Surgery

Hybrid Harris hawks-optimized random forest model for detecting multi-element geochemical anomalies related to mineralization.

Reliable recognition of geochemical anomalies linked to ore deposits is one of the most significant challenges in mineral exploration. Several advanced machine learning (AML) algorithms have recently been applied to recognize multi-element geochemical anomalies. Performance of the AML algorithms are extremely dependent to values of their hyperparameters. Because, conclusions of their application c...

Comparative study of five-year cervical cancer cause-specific survival prediction models based on SEER data.

Cervical cancer (CC) is a major cause of mortality in women, with stagnant survival rates, highlighting the need for improved prognostic models. This study aims to develop and compare machine learning models for predicting five-year cause-specific survival (CSS) in CC patients and evaluate their performance against traditional methods like the Cox Proportional Hazards model. Using data from the Su...

Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery.

Hip fractures among the elderly population continue to present significant risks and high mortality rates despite advancements in surgical procedures....

Natural orifice transluminal endoscopic surgery: history and current development.

Natural orifice transluminal endoscopic surgery (NOTES) represents an innovative advancement in minimally invasive surgery, utilizing natural body ori...

A novel artificial intelligence-based system for quality monitoring during esophagogastroduodenoscopy: a multicenter randomized controlled study.

Esophagogastroduodenoscopy (EGD) is the pivotal procedure for diagnosis of upper gastrointestinal (UGI) lesions. However, significant variation in EGD...

Personalized azithromycin treatment rules for children with watery diarrhea using machine learning.

We use machine learning to identify innovative strategies to target azithromycin to the children with watery diarrhea who are most likely to benefit. ...

Decades of Scientific Data and Global Media Reporting on Complications in Non-surgical Aesthetic Treatments for a Transparent Safety Profile: Kissing Snow White Awake.

INTRODUCTION: Non-surgical aesthetic treatments (NSATs) have gained significant traction over the past two decades, prized for their minimally invasiv...

GenAI exceeds clinical experts in predicting acute kidney injury following paediatric cardiopulmonary bypass.

The emergence of large language models (LLMs) opens new horizons to leverage, often unused, information in clinical text. Our study aims to capitalise...

Multi-objective artificial-intelligence-based parameter tuning of antennas using variable-fidelity machine learning.

Multi-objective optimization (MO) is an important topic in contemporary antenna design. Due to the reliance on computationally-expensive electromagnet...

Developed a knowledge base of risk factors for postoperative cognitive dysfunction: a retrospective database study.

Postoperative cognitive dysfunction (POCD), a heterogeneous spectrum of surgery/anesthesia-associated neurocognitive impairments, represents a critica...

Semi-automated screening of azobezenes for solar energy storage using extended tight binding methods.

In the face of the pressing climate change crisis, Molecular Solar Thermal Energy Storage (MOST) Systems offer a promising avenue for efficient energy...

Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble.

A machine learning model was developed and validated to predict postoperative complications in patients with acute type A aortic dissection (ATAAD) wh...

Auto-Segmentation via deep-learning approaches for the assessment of flap volume after reconstructive surgery or radiotherapy in head and neck cancer.

Reconstructive flap surgery aims to restore the substance and function losses associated with tumor resection. Automatic flap segmentation could allow...

Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures.

Postoperative pneumonia, a prevalent complication arising from lower limb fracture surgery, can significantly prolong hospitalization periods and elev...

Quantitative ultrasound classification of healthy and chemically degraded ex-vivo cartilage.

In this study, we explore the potential of ten quantitative (radiofrequency-based) ultrasound parameters to assess the progressive loss of collagen an...

Large language models versus traditional textbooks: optimizing learning for plastic surgery case preparation.

BACKGROUND: Large language models (LLMs), such as ChatGPT-4 and Gemini, represent a new frontier in surgical education by offering dynamic, interactiv...

Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis.

BACKGROUND: Predicting pituitary adenoma (PA) recurrence after surgical resection is critical for guiding clinical decision-making, and machine learni...

Intraoperative Assessment of Parathyroidectomy Outcomes via Autoencoder-Support-Vector-Machine-Assisted Label-Free Differential SERS Spectroscopy.

Intraoperative guidance plays a pivotal role in enhancing surgical success rates and optimizing patients' prognosis. However, during surgery, the lack...

MRI's evolving role in rectal cancer in the era of personalized medicine.

Recent decades have seen great advances in the diagnosis and management of rectal cancer, and magnetic resonance imaging (MRI) has become pivotal for ...

Establishment of a machine learning-based predictive model with dual-center external validation: investigating the role of robotic surgery in preventing delayed gastric emptying for right-sided colon cancer.

After colorectal surgery, delayed gastric emptying (DGE) is a clinically significant postoperative complication that significantly lowers patients' qu...