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Adenoma

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Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial.

Gastroenterology
BACKGROUND & AIMS: One-fourth of colorectal neoplasias are missed during screening colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems allow for real-time computer-aided detection (CADe) of polyps with high accuracy. ...

The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: The utility of artificial intelligence (AI) in colonoscopy has gained popularity in current times. Recent trials have evaluated the efficacy of deep convolutional neural network (DCNN)-based AI system in colonoscopy for improving ...

How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening.

Gastrointestinal endoscopy clinics of North America
Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improv...

A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging.

European journal of radiology
PURPOSE: The type of pituitary adenoma (PA) cannot be clearly recognized with preoperative magnetic resonance imaging (MRI) but can be classified with immunohistochemical staining after surgery. In this study, a model to precisely immunohistochemical...

Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

The lancet. Gastroenterology & hepatology
BACKGROUND: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system...

Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model.

Scientific reports
We aimed to develop a computer-aided diagnostic system (CAD) for predicting colorectal polyp histology using deep-learning technology and to validate its performance. Near-focus narrow-band imaging (NBI) pictures of colorectal polyps were retrieved f...

Challenges Facing the Detection of Colonic Polyps: What Can Deep Learning Do?

Medicina (Kaunas, Lithuania)
Colorectal cancer (CRC) is one of the most common causes of cancer mortality in the world. The incidence is related to increases with age and western dietary habits. Early detection through screening by colonoscopy has been proven to effectively redu...

Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.

Neuroradiology
PURPOSE: Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker whic...

Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT.

Clinical radiology
AIM: To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluation by radiologists for differentiation between large adrenal adenomas and carcinomas.