Peptic Ulcer Disease

Machine learning-driven automated evaluation of surgical skills during laparoscopic distal gastrectomy based on blood pixel analysis.

BACKGROUND: In laparoscopic distal gastrectomy (LDG), intraoperative bleeding directly reflects surgical proficiency, including tissue handling and hemostasis. Conventional assessments of surgical skills, such as global operative assessment of laparoscopic skills, are subjective and time-consuming. Bleeding events can serve as an objective indicator for automated skill evaluation in this procedure...

Fusion prediction model for post-ERCP pancreatitis under NSAIDs prophylaxis.

BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) is a common procedure for treating biliary and pancreatic disorders; however, it is associated with significant complications, notably post-ERCP pancreatitis (PEP), which has an incidence ranging from 3.5 to 9.7%. OBJECTIVE: This study aims to develop machine learning-based predictive models for PEP in patients receiving prophylacti...

Long-term outcomes with emicizumab prophylaxis for haemophilia A in China: A multicentre, large-cohort retrospective study.

Emicizumab is an established prophylactic therapy for haemophilia A (HA). However, real-world multicentre evidence on long-term outcomes, particularly...

Protein language models are accidental taxonomists.

Protein-protein interactions (PPIs) are fundamental to nearly all biological processes, yet their experimental characterization remains costly and tim...

Predictions of protein-protein interactions and co-complex models with deep learning.

Protein-protein interactions (PPIs) are fundamental to cellular processes, and essential for understanding biological function and disease mechanisms....

Identification of diagnostic and prognostic hub genes in osteoarthritis: an integrated bioinformatics study.

BACKGROUND: Osteoarthritis (OA) is a heterogeneous whole-joint disease and a leading cause of pain and disability worldwide. Synovial inflammation pla...

Deep learning-based automatic segmentation of rectal tumors in endoscopic images.

PURPOSE: Endoscopy is critical in the identification of rectal tumors, but is prone to observer errors. The aim of this study was to assess the inter-...

Interpretable machine learning for endometriosis classification: a rule-based approach.

BACKGROUND: Endometriosis is a chronic gynecological disease characterized by the growth of endometrial-like tissue outside the uterus, leading to pel...

Combinations of single gene predictors with AI-driven polygenic precision therapies in cardiovascular diseases.

Pharmacogenomics (PGx) is transforming how we treat cardiovascular disease (CVD) by enabling us to select and dose drugs based on our genetic profiles...

Machine Learning for Intraoperative Bleeding Prediction in Patients Undergoing Surgery: Scoping Review.

BACKGROUND: Intraoperative bleeding is a critical event that impacts surgical safety and patient outcomes. Machine learning (ML) has demonstrated pote...

AI-assisted improvement of endoscopic diagnosis of ampullary lesions.

Ampullary neoplasms are often challenging to detect during forward-viewing esophagogastroduodenoscopy. This study aimed to evaluate the impact of arti...

StyleGAN-based synthetic image augmentation for multi-class otoscopy image classification.

Accurate diagnosis of eardrum abnormalities is pivotal for effectively managing various ear conditions. Otoscopy, a non-invasive diagnostic procedure,...

SERPINA3 and NDRG1 are critical diagnostic immune genes associated with macrophages in preeclampsia.

Preeclampsia (PE) is a pregnancy complication involving immune dysregulation. This study aims to identify diagnostic immune biomarkers for PE using ma...

Generative artificial intelligence in endoscopy: distinguishing opportunity from risk.

Deciphering the potential molecular links between ochratoxin A and colorectal cancer: an integrated computational toxicological and single-cell transcriptomic study.

BACKGROUND: Ochratoxin A (OTA), a common foodborne mycotoxin, is classified as a potential human carcinogen. However, the specific molecular mechanism...

LightGastroFormer: a lightweight multi-resolution transformer for gastrointestinal disease classification.

In order to promote early diagnosis and lessen the workload of doctors during endoscopic and capsule endoscopy tests, automated analysis of gastrointe...

Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.

Artificial intelligence (AI) is rapidly transforming healthcare, supporting disease management and enabling outcome prediction across multiple clinica...

A histopathologically verified dataset of magnifying narrow-band imaging endoscopy for classifying the gastric precancerous cascade.

Gastric cancer is a leading cause of mortality worldwide, yet the development of computer-aided diagnosis (CAD) systems for its early detection is hin...

A novel deep learning network for small bowel ulcerative lesion detection and differential diagnosis on double-balloon endoscopy images.

Differentiating small bowel ulcerative diseases (SBUDs) on double-balloon endoscopy (DBE) is challenging. We aimed to develop an artificial intelligen...

Dual-stream token fusion with Swin Transformer and lesion-aware tokens for gastric metaplasia classification in IoMT-assisted deployment.

BACKGROUND: Gastric intestinal metaplasia (GIM) is often visually inconspicuous on routine endoscopy, while many artificial intelligence systems rely ...