Surgery

Development and validation of a nomogram combining PI-RADS v2.1 and clinical indicators for the diagnosis of prostate cancer in patients with PSA ≤ 20 ng/mL.

PURPOSE: This investigation focused on developing a predictive clinical tool that combines biparametric MRI-derived PI-RADS v2.1 assessments with patient-specific biomarkers. The model was designed to optimize prostate cancer detection reliability in individuals exhibiting prostate-specific antigen concentrations below 20 ng/mL, particularly targeting the diagnostic challenges presented by this in...

Radiomics-based IVIM-DWI for early noninvasive assessment of renal allograft dysfunction.

OBJECTIVE: To develop and rigorously validate radiomics-based predictive models using postoperative intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) MRI for the early, noninvasive assessment of impaired renal allograft function (IRF) in kidney transplant recipients. METHODS: This retrospective study included 97 kidney transplant recipients (mean age, 36.77 ± 10.71 years), categor...

Explainable machine learning models for early prediction of acute kidney injury after cardiac surgery.

BACKGROUND: This study aimed to develop a data-driven prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI) at the end of surg...

Artificial Intelligence 3D Augmented Reality-guided Robotic Prostatectomy Versus Cognitive MRI Intervention: Results of the Prospective Randomized RIDERS Trial.

BACKGROUND AND OBJECTIVE: Three-dimensional (3D) augmented reality (AR) and artificial intelligence (AI) technologies have recently been introduced to...

Predictive value of artificial intelligence-based quantitative CT feature analysis for diagnosing the pathological types of pulmonary nodules.

OBJECTIVES: Accurate preoperative classification of pulmonary nodules (PNs) is critical for guiding clinical decision-making and preventing overtreatm...

The effect of autonomous mobile robots on improving endoscope reprocessing efficiency: a prospective comparative trial (with video).

BACKGROUND AND AIMS: Autonomous mobile robots (AMRs) have an increasingly wide range of medical applications. However, their use in endoscopy centers ...

Radiomics-powered radiographic image analysis for enhanced mechanical complications prediction and surgical planning in adult spine deformity.

BACKGROUND CONTEXT: Radiomics, a technique employing machine learning (ML) to extract quantitative features from processed radiographic images, holds ...

Accuracy of Robot-Guided Access Cavity Preparation in Managing Calcified Canals: An In Vitro Study.

AIM: To evaluate the accuracy of guided access cavity preparation in calcified canals using a robotic system, compared to static and dynamic guided me...

Prognostic prediction of hepatoblastoma in children: development and validation of machine learning models-an SEER-based study.

Hepatoblastoma (HB) is the most common primary malignant liver tumor in children. Although the incidence is low, it is a serious threat to children's ...

Prevalence and Characteristics of Generative Artificial Intelligence Policies in Vascular Surgery Journals: A Cross-Sectional Review.

BACKGROUND: The integration of generative artificial intelligence (GenAI) into academic publishing presents new opportunities and ethical challenges. ...

Robotic rehabilitation and intelligent algorithms improving the performance skills of stroke patients: a scoping review.

BACKGROUND: This scoping review highlights major advances and persisting gaps in robotic and AI-driven rehabilitation for stroke, evaluating their imp...

Patient Variability Drives Postoperative Outcome Volatility More Than Surgeon or Indication: A Bayesian Simulation Study of PROMIS Global Health for Lumbar Spinal Stenosis.

BACKGROUND AND OBJECTIVES: Patient-reported outcome measures (PROMs) are ubiquitously used to assess surgical success after surgery for lumbar spinal ...

Genomic and Transcriptomic Profiling of High-risk Bladder Cancer Reveals Diverse Molecular and Microenvironment Ecosystems.

BACKGROUND AND OBJECTIVE: High-risk bladder cancer recurs in 30% of cases and causes fatal outcomes in 10% within 2 yr despite surgical resection, end...

Illuminating Mouse Renal Proximal Tubule Architecture through High-Resolution Volume EM and Machine Learning Analysis.

KEY POINTS: High-resolution 3D imaging reveals new features of proximal tubule ultrastructure that suggested mechanisms for regulating kidney function...

Fat Repositioning with Deep Fat Excision in Transcutaneous Lower Blepharoplasty.

BACKGROUND: Lower blepharoplasty has shifted from fat resection to preservation by means of repositioning, yet severe fat herniation still requires se...

Anesthesia and mitochondria: balancing toxicity and protection through emerging therapeutic strategies.

Anesthesia is a cornerstone of modern surgical practice, enabling interventions by deliberately modulating nociception and consciousness-from localize...

Predicting treatment pathways in Class II malocclusion patients using machine learning: A comparative study of four algorithms for classifying camouflage, growth modulation, and surgical decisions.

OBJECTIVES: The aim of this study was to develop a machine-learning model to assist in treatment decision-making for surgery, camouflage, and growth m...

Deep Learning for Automatic Segmentation of Pituitary Adenomas: A Videomics Study.

BACKGROUND AND OBJECTIVES: Videomics, which integrates video-endoscopy and artificial intelligence, presents significant potential for real-time surgi...

Comparative Analysis of AI-Generated and Manually Designed Approaches in Accuracy and Design Time for Surgical Path Planning of Dynamic Navigation-Aided Endodontic Microsurgery: A Retrospective Study.

AIM: To compare the accuracy and design time of artificial intelligence (AI)-generated and manually designed (MD) surgical pathways for osteotomies an...

Interpretable artificial intelligence to predict preoperative risk factors for failure to rescue after coronary artery bypass grafting.

OBJECTIVE: Failure to rescue (FTR) is a significant quality indicator for postoperative cardiothoracic care. We developed an interpretable artificial ...