Latest AI and machine learning research in surgery for healthcare professionals.
BACKGROUND AND PURPOSE: The delineation of contrast enhancement in pediatric brain tumors is crucial for effective surgical and treatment planning, as well as for monitoring treatment response per Response Assessment in Pediatric Neuro-Oncology (RAPNO) guidelines. Accurate delineation of enhancement is also important in ground truth generation for training automated deep learning models. However, ...
Accurate, noninvasive prediction of invasiveness in ground-glass nodules (GGNs) is important for surgical planning in lung adenocarcinoma. This multicenter study developed and validated an interpretable machine learning (ML) model based on quantitative and qualitative computed tomography (CT) features. We retrospectively enrolled 860 patients with 1009 GGNs from three centers. Center 1 cases were ...
BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening condition with high morbidity and mortality, particularly in poor-grade pa...
BACKGROUND: Operator-dependent laboratory tasks-embryo selection, vitrification and warming, and intracytoplasmic sperm injection (ICSI)-have been the...
BACKGROUND: This is an updated component network meta-analysis to evaluate efficacy and safety of various therapeutic approaches and their combination...
BACKGROUND: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Current diagnostic strategies rely primarily on imagi...
The increasing adoption of machine learning and artificial intelligence in surgical risk prediction has introduced new challenges related to the fairn...
Humanoid robots can be seamlessly integrated into human-robot interaction scenarios due to their human-like appearances. Pneumatic artificial muscles ...
BACKGROUND: Circulating tumor DNA (ctDNA) detection offers minimally invasive monitoring of cancer from blood samples. While tumor-informed approaches...
BACKGROUND: Minimally invasive pyeloplasty (MIP), encompassing both conventional laparoscopy and robot-assisted approaches, has become the primary tre...
Real-time monitoring of infection-associated volatile organic compounds (VOCs) offers a non-invasive pathway for early respiratory infection detection...
Robotic liver surgery (RLS) is expanding in recent years. Complication prediction is crucial for postoperative outcomes. Traditional MIS scores are po...
PURPOSE: Paediatric dental anxiety remains a significant challenge in clinical practice, often impacting co-operation and treatment outcomes. This ran...
OBJECTIVE: To investigate the potential of magnetic resonance imaging (MRI) radiomics-based machine learning (ML) models in predicting local lymph nod...
BACKGROUND AND OBJECTIVE: Vocal cord leukoplakia (VCL) is a common precancerous lesion of the larynx associated with a high postoperative recurrence r...
Neurological prognostication after out-of-hospital cardiac arrest (OHCA) remains challenging. Existing clinical scores rely on static, single-timepoin...
BACKGROUND: Intracerebral hemorrhage (ICH) remains associated with high mortality and treatment variability. Current workflows rely on fragmented imag...
INTRODUCTION: OSAS is a common yet underdiagnosed condition, particularly among patients with head and neck cancers (HNC). Anatomical changes caused b...
PURPOSE OF REVIEW: This review examines the current role of deep learning in the surgical management of gynecologic cancers. It aims to evaluate appli...
BACKGROUND: Thyroid-associated ophthalmopathy (TAO) is an autoimmune orbital disease that may cause ocular motility abnormalities, altered periocular ...