Latest AI and machine learning research in inflammatory bowel disease for healthcare professionals.
Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy ...
Key variables recorded as text in colonoscopy and pathology reports have been extracted using natural language processing (NLP) tools that were not easily adaptable to new settings. We aimed to develop a reliable NLP tool with broad adaptability. During 1996-2016, Kaiser Permanente Northern California performed 401,566 colonoscopies with linked pathology. We randomly sampled 1000 linked reports in...
Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they...
A new method to predict coronary artery lesions (CALs) in Kawasaki disease (KD) was developed using a mean structure equation model (SEM) and neural n...
Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two o...
BACKGROUND: Severe bleeding and perforation of the colon and rectum are complications of ulcerative colitis which can be treated by a targeted drug de...
BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recogn...
Colonoscopy is commonly used to screen for colorectal cancer (CRC). We develop a deep learning model called CRCNet for optical diagnosis of CRC by tra...
Artificial intelligence (AI) technologies in clinical medicine have become the subject of intensive investigative efforts and popular attention. In do...
BACKGROUND AND STUDY AIMS: Small polyps are occasionally missed during colonoscopy. This study was conducted to validate the diagnostic performance of...
BACKGROUND: Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One w...
The industrial revolution that began in the late 1800s has resulted in dramatic changes in the environment, human lifestyle, dietary habits, social st...
BACKGROUND & AIMS: One-fourth of colorectal neoplasias are missed during screening colonoscopies; these can develop into colorectal cancer (CRC). Deep...
Nutrient recovery from source-separated human urine has attracted interest as it is rich in nitrogen and phosphorus that can be utilized as fertilizer...
Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testin...
BACKGROUND AND AIM: The utility of artificial intelligence (AI) in colonoscopy has gained popularity in current times. Recent trials have evaluated th...
Mild cognitive impairment (MCI) may be caused by Alzheimer's disease, Parkinson's disease (PD), cerebrovascular accident, nutritional or metabolic dis...
Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary c...
Radiomics is an emerging field that involves extraction and quantification of features from medical images. These data can be mined through computatio...
Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and survival prediction. Our goal is to compare the ha...