Latest AI and machine learning research in surgery for healthcare professionals.
Colorectal liver metastases (CRLM) represent a major clinical challenge because outcomes after hepatic resection vary widely between patients. Preoperative risk stratification remains limited, and radiomics may provide non invasive imaging biomarkers to support prognosis. Objective: This study aimed to develop a CT based radiomics signature capable of generating risk scores for predicting...
Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei (Bp). Rapid diagnosis and appropriate antimicrobial treatment are critical to reduce mortality, yet diagnosis is hindered by diverse clinical manifestations, mimicry with other diseases, and reliance on slow culture-based methods. Detecting volatile compounds offers a non-invasive approach for rapid infection ...
BACKGROUND: Accurate segmentation of cartilage from magnetic resonance imaging (MRI) is crucial for the diagnosis and surgical planning of knee osteoa...
BACKGROUND: Dysphagia is recognized as one of the most common severe complications following cardiac surgery, with the potential to result in adverse ...
PURPOSE: To develop and validate machine learning (ML) models for postoperative risk stratification in oral cavity squamous cell carcinoma (OCSCC) and...
BACKGROUND: Intracerebral hemorrhage (ICH) is a leading cause of long-term disability, particularly in China. Post-stroke motor recovery exhibits cons...
IMPORTANCE: Hospitals are increasingly experiencing challenges with variable and unpredictable inpatient loads, including days with excessively high a...
Accurate prediction of tracheostomy after craniotomy for supratentorial intracerebral hemorrhage (sICH) remains challenging. This study aimed to devel...
Purpose To develop a self-supervised text-vision framework to detect abnormalities on brain MRI scans by leveraging free-text neuroradiology reports, ...
BACKGROUND: A comprehensive preoperative assessment of the patient's physical condition is crucial for predicting the prognosis of patients undergoing...
Pregnant women and children have been underrepresented in clinical studies due to ethical concerns and perceived vulnerabilities. This resulted in a s...
AIM: To evaluate the value of machine learning in assessing intraoperative blood loss by comparing associated outcomes with those of the gold standard...
BACKGROUND: Epilepsy surgery is an important intervention for treatment-resistant epilepsy, butthe ability to predict long-term seizure freedom post-s...
OBJECTIVES: To evaluate the caries detection performance of a commercially available AI-system (Nostic) and human raters for radiographic caries detec...
Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a major cause of severe thrombocytopenia, intracranial hemorrhage, and long-term neurologica...
OBJECTIVE: Artificial intelligence (AI) is poised to transform surgical education, particularly in providing objective, scalable feedback. This study ...
Covariate adjustment is an approach to improve the precision of trial analyses by adjusting for baseline variables that are prognostic of the primary ...
Current prostate cancer detection methods remain limited in non-invasiveness and specificity, prompting interest in urinary biomarkers such as sarcosi...
Aspiration is prevalent in the elderly population, and can lead to life-threatening conditions such as suffocation and aspiration pneumonia. However, ...
BACKGROUND AND PURPOSE: Artificial intelligence (AI) models have shown promise in neuroradiology, yet their real-world generalizability remains uncert...