Surgery

Exploring non-invasive biomarkers for pulmonary nodule detection based on salivary microbiomics and machine learning algorithms.

Microorganisms are one of the most promising biomarkers for cancer, and the relationship between microorganisms and lung cancer occurrence and development provides significant potential for pulmonary nodule (PN) diagnosis from a microbiological perspective. This study aimed to analyze the salivary microbiota features of patients with PN and assess the potential of the salivary microbiota as a non-...

Explainable machine learning to compare the overall survival status between patients receiving mastectomy and breast conserving surgeries.

The most prevalent malignancy among women is breast cancer; hence, treatment approaches are needed in consideration of tumor characteristics and disease stage but also patient preference. Two surgical options, Mastectomy and Breast Conserving Surgery (BCS), share the same survival outcomes, clinical or molecular factors; and explainable Machine Learning (ML) techniques like SHapley Additive exPlan...

Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.

BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study ...

The Role of 3D technology in corrective osteotomy for forearm malunion.

Forearm malunion affects upper limb function, impairing rotation, grip strength, and dexterity. Traditional osteotomies, based on two-dimensional imag...

Robotic hand surgery: current insights and future directions.

Robotic surgery is a rapidly evolving field that will potentially play a pivotal role in future patient care. There are specific challenges that needs...

Development and Validation of Models for Preoperative Prediction of Risk and Postoperative Detection of Noninfectious Complications Using Interpretable Machine Learning and Electronic Health Record Data.

OBJECTIVE: To apply interpretable machine learning methodology to electronic health record data to develop models for preoperative risk estimation and...

Predicting hemorrhagic transformation in acute ischemic stroke: a systematic review, meta-analysis, and methodological quality assessment of CT/MRI-based deep learning and radiomics models.

Acute ischemic stroke (AIS) is a major cause of mortality and morbidity, with hemorrhagic transformation (HT) as a severe complication. Accurate predi...

A new era in nephrology: the role of super-resolution microscopy in research, medical diagnostic, and drug discovery.

For decades, electron microscopy has been the primary method to visualize ultrastructural details of the kidney, including podocyte foot processes and...

Early prediction of intraventricular hemorrhage in very low birth weight infants using deep neural networks with attention in low-resource settings.

Early prediction of intraventricular hemorrhage (IVH) in very low-birthweight infants (VLBWIs) remains challenging because of multifactorial risk fact...

Proof-of-Concept Prompted Large Language Model for Radiology Procedure Request Routing.

PURPOSE: To measure the accuracy and cost of a proof-of-concept prompted large language model to route procedure requests to the appropriate phone num...

Artificial intelligence enhanced Chatbot boom: A single center observational study to evaluate assistance in clinical anesthesiology.

BACKGROUND AND AIMS: The field of anaesthesiology and perioperative medicine has explored advancements in science and technology, ensuring precision a...

Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.

Multimodal artificial intelligence (AI) has the potential to revolutionise healthcare by enabling the simultaneous processing and integration of vario...

A stacking ensemble machine learning model for predicting postoperative axial pain intensity in patients with degenerative cervical myelopathy.

Machine learning (ML) has been extensively utilized to predict complications associated with various diseases. This study aimed to develop ML-based cl...

Application of machine learning models to identify predictors of good outcome after laparoscopic fundoplication.

BACKGROUND: Laparoscopic fundoplication remains the gold standard treatment for gastroesophageal reflux disease. However, 10% to 20% of patients exper...

Rapid, non-invasive breath analysis for enhancing detection of silicosis using mass spectrometry and interpretable machine learning.

Occupational lung diseases, such as silicosis, are a significant global health concern, especially with increasing exposure to engineered stone dust. ...

Innovative Technologies for Smarter and Efficient Operating Room Scheduling.

An optimized scheduling system for surgical procedures is considered fundamental for maximizing hospital resource utilization and improving patient ou...

Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy.

New bronchoscopy techniques like radial probe endobronchial ultrasound have been developed for real-time sampling characterization, but their use is s...

Predicting postoperative nausea and vomiting using machine learning: a model development and validation study.

BACKGROUND: Postoperative nausea and vomiting (PONV) is a frequently observed complication in patients undergoing surgery under general anesthesia. Mo...

Perioperative risk assessment for emergency general surgery in those with multimorbidity or frailty.

PURPOSE OF REVIEW: This review explores advances in risk stratification tools and their applicability in identifying and managing high-risk emergency ...

Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systems.

Effective weed management is crucial for maintaining optimal crop growth and achieve higher yield. Recent advancement in robotic technologies and adva...