Surgery

Predicting the complexity of minimally invasive liver resection for hepatocellular carcinoma using machine learning.

BACKGROUND: Despite technical advancements, minimally invasive liver surgery (MILS) for hepatocellular carcinoma (HCC) remains challenging. Nonetheless, effective tools to assess MILS complexity are still lacking. Machine learning (ML) models could improve the accuracy of such tools.

Natural Language Processing and soft data for motor skill assessment: A case study in surgical training simulations.

BACKGROUND AND OBJECTIVE: Automated surgical skill assessment using kinematic and video data (hard data) sources has been widely adopted in the literature. However, experts' opinions (soft data) in the form of free-text could be an invaluable source for evaluating one's skill level since the availability and semantic richness of the soft data are both higher than the hard data. In this paper, the ...

Machine Learning-Based Prediction of Early Complications Following Surgery for Intestinal Obstruction: Multicenter Retrospective Study.

BACKGROUND: Early complications increase in-hospital stay and mortality after intestinal obstruction surgery. It is important to identify the risk of ...

Prediction of Postoperative Macular Hole Status by Automated Preoperative Retinal OCT Analysis: A Narrative Review.

Optical coherence tomography (OCT) is a non-invasive imaging modality essential for macular hole (MH) management. Artificial intelligence (AI) algorit...

Machine-learning models for the prediction of ideal surgical outcomes in patients with adult spinal deformity.

AIMS: Adult spinal deformity (ASD) surgery can reduce pain and disability. However, the actual surgical efficacy of ASD in doing so is far from desira...

Diagnostic accuracy of convolutional neural network algorithms to distinguish gastrointestinal obstruction on conventional radiographs in a pediatric population.

PURPOSE: Gastrointestinal (GI) dilatations are frequently observed in radiographs of pediatric patients who visit emergency departments with acute sym...

Artificial intelligence and machine learning in knee arthroplasty.

BACKGROUND: Artificial intelligence (AI) and its subset, machine learning (ML), have significantly impacted clinical medicine, particularly in knee ar...

GALR1 and PENK serve as potential biomarkers in invasive non-functional pituitary neuroendocrine tumours.

BACKGROUND: Some nonfunctioning pituitary neuroendocrine tumor (NFPitNET) can show invasive growth, which increases the difficulty of surgery and indi...

Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique.

Surgical castration of males is carried out on a large scale in the US swine industry and the pain resulting from this procedure can be assessed using...

Clinical Application of Artificial Intelligence Preoperative Planning System Combined with Expert Database Retrieval in Complex Revision Hip Surgery.

Accurate preoperative planning in revision hip arthroplasty is crucial for achieving successful outcomes. To enhance the intuitive evaluation of aceta...

A Survey of Robotic Monocular Pose Estimation.

Robotic monocular pose estimation is an important part of neural monocular pose estimation-driven methods, which includes monocular simultaneous local...

Human-Centered Sensor Technologies for Soft Robotic Grippers: A Comprehensive Review.

The importance of bio-robotics has been increasing day by day. Researchers are trying to mimic nature in a more creative way so that the system can ea...

Artificial Intelligence in Breast Reconstruction: A Narrative Review.

Breast reconstruction following mastectomy or sectorectomy significantly impacts the quality of life and psychological well-being of breast cancer pat...

Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture.

BACKGROUND: Hip fractures have become a significant health concern, particularly among super-aged patients, who were at a high risk of postoperative p...

An advanced robotic system incorporating haptic feedback for precision cardiac ablation procedures.

This study introduces an innovative master-slave cardiac ablation catheter robot system that employs magnetorheological fluids. The system incorporate...

Using Machine Learning to Improve Readmission Risk in Surgical Patients in South Africa.

Unplanned readmission within 30 days is a major challenge both globally and in South Africa. The aim of this study was to develop a machine learning m...

Deep-learning tool for early identification of non-traumatic intracranial hemorrhage etiology and application in clinical diagnostics based on computed tomography (CT) scans.

BACKGROUND: To develop an artificial intelligence system that can accurately identify acute non-traumatic intracranial hemorrhage (ICH) etiology (aneu...

Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant.

Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized care. Alt...

Applications of Artificial Intelligence in Neurosurgery for Improving Outcomes Through Diagnostics, Predictive Tools, and Resident Education.

BACKGROUND: Artificial intelligence (AI) has become an increasingly prominent tool in the field of neurosurgery, revolutionizing various aspects of pa...

Artificial Intelligence Based Augmented Reality Navigation in Minimally Invasive Partial Nephrectomy.

OBJECTIVE: To explore the role of artificial intelligence based augmented reality intraoperative real-time navigation in minimally invasive partial ne...