Surgery

Risk Classification of Low-Resolution Whole-Slide Thumbnail Images by Multi-dimensional Feature Reconstruction with Multi-task Deep Learning Network Helps Prioritize Pathology Case Registration.

Contemporary surgical pathology workflows often prioritize slide examination based on case registry order rather than patient risk level. As a result, high-risk cases, especially those involving malignant lesions, may be unintentionally delayed, potentially affecting patient outcomes. In this study, we present an artificial intelligence (AI)-based framework designed to efficiently screen and prior...

Machine learning-driven national analysis for predicting adverse outcomes in intramedullary spinal cord tumor surgery.

UNLABELLED: Spinal tumors represent 15% of all central nervous system malignancies, with intramedullary spinal cord tumors (IMSCTs) being rare. Predominantly ependymomas and astrocytomas, IMSCTs often present late, leading to significant morbidity and mortality. Surgical excision is key but challenging due to the tumors' complex, invasive nature. Treatment involves a multidisciplinary approach, co...

[Current situation and development trends of minimally invasive surgical procedures in the treatment of pancreatic tumors].

With the rapid development of modern medical technology,minimally invasive surgical procedures are playing an increasingly important role in the field...

[The application of artificial intelligence in enhancing the safety of pancreatic surgeries].

Pancreatic surgeries have long been considered as challenging procedures due to its complex surgical characteristics. Establishing a surgical safety s...

A Comparative Analysis of the Accuracy and Readability of Popular Artificial Intelligence-Chat Bots for Inguinal Hernia Management.

BackgroundArtificial intelligence (AI), particularly large language models (LLMs), has gained attention for its clinical applications. While LLMs have...

Towards a dynamic model to estimate evolving risk of major bleeding after percutaneous coronary intervention.

While static risk models may identify key driving risk factors, the dynamic nature of risk requires up-to-date risk information to guide treatment dec...

Computer-Based Tracking of Microsurgical Instruments: A Novel Assessment Tool for Robot-Assisted and Conventional Microsurgery.

BACKGROUND: Efficient and objective tools for self-assessment of microsurgical skills are needed to ensure high-quality microsurgical training and opt...

A Multicentre Comparative Analysis of Radiomics, Deep-learning, and Fusion Models for Predicting Postpartum Hemorrhage.

RATIONALE AND OBJECTIVE: This study compared the capabilities of two-dimensional (2D) and three-dimensional (3D) deep learning (DL), radiomics, and fu...

From Faster Frames to Flawless Focus: Deep Learning HASTE in Postoperative Single Sequence MRI.

BACKGROUND: This study evaluates the feasibility of a novel deep learning-accelerated half-fourier single-shot turbo spin-echo sequence (HASTE-DL) com...

Advances and Integrations of Computer-Assisted Planning, Artificial Intelligence, and Predictive Modeling Tools for Laser Interstitial Thermal Therapy in Neurosurgical Oncology.

Laser interstitial thermal therapy (LiTT) has emerged as a minimally invasive, MRI-guided treatment of brain tumors that are otherwise considered inop...

Machine learning model for the prediction of postoperative acute kidney injury (AKI) in neonates undergoing digestive surgery.

OBJECTIVE: The aim of our study is to determine the main predictors of postoperative AKI in neonates using machine learning models compared with the l...

Comprehensive predictive modeling in subarachnoid hemorrhage: integrating radiomics and clinical variables.

Subarachnoid hemorrhage (SAH) is a severe condition with high morbidity and long-term neurological consequences. Radiomics, by extracting quantitative...

NeuroLens: organ localization using natural language commands for anatomical recognition in surgical training.

PURPOSE: This study introduces NeuroLens, a multimodal system designed to enhance anatomical recognition by integrating video with textual and voice i...

Machine Learning-Based Biomarker Discovery from Serum Trace Elements and Biochemical Parameters in Patients with Nasal Polyps.

Nasal polyps (NP) are benign mucosal outgrowths associated with chronic inflammation that can significantly reduce quality of life. This study aimed t...

An artificial intelligence enhanced coaching mode.

Surgical coaching has emerged as an innovative educational strategy designed to enhance both the technical and non-technical competencies of surgeons ...

Invited Response to "Artificial Intelligence in Rhinoplasty: Precision or Over-Reliance?".

The integration of artificial intelligence (AI) into surgical practice demands critical reflection-not only on its capabilities, but also on its appro...

Enhancing Surgical Precision: A Systematic Review of Wearable Medical Devices for Assisted Surgery.

The integration of wearable medical devices into surgical practice has transformed the field, enabling enhanced precision, informed decision-making, a...

Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis.

Accurate prediction of postoperative mortality risk after cardiac surgery is essential to improve patient outcomes. Traditional models, such as EuroSC...

OncoE25: an AI model for predicting postoperative prognosis in early-onset stage I-III colon and rectal cancer-a population-based study using SEER with dual-center cohort validation.

BACKGROUND: Although CRC incidence is declining overall, early-onset colorectal cancers are increasing. No prognostic models currently exist for predi...

Surveillance and Surgical Salvage Treatment for Endoscopically Removed T1 Colorectal Cancers.

Endoscopic submucosal dissection (ESD) enables en-bloc resection of large lesions more than 20 mm in size. Therefore, the use of ESD has gained broade...