Surgery

Inflammatory marker-driven deep learning model for postoperative gastric cancer prognosis.

BACKGROUND: Prognostic prediction following gastric cancer surgery plays a pivotal role in postoperative management, helping to optimize therapeutic strategies and improve patient survival. Standard clinicopathological indicators, including tumor differentiation and lymph node metastasis, continue to serve as the basis for outcome evaluation; however, they do not adequately represent the host's sy...

Perioperative prediction of adnexal malignancy by an interpretable machine learning model for guiding ovarian preservation in premenopausal endometrial cancer.

BACKGROUND: Ovarian preservation in premenopausal patients with endometrial cancer remains challenging due to the potential presence of concurrent adnexal malignancy. To support surgical decision-making, we developed an interpretable machine learning model for the perioperative identification of high-risk patients, thereby facilitating personalized ovarian preservation strategies. METHODS: We cond...

Enhancing decision-making in surgery for a large temporocorneal meningioma through an explainable human-AI collaboration: a case report.

BACKGROUND: Meningiomas, particularly large temporocorneal meningiomas, pose significant surgical challenges due to their proximity to critical brain ...

Explainable Machine Learning-Based Prediction of Postoperative Hypoxemia in Elderly Patients Undergoing General Anesthesia.

In order to predict the risk of postoperative hypoxemia in elderly patients undergoing general anesthesia, this study developed, validated, and interp...

Linguistic Fidelity and Classification Performance of Large Language Models for Generating Synthetic Operative Notes: Evaluation Study.

BACKGROUND: Machine learning models for surgical applications require large, diverse datasets; however, data scarcity remains a critical limitation du...

Development of a predictive model for postoperative body mass index and diabetes outcomes after metabolic bariatric surgery: retrospective cohort study.

BACKGROUND: Predicting postoperative body mass index (BMI) trajectories and long-term type 2 diabetes (T2D) remission after bariatric surgery remains ...

A multimodal deepsurv approach: integrating radiomics and clinical factors for brain metastasis survival prediction.

PURPOSE: Predicting survival outcomes for brain metastasis (BM) patients is crucial for tailoring treatment strategies and improving patient managemen...

Prediction of Acromial and Scapular Spine Fractures After Reverse Total Shoulder Arthroplasty using Machine Learning: A Retrospective Cohort Study.

BACKGROUND: Acromial and scapular spine fractures (ASSF) are an uncommon but significant complication following reverse total shoulder arthroplasty (r...

Machine Learning-Based Drug Susceptibility Prediction from Candida Genomic Data.

Invasive Candida infection is an increasing clinical concern, with antifungal resistance rising across multiple species. However, rapid and accurate a...

Real-World Implementation of Large Language Models for Writing Clinical Discharge Summaries Within a Secure Data Environment: Development and Expert Evaluation Study.

BACKGROUND: A discharge summary should be a clinical report that documents a patient's hospital stay, including test results, diagnoses, management, a...

Identifying patients with poor visual outcomes after primary rhegmatogenous retinal detachment surgery using machine learning.

AIMS: To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous retinal detachment (RRD). ...

Serum integrative omics reveals predictive signatures for coronary artery disease.

Identifying serum biomarkers that accurately reflect the progression of coronary artery disease (CAD) remains a major challenge. Integrative proteomic...

Development and validation of machine learning models for obstructive sleep apnea risk stratification in high-risk pregnant women.

BACKGROUND: Obstructive sleep apnea (OSA) affects approximately 15% of pregnancies and is associated with adverse maternal and fetal outcomes. Althoug...

The role of large language models in the writing of surgical reviews: fact or fantasy?

BACKGROUND: Amidst the current enthusiasm concerning artificial intelligence and its possible application in the composition of different kinds of sci...

Developing and validating an interpretable machine learning model for predicting postoperative nausea and vomiting in pulmonary resection patients: a retrospective observational study.

BACKGROUND: Postoperative nausea and vomiting (PONV) remains a prevalent complication hindering Enhanced Recovery After Surgery (ERAS) protocols, part...

Technical and clinical feasibility of single-use gastroscopy with real-time AI-based quality monitoring and single-use colonoscopy: a prospective two-center study.

BACKGROUND: Single-use gastrointestinal endoscopes eliminate the need for post-procedure reprocessing and have become an area of interest in endoscopi...

Effects of actuation angle of spatially distributed control surfaces of a bio-robotic sea lion on turning performance.

To expand the performance envelope of current unmanned underwater vehicles operating in near shore environments, researchers have increasingly turned ...

Performance of the Pre-Eclampsia Integrated Estimate of Risk-Machine Learning (PIERS-ML) model in a Kenyan cohort of women with pre-eclampsia- a retrospective test derivation validation study.

OBJECTIVE: Pre-eclampsia is a cause of significant maternal morbidity and mortality, with delivery initiating resolution. The Pre-eclampsia Integrated...

Vibe Coding in Neurosurgery: Bridging the Gap Between Clinical Needs and Digital Innovation-A Narrative Review and Practical Perspective.

BackgroundArtificial intelligence (AI) and machine learning are transforming neurosurgical research and practice, yet the programming barrier has excl...

Extracting Medical Information From Unstructured Clinical Text Using Large Language Models to Enhance Health Care Interoperability: Proof-of-Concept Study.

BACKGROUND: Unstructured clinical text remains a major barrier to interoperable data reuse and large-scale secondary analysis in health care. Large la...