AI Medical Compendium Topic

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Colonoscopy

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Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Pattern analysis of big data can provide a superior direction for the clinical differentiation of diseases with similar endoscopic findings. This study aimed to develop a deep-learning algorithm that performs differential diagnosi...

A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution.

Sensors (Basel, Switzerland)
Colon carcinoma is one of the leading causes of cancer-related death in both men and women. Automatic colorectal polyp segmentation and detection in colonoscopy videos help endoscopists to identify colorectal disease more easily, making it a promisin...

A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images.

Surgical endoscopy
OBJECTIVES: Computer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be highly accurate for detecting and characterizing colon polyps. However, the application of AI to identify normal colon landmarks and differentiate mult...

Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network.

BioMed research international
Colorectal imaging improves on diagnosis of colorectal diseases by providing colorectal images. Manual diagnosis of colorectal disease is labor-intensive and time-consuming. In this paper, we present a method for automatic colorectal disease classifi...

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method.

Endoscopy
BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified D...

Learning Spatiotemporal Features for Esophageal Abnormality Detection From Endoscopic Videos.

IEEE journal of biomedical and health informatics
Esophageal cancer is categorized as a type of disease with a high mortality rate. Early detection of esophageal abnormalities (i.e. precancerous and early cancerous) can improve the survival rate of the patients. Recent deep learning-based methods fo...

Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Artificial intelligence (AI) and its application in medicine has grown large interest. Within gastrointestinal (GI) endoscopy, the field of colonoscopy and polyp detection is the most investigated, however, upper GI follows the lead. Since endoscopy ...

Colorectal polyp characterization with endocytoscopy: Ready for widespread implementation with artificial intelligence?

Best practice & research. Clinical gastroenterology
Endocytoscopy provides an in-vivo visualization of nuclei and micro-vessels at the cellular level in real-time, facilitating so-called "optical biopsy" or "virtual histology" of colorectal polyps/neoplasms. This functionality is enabled by 520-fold m...