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Adenoma

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Cost-Effectiveness for Artificial Intelligence in Colonoscopy.

Gastrointestinal endoscopy clinics of North America
Artificial intelligence (AI) is set to transform the field of colonoscopy through the implementation of computer-assisted detection and diagnosis. While over 20 randomized controlled trials have demonstrated the efficacy of AI in increasing adenoma d...

Application of an Automated Deep Learning Program to A Diagnostic Classification Model: Differentiating High-Risk Adenomas Among Colorectal Polyps 10 mm or Smaller.

Journal of digestive diseases
OBJECTIVE: This study aimed to develop a computer-aided diagnosis (CADx) model using an automated deep learning (DL) program to classify low- and high-risk adenomas among colorectal polyps ≤ 10 mm with standard white-light endoscopy.

Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study.

World journal of surgical oncology
BACKGROUND: With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperativ...

Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma.

Scientific reports
This study aimed to develop and validate machine learning (ML) models to predict the occurrence of delayed hyponatremia after transsphenoidal surgery for pituitary adenoma. We retrospectively collected clinical data on patients with pituitary adenoma...

Artificial Intelligence Models Could Enhance the Diagnostic Accuracy (DA) of Fecal Immunochemical Test (FIT) in the Detection of Colorectal Adenoma in a Screening Setting.

Anticancer research
BACKGROUND/AIM: This study evaluated the diagnostic accuracy (DA) for colorectal adenomas (CRA), screened by fecal immunochemical test (FIT), using five artificial intelligence (AI) models: logistic regression (LR), support vector machine (SVM), neur...

Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition.

Scientific data
Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces the adenoma miss rate (AMR). However, few studies address the critical issue of endoscopist-AI collab...

A Novel Natural Language Processing Tool Improves Colonoscopy Auditing of Adenoma and Serrated Polyp Detection Rates.

Journal of gastroenterology and hepatology
BACKGROUND AND STUDY AIMS: Determining adenoma detection rate (ADR) and serrated polyp detection rate (SDR) can be challenging as they usually involve manual matching of colonoscopy and histology reports. This study aimed to validate a Natural Langua...

[Role of Artificial Intelligence in Improving Quality of Colonoscopy].

The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi
Colorectal cancer is a common malignancy and a major health concern in Korea. Although colonoscopy is an effective tool for screening and preventing colorectal cancer through the early detection of pre-cancerous lesions, many factors influence the qu...

A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI.

Neuroradiology
PURPOSE: Magnetic resonance imaging (MRI) is an essential technique for diagnosing pituitary adenomas; however, it is also challenging for neurosurgeons to use it to precisely identify some types of microadenomas. A novel neural network model was dev...