Surgery

The Power of Multimodality in Multimodal Large Language Models, Unimodal ChatGPT 5.0, and Human Clinical Experts on a Wound Care Certification Examination: Cross-Sectional Comparative Study.

BACKGROUND: Multimodal large language models (MLLMs) capable of integrating visual and textual information represent a promising advancement for clinical applications requiring image interpretation. Wound care assessment, which demands simultaneous analysis of wound photographs and clinical data, provides an ideal domain to evaluate multimodal vs unimodal artificial intelligence capabilities again...

From Static Diagnosis to Dynamic Guidance : Evolution of Artificial Intelligence in Pediatric Neuroimaging.

The pediatric brain represents a dynamic biological target characterized by rapid myelination and functional reorganization, which presents unique challenges for conventional, adult-centric artificial intelligence (AI) models. This review provides a structured overview of the evolution of AI applications in pediatric neuroimaging and neurosurgery, tracing the transition from early standardized pip...

Preface : Digital and Robotic Transformation of Pediatric Neurosurgery.

This Pediatric Issue of the Journal of Korean Neurosurgical Society addresses the digital and robotic transformation of pediatric neurosurgery. It rev...

Deep learning-based Wilms tumor segmentation to create 3D models for surgical planning: Implementation in the clinical workflow.

BACKGROUND AND AIM: Creating 3D models based on pre-operative MRI of patients with a Wilms tumor (WT) can aid surgical planning. However, creating the...

Decoding the surgical scene: A scoping review of scene graphs in surgery.

As surgical AI transitions from pixel-level detection to complex reasoning, Scene Graphs (SGs) offer the structured, relational representations necess...

AI-assisted versus fully automated volumetry for hemorrhage quantification in spontaneous ICH with ventricular extension: A multi-center study.

BACKGROUND: Spontaneous intracerebral hemorrhage (sICH) with intraventricular hemorrhage (IVH) extension is a neurological emergency associated with h...

Emerging role of artificial intelligence in necrotizing enterocolitis and implementation challenges.

Necrotizing enterocolitis (NEC) remains a persistent clinical challenge, with diagnostic strategies largely relying on reactive staging criteria that ...

Prediction of survival in patients with spinal metastatic disease.

The spine is among the most frequent sites of metastatic disease, with an increasing prevalence, leading to increasing rates of surgical interventions...

Radiomics and artificial intelligence in pancreatic cyst characterization: future or fiction?

Pancreatic cystic lesions (PCLs) are increasingly detected due to the widespread use of cross-sectional imaging and represent a significant diagnostic...

An explainable clinical-radiomics machine learning model for preoperative prediction of WHO/ISUP nuclear grade in clear cell renal cell carcinoma.

BACKGROUND: Accurate preoperative assessment of the WHO/ISUP nuclear grade of clear cell renal cell carcinoma (ccRCC) is critical for guiding individu...

Evaluating the Utility and Limitations of Machine Learning Tumor Segmentation for Automated Longitudinal RANO Treatment Response Classification.

PURPOSE: Machine learning segmentation has emerged in tumor assessment with high performance in volumetric evaluation of brain tumors. It is unclear, ...

CT-guided robotic placement of multiple convective delivery cannulas.

OBJECTIVE: Emerging data indicate that the use of cranial robotics with CT guidance can provide an efficient and effective alternative for the simulta...

Artificial Intelligence in Minimally Invasive Gynecological Surgery: A Systematic Review of Task- Specific Performance and Clinical Translational Readiness.

OBJECTIVE: To systematically evaluate the task-specific performance and clinical translational readiness of artificial intelligence (AI) applications ...

External Validation of the PIPRA Model for Postoperative Delirium Prediction in a Japanese ICU Cohort: Integrating AI-Based Risk Assessment into Perioperative Care.

BACKGROUND: Postoperative delirium (POD) is a prevalent and serious complication in older surgical patients, linked to prolonged hospitalization, high...

Prediction of Pregnancy-Related Cardiovascular Outcomes Using Electrocardiogram-Based Deep Learning Estimation of Cardiorespiratory Fitness.

BACKGROUND: Peak oxygen consumption (peak VO2), the gold standard measure of cardiorespiratory fitness, may identify women at high risk for pregnancy-...

Real-time planar angiographic cerebral perfusion imaging.

PURPOSE: Digital Subtraction Angiography (DSA) is an X-ray-based imaging modality intimately related to minimally invasive procedures in interventiona...

Automated estimation of drain output in postoperative patients using deep learning on clinical images.

Postoperative drains are essential components of care in general surgery and intensive care units, where accurate monitoring of drain output is critic...

Multimodal neural operators for real-time biomechanical modelling of traumatic brain injury.

BACKGROUND: Traumatic brain injury (TBI) remains a major public health concern, with over 69 million cases annually worldwide. Accurate patient-specif...

Reconstruction of Under-Sampled Images and Concurrent Optimization of Sampling Masks for 3D Carotid Simultaneous Non-Contrast Angiography and intraPlaque Hemorrhage MRI With Model Based Deep Learning Architecture (deepSNAP).

PURPOSE: To improve the imaging efficiency of 3D carotid simultaneous noncontrast angiography and intraplaque hemorrhage (SNAP) MRI by reconstruction ...

The ABCD of AI-Enabled Clinical Reasoning: A Guided Framework to Transform Learner Interactions and Decision Quality.

OBJECTIVE: To evaluate the efficacy of a structured guidance framework articulate, brainstorm and benchmark, critique and customize, and decide and di...