Latest AI and machine learning research in surgery for healthcare professionals.
Accurate detection of tumor boundaries is critical for the success of oncologic surgical intervention. Traditionally, palpation can handover important information for tumor localization based on the tissue mechanical properties, but in Minimally Invasive Surgery no direct access to the tumor for palpation is feasible. For providing a technical analogy, this feasibility-level study focusses on the ...
BACKGROUND AND OBJECTIVE: Accurate intraoperative differentiation between focal nodular hyperplasia (FNH) and hepatocellular carcinoma (HCC) remains a major clinical challenge, especially in atypical cases where conventional imaging and histopathology are constrained by turnaround time, cost, or spectral resolution. This study aims to develop a novel deep learning framework to improve the precisio...
BACKGROUND: Understanding the patient-specific anatomy of the superior mesenteric artery (SMA) and its branches is of critical importance when perform...
AIMS: Early identification of pharmacological therapy for gestational diabetes mellitus (GDM), a common pregnancy complication, through machine learni...
PURPOSE: Pediatric adrenocortical tumors (pACTs) are rare and clinically heterogeneous. Existing risk stratification systems rely on fixed thresholds ...
The aim of this study was to develop a machine learning classification model that can forecast salvage surgery complications. This was a retrospective...
INTRODUCTION: larynGuide™ is a novel assistive software integrated with the C-MAC® videolaryngoscope, which provides guidance during laryngoscopy and ...
PURPOSE: We aim to apply the deep learning (DL) technique to predict the gold-standard invasive coronary angiography (ICA) for coronary artery disease...
OBJECTIVE: Automated segmentation models for volumetric measurement of vestibular schwannoma (VS) have been developed for sporadic VS but not for bila...
PURPOSE: To develop and validate OCT-PRO, a multimodal machine learning model integrating OCT images and clinical traits to predict postoperative visu...
OBJECTIVE: This study aims to develop and validate a deep learning radiomics (DLR) model based on ultrasound images for non-invasively distinguishing ...
BACKGROUND: The TAILORED-AF randomized trial demonstrated that artificial intelligence-guided ablation of spatiotemporal dispersion in addition to pul...
BACKGROUND: This study evaluates the performance of ChatGPT Plus and Perplexity Pro in matching Current Procedural Terminology (CPT) codes from vascul...
Bone metastasis, a frequent complication of advanced cancers, requires early, precise detection to enable timely interventions and improve patient out...
PURPOSE: To evaluate whether the difference between artificial intelligence (AI)-estimated retinal biological age and chronological age-the retinal ag...
Foundation models have revolutionized computer vision by achieving vastly superior performance across diverse tasks through large-scale pretraining on...
AIM: To investigate the artificial Intelligence (AI) landmark for guiding rotator cuff interval injections for adhesive capsulitis (AC). MATERIAL AND ...
OBJECTIVE: We aimed to develop and internally validate a radiomics classification model based on multiphase computed tomography (CT) scans for preoper...
In the rapidly advancing landscape of surgical education, the traditional apprenticeship model is being increasingly complemented by individualized le...
BACKGROUND: Documentation burden is a major contributor to surgeon burnout, particularly in high-volume outpatient specialties such as hand surgery. T...