Surgery

Improving Postoperative Outcomes Using Preoperative Patient Education.

Preoperative education has been shown to improve postoperative outcomes, yet accessible and individualized information remains limited. This study presents an AI-based Chatbot designed to support patients through interactive and understandable preoperative guidance. Initial testing shows that the AI-Chatbot adapts to patient questions and promotes interaction, although occasional hallucinations oc...

Machine learning prediction of surgical approach and clinical heterogeneity in cervical spondylotic myelopathy.

BACKGROUND: Preoperative planning of the surgical approach for cervical spondylotic myelopathy (CSM) is central to precision medicine. This study used supervised machine learning (SML) to build a predictive model for surgical approach selection in CSM and applied unsupervised machine learning (UML) to explore clinical heterogeneity. METHODS: In this retrospective study, a development cohort of 884...

Noninvasive detection and prognostic stratification of biliary tract cancer using cell-free DNA fragmentomics: a model development and validation study.

Biliary tract cancer (BTC) is typically diagnosed at an advanced stage due to the lack of effective screening tools, resulting in limited therapeutic ...

Qualitative perspectives of early surgeon users on the value of the daVinci 5 surgical system.

To understand the value and experience of using the da Vinci 5 (dV-5) robotic surgical system among early-adopting surgeons in the United States. In M...

[Artificial intelligence in urological surgery].

BACKGROUND: The integration of artificial intelligence (AI) into urological robotic surgery is currently in a dynamic phase of development and validat...

Defining the artificial intelligence knowledge gap in surgery: experience and perspectives from surgical resident and postgraduate.

Despite the growing prominence of Artificial Intelligence (AI) in surgical practice, surgical residents and postgraduates receive limited formal train...

AI-Enhanced Nanophotonic Heterochain Sensor Enables Multiplexed Biomarker Detection across Serum, Urine, and Saliva for Stroke Differentiation.

Background: Rapid and accurate identification of stroke subtype is critical for timely intervention, yet current diagnostic assays are limited by long...

Evaluating Postpartum Hemorrhage Transfusion Risk with a Machine Learning Model for Informed Consent: Retrospective Cohort Study.

BACKGROUND: Postpartum hemorrhage requiring a blood transfusion is a concern for patients and clinicians. Postpartum hemorrhage risk and mode of deliv...

Optimizing adjuvant therapy selection with machine learning after transoral surgery in HPV-related oropharyngeal cancer.

BACKGROUND: De-escalation after transoral surgery (TOS) for HPV-related oropharyngeal squamous cell carcinoma (OPSCC) requires accurate risk stratific...

Evaluating the feasibility of a scalable, digitally supported model for global collaborative surgical research: protocol for a prospective, international, multicentre observational study in cryptoglandular anal fistula treatment (CRAFT).

INTRODUCTION: Management of cryptoglandular anal fistula is characterised by wide variation in diagnostic strategies, surgical techniques and outcome ...

Artificial intelligence to revolutionize surgical decision-making is still just around the corner.

Medical artificial intelligence, especially large language models, has engendered both excitement and unease across the medical community, promising i...

Preoperative identification of tumor deposits in rectal cancer using a transformer-based multimodal fusion model: a multicenter retrospective study.

OBJECTIVE: To develop and validate a transformer-based deep learning-radiomics model for the non-invasive preoperative discrimination of tumor deposit...

An evidence-based nursing management app prototype for adult urology day surgery: an AI-driven development pathway.

OBJECTIVES: The nursing management of adult urology patients in day surgical settings has undergone rapid development. This study aimed to (1) retriev...

Development and validation of an MRI-based deep learning system for triple-class ER expression classification in breast cancer: a large-scale multicenter study.

BACKGROUND: Estrogen receptor (ER) expression is a key prognostic and predictive marker in breast cancer. The 2020 ASCO/CAP guidelines classify tumors...

Deep Learning Analysis of Indocyanine Green Fluoroscopy of Ureters in Robotic Cystectomy: Toward Reducing Ureteroenteric Strictures.

OBJECTIVES: To develop a deep learning method to quantify ureter perfusion during indocyanine green (ICG) fluoroscopy in robot-assisted radical cystec...

MRI radiomics-based approach to predict pituitary neuroendocrine tumor invasiveness.

OBJECTIVES: To assess the diagnostic potential of magnetic resonance imaging (MRI) radiomics and machine learning models using T2-weighted and contras...

Development of an integrated multidimensional nomogram for predicting stone-free status after retrograde intrarenal surgery: a machine learning-based approach.

BACKGROUND: This study aimed to develop and validate a novel preoperative nomogram to predict stone-free status (SFS) in patients undergoing retrograd...

Implementing Artificial Intelligence for Intracranial Hemorrhage Detection.

Deep Learning Algorithm Based on Contrast-Enhanced Ultrasound Potentially Optimizes Treatment Strategies for Solitary Primary Hepatocellular Carcinoma.

OBJECTIVE: We aimed to propose a prognostic framework using a dual-branch Vision Transformer (ViT) deep learning (DL) architecture for stratifying rec...

Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.

Timely activation of massive hemorrhage protocols (MHP) is critical to prevent exsanguination and improve survival in trauma patients. Current clinica...