Perioperative Echocardiography: Selected Highlights from 2024.

This article is the ninth of an annual series reviewing the research highlights of the year pertaining to the subspecialty of perioperative echocardiography for the Journal of Cardiothoracic and Vascular Anesthesia. The authors thank the editor-in-chief, Dr. Kaplan, and the editorial board for the opportunity to continue this series. In most cases, these will be research articles that are targeted...

Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children.

Disseminated intravascular coagulation (DIC) is a thrombo-hemorrhagic disorder that can be life-threatening in critically ill children, and the quest for an accurate and efficient method for early DIC prediction is of paramount importance. Candidate predictors encompassed demographics, comorbidities, laboratory findings, and therapy strategies. A stepwise logistic regression model was employed to ...

Solving Complex Pediatric Surgical Case Studies: A Comparative Analysis of Copilot, ChatGPT-4, and Experienced Pediatric Surgeons' Performance.

The emergence of large language models (LLMs) has led to notable advancements across multiple sectors, including medicine. Yet, their effect in pediat...

The future outlook for data in orthopedic surgery: A new era of real-time innovation.

The orthopedic field is on the brink of a significant transformation-a shift from retrospective analysis to real-time decision-making fueled by data. ...

Right ventricular dysfunction following tetralogy of Fallot correction: anatomical determinants and therapeutic strategies.

Right ventricular dysfunction following surgical correction of tetralogy of Fallot (TOF) remains a major determinant of long-term morbidity and mortal...

Updates in Neonatal Seizures.

Neonatal seizures are a common medical emergency, necessitating prompt treatment. The most common etiologies include hypoxic-ischemic encephalopathy, ...

Generation of surgical reports for lymph node dissection during laparoscopic gastric cancer surgery based on artificial intelligence.

PURPOSE: This study aimed to develop an artificial intelligence (AI) model for the surgical report output of laparoscopic lymph node dissection in the...

Multiparametric MRI and transfer learning for predicting positive margins in breast-conserving surgery: a multi-center study.

This study aimed to predict positive surgical margins in breast-conserving surgery (BCS) using multiparametric MRI (mpMRI) and radiomics. A retrospect...

Development, validation, and clinical evaluation of a machine-learning based model for diagnosing early infection after cardiovascular surgery (DEICS): a multi-center cohort study.

BACKGROUND: This study addresses the critical need for timely and accurate diagnosis of early postoperative infection (EPI) following cardiac surgery....

The AI-enhanced surgeon - integrating black-box artificial intelligence in the operating room.

New artificial intelligence (AI)/machine learning (ML) technology offers great potential to assist surgeons with real-time intra-operative decision-ma...

Improved gated recurrent unit-based osteosarcoma prediction on histology images: a meta-heuristic-oriented optimization concept.

The major prevalent primary bone cancer is osteosarcoma. Preoperative chemotherapy is accompanied by resection as part of the normal course of treatme...

Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.

BACKGROUND: Non-small-cell lung cancer (NSCLC) and its surgery significantly increase the venous thromboembolism (VTE) risk. This study explored the V...

Impact of AI on Access to Care a Three-Pronged Approach for Enhancing Access to Surgical Care: Lessons From Electrical Safety and the Impact of AI on Health Equity.

As we enter the 21st century, a new wave of transformation is occurring in health care, mainly through artificial intelligence (AI). Like electricity,...

Artificial intelligence for intraoperative video analysis in robotic-assisted esophagectomy.

BACKGROUND: Robotic-assisted minimally invasive esophagectomy (RAMIE) is a complex surgical procedure for treating esophageal cancer. Artificial intel...

Evidence-based artificial intelligence: Implementing retrieval-augmented generation models to enhance clinical decision support in plastic surgery.

The rapid advancement of large language models (LLMs) has generated significant enthusiasm within healthcare, especially in supporting clinical decisi...

Optimizing intraoperative AI: evaluation of YOLOv8 for real-time recognition of robotic and laparoscopic instruments.

The accurate recognition of surgical instruments is essential for the advancement of intraoperative artificial intelligence (AI) systems. In this stud...

Automated detection of metastatic lymph nodes in head and neck malignant tumors on high - resolution MRI images using an improved convolutional neural network.

PURPOSE: To develop an AI-based diagnostic model for assessing cervical lymph nodes in head and neck malignant tumors using MRI, enabling non-invasive...

Deep Learning Based on Ultrasound Images Differentiates Parotid Gland Pleomorphic Adenomas and Warthin Tumors.

Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurat...

A predictive model for MGMT promoter methylation status in glioblastoma based on terahertz spectral data.

O-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a crucial biomarker in glioblastoma (GBM) that influences response to temozolomid...

The Prediction of Venous Thromboembolism Using Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Systematic Review.

BACKGROUND: Venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, is a common and serious complication following lower ...