Surgery

Machine Learning-Based Identification of Craniosynostosis From Nonsynostotic Cranial Deformities.

ObjectiveCraniosynostosis, the premature fusion of cranial sutures, affects 5.2 cases per 10,000 live births and can result in impaired neurodevelopment if not treated early, often necessitating surgery. The absence of efficient screening tools for pediatricians during routine visits contributes to unnecessary referrals, treatment delays, and increased healthcare costs. Through machine learning (M...

Nonhomogeneous smoke mitigation in laparoscopic images using a hybrid physical-neural model.

Surgical smoke generated during electrocautery in minimally invasive surgery introduces nonhomogeneous optical scattering that compromises visibility and clinical interpretability of laparoscopic images. This work proposes a hybrid physics- and learning-based method for surgical smoke mitigation combining a convolutional neural network-vision transformer (CNN-ViT) architecture, which jointly estim...

AI-Generated Microlearning for Plastic Surgery Residency: Single-Arm Pre-Post Feasibility Study.

BACKGROUND: Surgical residency training faces mounting pressure from expanding subspecialty knowledge requirements alongside compressed learning oppor...

Predicting Patient-Reported Outcome Measures, Satisfaction, Healthcare Utilization, Mortality, and Return to Work After Total Knee Arthroplasty Using Machine Learning: A 14,900-Patient Study.

BACKGROUND: Patient-reported outcome measures and healthcare utilization metrics are increasingly used to evaluate the success of total knee arthropla...

ERDES: A Benchmark Video Dataset for Retinal Detachment and Macular Status Classification in Ocular Ultrasound.

Retinal detachment (RD) is a vision-threatening condition that requires prompt intervention to preserve sight. A critical factor in treatment urgency ...

Japan's contributions to hip joint preservation and reconstruction: from osteonecrosis and developmental dysplasia to precision arthroplasty.

PURPOSE: To review Japan's major contributions to hip joint preservation and reconstruction, spanning joint-preservingosteotomy, biomaterials innovati...

Forecasting knee arthroplasty surgery with deep learning : an integrated approach using routine clinical data and radiographs.

AIMS: We aimed to develop and validate a practical deep learning model integrating commonly collected clinical data and knee radiographs to predict th...

A Radiomics-Driven Model to Distinguish Between Clinically Similar Myxopapillary Ependymomas and Lumbosacral Schwannomas.

BACKGROUND AND OBJECTIVES: Myxopapillary ependymomas (MPE) and intradural lumbosacral schwannomas may be challenging to distinguish based on presentin...

Association Between the Hemoglobin-to-Red Blood Cell Distribution Width Ratio and Perioperative Mortality Risk in Myasthenia Gravis Patients with Sepsis: A Retrospective Cohort Study.

BackgroundThis study aims to explore the association between the Hemoglobin/Red Cell Distribution Width Ratio (HRR) and perioperative mortality in mya...

Artificial Intelligence for Predicting Perioperative Outcomes in Cardiac Surgery: A Systematic Review.

OBJECTIVE: Cardiac surgery carries a significant risk of complications and mortality. Artificial intelligence (AI), particularly machine learning (ML)...

Artificial Intelligence in Medical Writing: A Practical and Ethical Framework for Surgical Research and Publication.

BACKGROUND: Artificial intelligence (AI) is rapidly transforming surgical research and medical publishing by changing how clinicians discover, evaluat...

Vocal Outcomes After Cell-Based Outer Vocal Fold Implant in Porcine Bilateral Vocal Fold Resection.

OBJECTIVE: Cell-based outer vocal fold replacement (COVR) consists of human adipose-derived stem cells and is a potential treatment for severe vocal f...

From Prediction to Action: Why Bleeding Risk in Cardiac Surgery Still Eludes Us in the Era of Artificial Intelligence.

Perioperative bleeding in cardiac surgery remains common, consequential, and difficult to predict despite decades of research and expanding data avail...

Predicting Neonatal Respiratory Outcomes Using Machine Learning: A Systematic Review Through a Life Course Lens.

BACKGROUND: Neonatal respiratory outcomes remain leading drivers of neonatal intensive care unit (NICU) morbidity, mortality, and prolonged hospitaliz...

Clinical, biomechanical, and digital factors associated with refinement after the first series of clear aligners: A systematic review.

OBJECTIVES: To systematically identify, appraise, and synthesize the evidence on clinical, biomechanical, and digital factors associated with refineme...

Multimodal Machine Learning Model Predicting Postoperative Delirium Based on Heart Rate Variability: A Prospective Observational Study.

BACKGROUND: Postoperative delirium is a common and serious complication after general anesthesia; its accurate prediction remains a substantial challe...

Data-driven subtypes of postoperative delirium in surgical ICU patients: a secondary analysis of a prospective cohort study.

BACKGROUND: Postoperative delirium (POD) is a heterogeneous syndrome associated with adverse outcomes. Traditional classifications may obscure underly...

Artificial intelligence for comprehensive skeletal maturity assessment: a unified CBCT-based deep learning framework.

Skeletal maturity assessment is crucial, and the cervical vertebral maturity (CVM), spheno-occipital synchondrosis (SOS), and mid-palatal suture (MPS)...

Comparative effectiveness of arthroscopic-assisted uniportal surgery versus percutaneous endoscopic decompression in lumbar spinal stenosis.

Single-level lumbar spinal stenosis (LSS) that does not respond to conservative therapy is now of the standard care given due to minimal invasive deco...

Portable RF brain scanner enables stroke type detection using deep learning.

Stroke remains a leading cause of mortality worldwide, demanding rapid, accurate diagnosis; distinguishing stroke types matters because treatments dif...