Latest AI and machine learning research in inflammatory bowel disease for healthcare professionals.
A major challenge in applying deep learning to medical imaging is the paucity of annotated data. This study explores the use of synthetic images for data augmentation to address the challenge of limited annotated data in colonoscopy lesion classification. We demonstrate that synthetic colonoscopy images generated by Generative Adversarial Network (GAN) inversion can be used as training data to imp...
This statement conveys the European Society of Gastrointestinal Endoscopy (ESGE) position on the use of computer-aided detection (CADe) with artificial intelligence (AI) during colonoscopy for colorectal cancer (CRC) screening or surveillance. The ESGE position is informed by the BMJ Rapid Recommendation initiative and the approach of the MAGIC Evidence Ecosystem Foundation; these include systemat...
BACKGROUND AND AIMS: Insufficient bowel preparation accounts for up to 42% of missed adenomas in colonoscopy. However, major analysis programs found n...
We introduce XYZ-IBD, a bin-picking dataset for 6D pose estimation that captures real-world industrial complexity, including challenging object geom...
Colorectal cancer often arises from precancerous polyps, where accurate size assessment is vital for clinical decisions but challenged by subjective m...
Traditional diagnostic methods like colonoscopy are invasive yet critical tools necessary for accurately diagnosing colorectal cancer (CRC). Detecti...
The radiological dosimetric parameters and clinical features were screened by machine learning to construct a prediction model for the short-term effi...
Circulating tumor cells (CTCs) serve as valuable biomarkers in tumor circulation, carrying essential primary tumor information. The purification of CT...
Objective: Ulcerative colitis (UC), characterized by chronic inflammation with alternating remission-relapse cycles, requires precise histological h...
Pre-training on image-text colonoscopy records offers substantial potential for improving endoscopic image analysis, but faces challenges including ...
BACKGROUND: Achieving long-term clinical remission in Crohn's disease (CD) with antitumor necrosis factor α (anti-TNF-α) agents remains challenging.
Identifying host defense peptides (HDPs) that are effective against drug-resistant infections is challenging due to their vast sequence space. Artific...
Current immunotherapeutic approaches for autoimmune disorders primarily rely on the use of generalized immunosuppressive medications. However, most im...
BACKGROUND AND AIMS: Inflammatory bowel diseases (IBD) are chronic conditions that can lead to a physical, social, and economic burden. Generative art...
Rotator cuff tears are a common cause of shoulder pain and dysfunction, affecting up to 33% of the population, and approximately 250,000 arthroscopic ...
The generation of realistic medical images from text descriptions has significant potential to address data scarcity challenges in healthcare AI whi...
Steroids are biologically active polycyclic compounds that have garnered significant scientific attention due to their distinct physiochemical propert...
Inflammatory bowel disease (IBD), comprising ulcerative colitis and Crohn's disease, is a chronic inflammatory condition with global prevalence and va...
Automatic segmentation of anatomical landmarks in endoscopic images can provide assistance to doctors and surgeons for diagnosis, treatments or medi...
OBJECTIVE: Machine learning (ML) techniques have shown promise for enhancing prediction of clinical outcomes; however, its application to predicting b...