Surgery

The effect of human ratings reliability on machine learning model performance: a case study in infant pain assessment.

Human-annotated data is foundational for supervised machine learning (ML). Low inter-rater reliability often introduces noise that degrades model performance. This study investigates how human rating reliability and panel size impact ML efficacy, and introduces a novel debiasing procedure utilizing Random Effects Models (REMs) to mitigate annotator noise. We conducted two complementary experiments...

Comparing manual and automated methods for extracting perihematomal features in predicting hemorrhage expansion in deep intracerebral hemorrhage.

Functional evaluation in adult spinal deformity can be reduced to 3 key kinematic parameters that can sufficiently predict health-related quality of life outcomes: an observational prospective study.

BACKGROUND CONTEXT: Recently, functional evaluation using 3D gait analysis (3DGA) proved to predict health-related quality-of-life (HRQOL) scores bett...

Anatomy data driven mechanical adaptation of bone tunnel-screw interface in ACL reconstruction.

PURPOSE: Anatomical information about bone microstructure at the anterior cruciate ligament attachment sites could guide biomimetic strategies in ante...

Are machine learning models superior to logistic regression models to predict 30-day mortality post-hip fracture surgery?

Hip fractures represent a significant global health burden, with high mortality rates. Accurate prediction of 30-d postoperative mortality is instrume...

Digital twins in urological oncology and surgery: A review of emerging applications.

BACKGROUND: Digital twin technology represents a transformative approach in healthcare, creating virtual replicas of physical entities that enable rea...

AI-driven Predictive Models in Endourological Treatment of Urolithiasis: A Systematic Review of Nomogram Applications from EAU Endourology.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in endourology to enhance surgical planning, risk stratification, and outcome predict...

Predicting intracranial aneurysm rupture risk and intervention outcomes using denoising-enhanced CT Angiography.

BACKGROUND AND PURPOSE: To develop a comprehensive multi-modal framework for assessing the rupture risk of intracranial aneurysms and predicting inter...

Pfirrmann Grading and Features of Internal Derangement Identified in Discs Studied By Lumbar Discography in Patients with Chronic Low Back Pain.

BACKGROUND AND PURPOSE: Lumbar disc degeneration Pfirrmann Grade (PfGr) is commonly used to evaluate clinical studies assessing patient outcomes or ev...

Post-operative delirium risk estimation and assessment with machine learning: development and validation of the Open-DREAM Model: A peri-operative dataset analysis.

BACKGROUND: Postoperative delirium (POD) is a common and serious complication in older surgical patients, associated with increased morbidity, prolong...

Evaluation of an AI-Driven Risk Stratification System for Clinical Deterioration in Geriatric Hip Fracture Patients.

OBJECTIVES: To determine whether an artificial-intelligence-driven Clinical Deterioration Index (CDI) could identify geriatric hip-fracture patients a...

"Do as I say and not as I do: Surgical critical care program directors shape the future" authors' response to the Letter to the Editor entitled: "Artificial intelligence literacy and infectious diseases competency: Essential considerations for future revisions of the surgical critical care curriculum".

Machine Learning-Based Risk Stratification for Symptomatic Intracranial Hemorrhage and 3-month Prognosis Following Intravenous Thrombolysis.

INTRODUCTION: Intravenous thrombolysis (IVT) with tissue-type plasminogen activator (tPA) is a cornerstone of acute ischemic stroke treatment, yet its...

Predicting Pediatric Urological Surgery Duration Through Multimodal Patient-Physician Feature Fusion: Deep Learning Framework Incorporating Clinical Text Embedding.

BACKGROUND: Accurate prediction of surgical duration is critical for optimizing operating room scheduling and resource allocation. Existing models, ho...

Development of a risk prediction model for second primary thyroid cancer in female breast cancer patients based on machine learning algorithms.

Female breast cancer patients remain at risk of developing additional primary cancers even following surgical treatment. Second primary thyroid cancer...

A machine learning predictive model for staging chronic hepatitis B inflammation based on non-invasive metrics.

Early screening and antiviral therapy for significant liver inflammation in chronic hepatitis B (CHB) remain significant barriers as the key to diseas...

Future Directions: Artificial Intelligence and Digital Tools in Bladder Cancer Care.

This comprehensive review examines using artificial intelligence (AI) across the diagnostic, therapeutic, and prognostic landscape of bladder cancer. ...

Personalizing anesthetic management in trauma patients using machine learning.

PURPOSE OF THE REVIEW: Artificial intelligence in health is evolving rapidly, and there is a lot of hope that it may improve patient outcomes. The per...

Machine Learning Prediction of Hospital Stay in Pediatric Typhoid Intestinal Perforation: Pilot Study.

INTRODUCTION: Typhoid intestinal perforation (TIP) remains a significant cause of pediatric morbidity in resource-limited settings, with prolonged hos...

Development and Deployment of an Explainable Machine Learning Model for Preoperative Prediction of Thigh Liposuction Volume in Female Patients.

BACKGROUND: Accurate preoperative estimation of liposuction volume is essential for achieving optimal aesthetic outcomes and minimizing surgical compl...