AIMC Topic: Gastrointestinal Diseases

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Application of artificial intelligence in gastrointestinal endoscopy.

Arab journal of gastroenterology : the official publication of the Pan-Arab Association of Gastroenterology
Endoscopy is an important method for diagnosing gastrointestinal (GI) diseases. In this study, we provide an overview of the advances in artificial intelligence (AI) technology in the field of GI endoscopy over recent years, including esophagus, stom...

The Evolving Role of Artificial Intelligence in Gastrointestinal Histopathology: An Update.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
Significant advances in artificial intelligence (AI) over the past decade potentially may lead to dramatic effects on clinical practice. Digitized histology represents an area ripe for AI implementation. We describe several current needs within the w...

Re-investigation of functional gastrointestinal disorders utilizing a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: Functional gastrointestinal disorders (FGIDs), as a group of syndromes with no identified structural or pathophysiological biomarkers, are currently classified by Rome criteria based on gastrointestinal symptoms (GI). However, the high ov...

Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images.

International journal of medical informatics
BACKGROUND: Gastrointestinal (GI) infections are quite common today around the world. Colonoscopy or wireless capsule endoscopy (WCE) are noninvasive methods for examining the whole GI tract for abnormalities. Nevertheless, it requires a great deal o...

Introduction.

Best practice & research. Clinical gastroenterology
Endoscopic ultrasound (EUS) was born from the combination of a high-frequency ultrasound probe with an endoscope to assess in detail the walls of the upper and lower gastrointestinal tract and surrounding organs and structures. The subsequent possibi...

Identification of upper GI diseases during screening gastroscopy using a deep convolutional neural network algorithm.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: The clinical application of GI endoscopy for the diagnosis of multiple diseases using artificial intelligence (AI) has been limited by its high false-positive rates. There is an unmet need to develop a GI endoscopy AI-assisted di...

Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases.

Sensors (Basel, Switzerland)
Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading causes of death worldwide. Thus, early detection of the type of tumor is of great importance in the s...

Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model.

Computational and mathematical methods in medicine
Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical pe...

Automated Bowel Sound Analysis: An Overview.

Sensors (Basel, Switzerland)
Despite technological progress, we lack a consensus on the method of conducting automated bowel sound (BS) analysis and, consequently, BS tools have not become available to doctors. We aimed to briefly review the literature on BS recording and analys...

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...