Surgery

A magnetic resonance imaging (MRI)-based deep learning radiomics model predicts recurrence-free survival in lung cancer patients after surgical resection of brain metastases.

AIM: To develop and validate a magnetic resonance imaging (MRI)-based deep learning radiomics model (DLRM) to predict recurrence-free survival (RFS) in lung cancer patients after surgical resection of brain metastases (BrMs).

Evaluating artificial intelligence in decision-making for surgical treatment of benign breast conditions.

INTRODUCTION: Determining the ideal surgical strategy for breast pathologies can sometimes be challenging, in particular if personalized solutions are required. Moreover, acquiring the necessary expertise takes years. Artificial intelligence (AI) may help streamline decision-making and improve treatment approaches. This study assessed the potential role of AI in therapeutic planning for common bre...

The best machine learning algorithm for building surgical site infection predictive models: A systematic review and network meta-analysis.

BACKGROUND: Many machine learning (ML) algorithms have been used to develop surgical site infection (SSI) prediction models, but little is known about...

Inter-hospital transferability of AI: A case study on phase recognition in cholecystectomy.

BACKGROUND: Identifying surgical phases is a crucial component of surgical workflow analysis, facilitating the automated evaluation of surgical proced...

An Optimized Framework of QSM Mask Generation Using Deep Learning: QSMmask-Net.

Quantitative susceptibility mapping (QSM) provides the spatial distribution of magnetic susceptibility within tissues through sequential steps: phase ...

A Master-Follower Teleoperation System for Robotic Catheterisation: Design, Characterisation and Tracking Control.

BACKGROUND: Over the past 2 decades, telerobotic systems with robot-mediated, minimally invasive techniques, have mitigated radiation exposure for med...

Improving microsurgical suture training with automated phase recognition and skill assessment via deep learning.

Microsurgical suturing demands a high level of precision, skill, and extensive training to ensure success in delicate procedures. In this study, we cr...

Optimizing predictive model performance in adult spinal deformity surgery: a comparative head-to-head analysis of learning models for perioperative complications.

OBJECTIVE: The aim of this study was to develop and compare 4 predictive algorithms, including logistic regression (LR), random forest (RF), gradient ...

Use of Machine Learning to Compare Disease Risk Scores and Propensity Scores Across Complex Confounding Scenarios: A Simulation Study.

PURPOSE: The surge of treatments for COVID-19 in the second quarter of 2020 had a low prevalence of treatment and high outcome risk. Motivated by that...

Dynamic Locomotion Synchronization and Fuzzy Control of a Lower Limb Exoskeleton With Body Weight Support for Active Following Human Operator.

Despite remarkable progress in robotic exoskeletons, exoskeletons are still far from matching human-level guidance and locomotion performance in gait ...

Attention in surgical phase recognition for endoscopic pituitary surgery: Insights from real-world data.

BACKGROUND AND OBJECTIVE: Surgical Phase Recognition systems are used to support the automated documentation of a procedure and to provide the surgica...

Factors influencing the estimation of phacoemulsification procedure time in cataract surgery: Analysis using neural networks.

BACKGROUND AND OBJECTIVE: Realistic and accurate estimation of the surgery duration is one of the key factors influencing the optimization of hospital...

Development and validation of a predictive machine learning model for postoperative long-term diabetes insipidus following transsphenoidal surgery for sellar lesions.

OBJECTIVE: Diabetes Insipidus (DI) is a common complication that occurs following transsphenoidal surgery for sellar lesions. DI is usually transient ...

Current Status and Future Perspectives of Robot-Assisted Dental Implant Surgery.

Dental implant surgery is now a well versed approach for tooth replacement, addressing various limitations of fixed bridges and removable dentures, th...

Evolving Therapeutic Landscape of Intracerebral Hemorrhage: Emerging Cutting-Edge Advancements in Surgical Robots, Regenerative Medicine, and Neurorehabilitation Techniques.

Intracerebral hemorrhage (ICH) is the most serious form of stroke and has limited available therapeutic options. As knowledge on ICH rapidly develops,...

Prediction of endoscopic restenosis after endoscopic balloon dilation in patients with Crohn's disease: a machine learning approach.

BACKGROUND: Endoscopic balloon dilation (EBD) is recognized as a minimally invasive and effective procedure for managing intestinal stenosis in patien...

The impact of the large language model ChatGPT in oral and maxillofacial surgery: a systematic review.

This systematic review evaluates the impact of the large language model (LLM) ChatGPT in oral and maxillofacial surgery. Following PRISMA guidelines a...

Multi-layered data framework for enhancing postoperative outcomes and anaesthesia management through natural language processing.

Anaesthesia management is a critical aspect of perioperative care, directly influencing postoperative recovery, pain management, and patient outcomes....

Integrating Machine Learning and Dynamic Digital Follow-up for Enhanced Prediction of Postoperative Complications in Bariatric Surgery.

BACKGROUND: Traditional risk models, such as POSSUM and OS-MS, have limited accuracy in predicting complications after bariatric surgery. Machine lear...

Automatic gesture recognition and evaluation in peg transfer tasks of laparoscopic surgery training.

BACKGROUND: Laparoscopic surgery training is gaining increasing importance. To release doctors from the burden of manually annotating videos, we propo...