Surgery

Guidance on artificial intelligence use for rapid evidence mapping of early childhood intervention outcome measures.

AIM: To synthesize research evidence on the psychometric properties of outcome measures alongside practical implementation considerations relevant to children, families, practitioners, and services, and produce evidence-based practice resources to support clinical decision-making in outcome measurement for the early childhood intervention (ECI) context. METHOD: We conducted a mapping review that i...

Psychosocial Stress in the Chinese Community: Speech Analytics Through Linguistic and Acoustic Fusion Using Machine Learning.

BACKGROUND: Family caregivers experience significant stress due to intensive caregiving activities, making them highly susceptible to adverse psychosocial health conditions. Early detection of this stress is crucial for timely interventions to prevent disease progression and long-term disability. OBJECTIVE: This study aimed to develop and validate the Linguistic and Acoustic Speech Analytics Progr...

Assessment system for short-term lower cranial nerve dysfunction following medulla oblongata glioma surgery: risk stratification and optimal surgical strategy.

OBJECTIVE: Medullary gliomas pose significant surgical risks, particularly the risk of postoperative lower cranial nerve (LCN) dysfunction, which prof...

Interlayer-aware postoperative facial appearance prediction in orthognathic surgery with bio-geometric guidance.

OBJECTIVE: Accurate prediction of postoperative facial appearance is essential for orthognathic surgical planning, yet remains challenging due to the ...

AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy.

BACKGROUND AND OBJECTIVE: Neoadjuvant immune-checkpoint inhibitors (ICIs) in muscle-invasive bladder cancer (MIBC) were tested in patient's ineligible...

VeNet: a lightweight neural network for efficient brain vessel segmentation in endovascular robotic surgery.

Telesurgery integrates artificial intelligence (AI), robotic systems, sensing technologies, and wireless communication to enable remote and computer-a...

The impact of an artificial intelligence (AI)-assisted education program on mental health, social support and quality of life in older individuals with head and neck cancer: study protocol of a randomized controlled trial.

BACKGROUND: Head and neck cancer (HNC) is a common malignant tumor, and its treatment often leads to functional impairments in speech, swallowing, and...

Machine learning models based on magnetic resonance imaging for predicting Lymphovascular Invasion in Invasive Breast Cancer.

OBJECTIVES: Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigat...

Intelligent documentation in medical education: can AI replace manual case logging?

OBJECTIVE: This study investigates the feasibility of using large language models (LLMs) to automate procedural case log documentation in radiology tr...

Generalisable artificial intelligence ECG trained on public data for outcome prediction after transcatheter aortic valve replacement.

BACKGROUND: Artificial intelligence ECG (AI-ECG) models can predict cardiovascular outcomes, but their clinical adoption is limited by restricted acce...

Retinal lesion annotation on fundus imaging: an interobserver variability study.

Accurate detection and segmentation of retinal lesions in fundus photographs is essential for diagnosing diabetic retinopathy (DR) and for developing ...

Please follow the rules: surgical workflow recognition constrained by linear temporal logic.

PURPOSE: Surgical workflow recognition aims at automatically recognizing the actions performed during a surgery. Deep learning methods showed their ca...

Machine learning for postoperative complication prediction and early recurrence risk assessment across cancer types: a systematic review and meta-analysis.

BACKGROUND: Although machine learning is often used in medical diagnosis, its effectiveness in cancer diagnosis remains uncertain. OBJECTIVE: To explo...

Colorectal cancer sidedness: prognostic implications and the predictive role of artificial intelligence.

BACKGROUND: Colorectal cancer (CRC) is a biologically heterogeneous disease in which tumor sidedness has emerged as a relevant prognostic factor. Conv...

Preoperative low-attenuation area on computed tomography is associated with recurrence after curative resection for non-small-cell lung cancer.

BACKGROUND: Recurrence after curative-intent resection remains a major determinant of long-term outcomes in non-small-cell lung cancer (NSCLC). Preope...

From Three to Eight Clusters: A Machine Learning-Based Classification of Operated Adult Spinal Deformity Patients.

BACKGROUND: Adult spinal deformity (ASD) is a heterogeneous condition encompassing diverse etiologies, clinical presentations, and surgical challenges...

Deriving Clavien-Dindo Classification from Administrative Data: Development and External Validation in Hepatobiliary Surgery.

INTRODUCTION: Routine electronic health records (HER)/administrative data could enable time- and cost-efficient, real-time surveillance and health-eco...

Automated recognition of surgical phases in robot-assisted radical prostatectomy using a Hiera-based vision transformer.

OBJECTIVES: To construct a reliable robot-assisted radical prostatectomy (RARP) surgical phase-recognition model and sought to deploy the model within...

Tree-based and sparse logistic models for predicting one-month postoperative performance status after surgery for spinal metastases.

PURPOSE: We aimed to develop and internally validate prediction models for one-month postoperative performance status (PS) after surgery for spinal me...

Interpretable machine learning model for predicting hemorrhage following ultrasound-guided percutaneous renal biopsy: a multicenter study.

OBJECTIVE: This study aims to develop and validate an interpretable machine learning (ML) model for predicting post-procedural hemorrhage (PH) after u...