Surgery

Machine learning models including preoperative and postoperative albumin-bilirubin score: short-term outcomes among patients with hepatocellular carcinoma.

BACKGROUND: We sought to assess the impact of various perioperative factors on the risk of severe complications and post-surgical mortality using a novel maching learning technique.

AI-powered prediction of HCC recurrence after surgical resection: Personalised intervention opportunities using patient-specific risk factors.

BACKGROUND: Hepatocellular carcinoma (HCC) recurrence following surgical resection remains a significant clinical challenge, necessitating reliable predictive models to guide personalised interventions. In this study, we sought to harness the power of artificial intelligence (AI) to develop a robust predictive model for HCC recurrence using comprehensive clinical datasets.

A tactile sensing system capable of recognizing objects based on bioinspired self-sensing soft pneumatic actuator.

Tactile sensors play an important role when robots perform contact tasks, such as physical information collection, force or displacement control to av...

Real-time prediction of postoperative spinal shape with machine learning models trained on finite element biomechanical simulations.

PURPOSE: Adolescent idiopathic scoliosis is a chronic disease that may require correction surgery. The finite element method (FEM) is a popular option...

Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study.

BACKGROUND:  Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalation...

Reliable prediction of implant size and axial alignment in AI-based 3D preoperative planning for total knee arthroplasty.

The size and axial alignment of prostheses, when planned during total knee replacement (TKA) are critical for recovery of knee function and improvemen...

Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures.

BACKGROUND: The frequency of hip and knee arthroplasty surgeries has been rising steadily in recent decades. This trend is attributed to an aging popu...

Deep Learning for Predicting the Difficulty Level of Removing the Impacted Mandibular Third Molar.

BACKGROUND: Preoperative assessment of the impacted mandibular third molar (LM3) in a panoramic radiograph is important in surgical planning. The aim ...

Risk prediction of kalaemia disturbance and acute kidney injury after total knee arthroplasty: use of a machine learning algorithm.

INTRODUCTION: Total knee arthroplasty (TKA) is a procedure associated with risks of electrolyte and kidney function disorders, which are rare but can ...

Classification of subtask types and skill levels in robot-assisted surgery using EEG, eye-tracking, and machine learning.

BACKGROUND: Objective and standardized evaluation of surgical skills in robot-assisted surgery (RAS) holds critical importance for both surgical educa...

Machine learning analysis of lab tests to predict bariatric readmissions.

The purpose of this study was to develop a machine learning model for predicting 30-day readmission after bariatric surgery based on laboratory tests....

AI-driven automatic compression system for colorectal anastomosis.

Although circular staplers offer technical advancements over traditional hand-sewn techniques, their use remains challenging for unskilled users, nece...

Predicting postoperative visual acuity in epiretinal membrane patients and visualization of the contribution of explanatory variables in a machine learning model.

BACKGROUND: The purpose of this study was to develop a model that can predict the postoperative visual acuity in eyes that had undergone vitrectomy fo...

Artificial intelligence/machine learning for neuroimaging to predict hemorrhagic transformation: Systematic review/meta-analysis.

BACKGROUND AND PURPOSE: Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for t...

Retrospective analysis of interpretable machine learning in predicting ICU thrombocytopenia in geriatric ICU patients.

We developed an interpretable machine learning algorithm that prospectively predicts the risk of thrombocytopenia in older critically ill patients dur...

Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.

OBJECTIVES: This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to ...

Pediatric cardiac surgery: machine learning models for postoperative complication prediction.

PURPOSE: Managing children undergoing cardiac surgery with cardiopulmonary bypass (CPB) presents a significant challenge for anesthesiologists. Machin...

A microdiscectomy surgical video annotation framework for supervised machine learning applications.

PURPOSE: Lumbar discectomy is among the most common spine procedures in the US, with 300,000 procedures performed each year. Like other surgical proce...

Validation of a novel, low-fidelity virtual reality simulator and an artificial intelligence assessment approach for peg transfer laparoscopic training.

Simulators are widely used in medical education, but objective and automatic assessment is not feasible with low-fidelity simulators, which can be sol...

Comparative Analysis of Milking and Behavior Characteristics of Multiparous and Primiparous Cows in Robotic Systems.

Robotic milking systems are successful innovations in the development of dairy cattle. The objective of this study was to analyse the milking characte...