Surgery

Automated hemodynamic management system for the prevention of hypotension and cardiac output reduction during general anesthesia: preclinical experimental validation.

PURPOSE: Intraoperative hypotension and cardiac output (CO) reduction adversely affect outcomes and require cause-specific treatment against vasodilation and/or hemorrhage. However, tight hemodynamic management increases anesthesiologists' workloads. We aimed to develop an automated hemodynamic management system that maintains arterial pressure (AP) and CO according to the causes, and to experimen...

An Australian perspective of using video for the assessment of laparoscopic surgery and support for artificial intelligence in performance evaluation.

Gene expression and machine learning techniques uncover corneal biomarkers associated with oxidative stress in the myopia progression.

This study aimed to identify and validate corneal biomarkers underlying myopia using human corneal tissues. Gene expression data were analyzed to inve...

Inverse treatment planning using deep learning-based organs at risk in radiotherapy for head and neck cancer: a prospective planning study.

PURPOSE: Radiotherapy (RT) planning for head and neck squamous cell carcinoma (HNSCC) is known to be both challenging and time-consuming. Deep learnin...

Image-Based Medical Navigation Systems for Cardiac Interventions: Recent Technological Advances.

BACKGROUND: The application of catheter-based treatments for a growing range of structural heart diseases (SHD) has significantly increased over the p...

Establishing a Sustainable Artificial Intelligence Center of Excellence for Health Care Systems: Part I-Strategic Frameworks, Infrastructure, and Performance Metrics.

Artificial Intelligence (AI) is rapidly transforming modern health care by enhancing data analysis, decision-making, and automation across clinical do...

Construction of an Intelligent Decision-Making Model for Laparoscopic Common Bile Duct Repair.

PURPOSE: This study aimed to develop a machine learning-based decision-support model to assist surgeons in intraoperatively selecting the optimal repa...

Machine learning-based prediction of postoperative delirium from intraoperative EEG signal alterations in brain functional connectivity.

OBJECTIVES: Postoperative delirium (POD) is a frequent complication following cardiovascular surgery and requires timely intervention. While early ris...

Practical Integration of Bedside Real-Time Lung Ultrasound and Electrical Impedance Tomography for Guiding Personalized Perioperative Lung Protective Ventilation in Cardiothoracic and Vascular Anesthesia.

Patients undergoing cardiothoracic and vascular surgery are at uniquely high risk for postoperative pulmonary complications due to the confluence of s...

OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations.

Despite significant advancements, segmentation based on deep neural networks in medical and surgical imaging faces several challenges, two of which we...

A Position Statement on Endovascular Models and Effectiveness Metrics for Mechanical Thrombectomy Navigation, on Behalf of the Stakeholder Taskforce for Artificial Intelligence-Assisted Robotic Thrombectomy (START).

Although we are making progress in overcoming infectious diseases and cancer, one of the major medical challenges of the mid-21st century will be the ...

Multiphysics learning with graph neural networks for thrombosis prediction in intracranial aneurysms.

Intracranial aneurysms are pathological dilations of cerebral arteries affecting 3-5% of the population. Their rupture can cause subarachnoid hemorrha...

Trends and disparities in female authorship in surgical publications: A large-scale bibliometric study using natural language processing.

BACKGROUND: Gender disparities in academic surgery persist, particularly in authorship positions that signal leadership, mentorship, and scholarly inf...

Artificial intelligence in the management of sports knee injuries: a narrative review.

INTRODUCTION: Sports-related knee injuries are common and debilitating, often leading to chronic pain, early osteoarthritis, and reduced performance. ...

Artificial Intelligence for Colorectal Surgeons - Part I: Foundations, Clinical and Educational Applications.

BACKGROUND: Artificial intelligence (AI) is rapidly transforming colorectal surgery through applications spanning screening to postoperative care. Thi...

The Effect of Parrot Therapy on During Venipuncture Pain in Chronic Patients.

OBJECTIVE: There are no randomized controlled trial (RCT) in the current literature comparing live, robotic, and visual animal-based therapies. This s...

Wearable Devices and Artificial Intelligence to Enhance Perioperative Care in Total Knee Arthroplasty.

Rapidly evolving wearable devices have the potential to transform health care delivery and enhance outcomes. Applications of wearable smart devices ra...

Toward Opioid-Free Ambulatory Surgery: A Prospective Study Using Machine Learning to Predict Postoperative Opioid Use.

BACKGROUND: Postoperative opioid use has the risk of dependence and diversion. We developed an opioid-sparing regimen and identified factors associate...

Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework.

Internal and external validation of a machine learning algorithm to detect preeclampsia-related adverse outcomes in high-risk pregnancies.

OBJECTIVES: This study aimed to refine an existing machine learning (ML) algorithm for predicting preeclampsia-related adverse outcomes and to assess ...