AIMC Topic: Colonoscopy

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Protocol for a multicenter randomized controlled trial to assess the usefulness of computer-aided detection systems for colonoscopy in colorectal cancer screening in the Asia-Pacific region (project CAD/NCCH2217).

Japanese journal of clinical oncology
Ensuring the high quality of colonoscopies in colorectal cancer (CRC) screening is essential to reducing CRC. Recently, computer-aided detection systems (CADe) that use artificial intelligence have attracted much attention as potentially useful tools...

Efficient polyp detection algorithm based on deep learning.

Scandinavian journal of gastroenterology
OBJECTIVE: Colon polyp detection is crucial in reducing the incidence of colorectal cancer. However, due to the diverse morphology of colon polyps, their high similarity to surrounding tissues, and the difficulty of detecting small target polyps, fal...

Use of computer-assisted detection (CADe) colonoscopy in colorectal cancer screening and surveillance: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement.

Endoscopy
This statement conveys the European Society of Gastrointestinal Endoscopy (ESGE) position on the use of computer-aided detection (CADe) with artificial intelligence (AI) during colonoscopy for colorectal cancer (CRC) screening or surveillance. The ES...

Development and validation of the Open-Source Automatic Bowel Preparation Scale.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Insufficient bowel preparation accounts for up to 42% of missed adenomas in colonoscopy. However, major analysis programs found no correlation between adenoma detection rate and the human-rated Boston Bowel Preparation Scale (BBP...

GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification.

IEEE journal of biomedical and health informatics
A major challenge in applying deep learning to medical imaging is the paucity of annotated data. This study explores the use of synthetic images for data augmentation to address the challenge of limited annotated data in colonoscopy lesion classifica...

Optimizing Pix2Pix GAN With Attention Mechanisms for AI-Driven Polyp Segmentation in IoMT-Enabled Smart Healthcare.

IEEE journal of biomedical and health informatics
This paper introduces an innovative approach for automated polyp segmentation in colonoscopy images, deploying an enhanced Pix2Pix Generative Adversarial Network (GAN) equipped with an integrated attention mechanism in the discriminator. Addressing p...

Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing.

Scientific data
Colorectal cancer often arises from precancerous polyps, where accurate size assessment is vital for clinical decisions but challenged by subjective methods. While artificial intelligence (AI) has shown promise in improving the accuracy of polyp size...

Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up.

World journal of gastroenterology
Colorectal cancer remains a major health concern, with colorectal polyps as key precursors. Endoscopic mucosal resection (EMR) is a common treatment, but recurrence rates remain high. Traditional surveillance strategies rely on polyp characteristics ...

[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...

Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection.

World journal of gastroenterology
BACKGROUND: Colorectal polyps are precancerous diseases of colorectal cancer. Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer. Endoscopic mucosal resection (EMR) is a common polypectomy pro...