Gastroenterology

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 logistic regression model.

Preoperative Assessment of Lymph Node Metastasis in Rectal Cancer Using Deep Learning: Investigating the Utility of Various MRI Sequences.

PURPOSE: This study aimed to develop a deep learning (DL) model based on three-dimensional multi-parametric magnetic resonance imaging (mpMRI) for preoperative assessment of lymph node metastasis (LNM) in rectal cancer (RC) and to investigate the contribution of different MRI sequences.

AI-based large-scale screening of gastric cancer from noncontrast CT imaging.

Early detection through screening is critical for reducing gastric cancer (GC) mortality. However, in most high-prevalence regions, large-scale screen...

Quantitative and automatic plan-of-the-day assessment to facilitate adaptive radiotherapy in cervical cancer.

To facilitate implementation of plan-of-the-day (POTD) selection for treating locally advanced cervical cancer (LACC), we developed a POTD assessment ...

Clinical benefits of deep learning-assisted ultrasound in predicting lymph node metastasis in pancreatic cancer patients.

AIM: This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) derived from ultrasound images to improve predictive accuracy ...

Prospective Registry Study on Thermal Liver Ablation of Primary and Secondary Liver Tumours Named the A-IMAGIO Study.

PURPOSE: The long-term objective of the Ablation-IMaging and Advanced Guidance for workflow optimization in Interventional Oncology (A-IMAGIO) project...

Association rule mining of the human gut microbiome.

The human gut carries a vast and diverse microbial community that is essential for human health. Understanding the structure of this complex community...

Harnessing the machine learning and nomogram models: elevating prognostication in nonmetastatic gastric cancer with "double invasion" for personalized patient care.

OBJECTIVE: To develop and validate a machine learning framework combined with a nomogram for predicting recurrence after radical gastrectomy in patien...

Machine Learning Models Based on CT Enterography for Differentiating Between Ulcerative Colitis and Colonic Crohn's Disease Using Intestinal Wall, Mesenteric Fat, and Visceral Fat Features.

PURPOSE: This study aimed to develop radiomic-based machine learning models using computed tomography enterography (CTE) features derived from the int...

Evolution of inflammatory bowel disease in Korea: a 60-year perspective on clinical and research development.

Inflammatory bowel disease (IBD) was once considered rare in Korea, with the first reported case documented in 1961. Since then, its incidence and pre...

The impact of artificial intelligence on the endoscopic assessment of inflammatory bowel disease-related neoplasia.

Inflammatory bowel disease (IBD) is a group of chronic inflammatory conditions of the gastrointestinal tract resulting from an inappropriate immune re...

Prediction of biological evolution following blood product transfusion during liver transplantation: the contribution of machine learning to decision-making.

OBJECTIVES: Liver transplantation is a complex procedure frequently requiring transfusion of blood products to manage coagulopathy and haemorrhage. Th...

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...

Real-time AI-based detection of excessive traction on the recurrent laryngeal nerve during robotic-assisted minimally invasive esophagectomy: a proof-of-concept study.

Recurrent laryngeal nerve (RLN) palsy often occurs due to excessive traction (ET) on the nerve during esophagectomy. Use of a nerve integrity monitor ...

Application of artificial intelligence lesion labeling system-assisted endoscopic submucosal dissection for the treatment of esophageal lesions in a low-volume center: a prospective cohort study.

BACKGROUND AND AIMS: Multiple artificial intelligence (AI) systems have been developed to assist with endoscopic diagnosis. We established the first r...

Disease entity impacts muscle wasting in the ICU with COVID-19 patients losing muscle nearly twice as fast.

Muscle loss in critically ill patients, particularly during prolonged ICU stays, poses significant challenges to recovery and long-term outcomes. ICU-...

Artificial intelligence-based marks detection and incision guide line prediction model in esophageal endoscopic submucosal dissection: a multicenter study (with video).

BACKGROUND: Endoscopic submucosal dissection (ESD) was an important minimally invasive procedure for treating early esophageal cancer, where the surro...

Algorithm-based intraoperative diagnosis of liver tumors using infrared spectroscopy.

Liver cancer, including hepatocellular carcinoma (HCC), cholangiocellular carcinoma (CCC), and metastases, presents diagnostic challenges during surge...

Artificial Intelligence-assisted Endoscopy and Examiner Confidence: A Study on Human-Artificial Intelligence Interaction in Barrett's Esophagus (With Video).

OBJECTIVE: Despite high stand-alone performance, studies demonstrate that artificial intelligence (AI)-supported endoscopic diagnostics often fall sho...