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A comparative study benchmarking colon polyp with computer-aided detection (CADe) software.
DEN Open
April 2025
Department of Molecular and Clinical Medicine, Institute of Medicine University of Gothenburg Gothenburg Sweden.
Background And Aims: Computer-aided detection software (CADe) has shown promising results in real-time polyp detection, but a limited head-to-head comparison of the available CADe systems has been performed. Moreover, such systems have not been compared to endoscopists using standardized videos. This study aims to compare the performance of three CADe systems in detecting polyps, employing a novel standardized methodology.
View Article and Find Full Text PDFEpidemiology of bacterial biofilms on polyps and normal tissues in a screening colonoscopy cohort.
Gut Microbes
December 2025
Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Background: Invasive bacterial biofilms are implicated in colorectal cancer. However, their prevalence on histologically normal tissues and polyps is not well established, and risk factors of biofilms have not been previously investigated. Here we evaluated potential procedural and demographic risk factors associated with biofilm status using a cross-sectional observational cohort.
View Article and Find Full Text PDFSynthesized colonoscopy dataset from high-fidelity virtual colon with abnormal simulation.
Comput Biol Med
January 2025
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, China. Electronic address:
With the advent of the deep learning-based colonoscopy system, the need for a vast amount of high-quality colonoscopy image datasets for training is crucial. However, the generalization ability of deep learning models is challenged by the limited availability of colonoscopy images due to regulatory restrictions and privacy concerns. In this paper, we propose a method for rendering high-fidelity 3D colon models and synthesizing diversified colonoscopy images with abnormalities such as polyps, bleeding, and ulcers, which can be used to train deep learning models.
View Article and Find Full Text PDFInCoLoTransNet: An Involution-Convolution and Locality Attention-Aware Transformer for Precise Colorectal Polyp Segmentation in GI Images.
J Imaging Inform Med
January 2025
Faculty of Medicine and Pharmacy of Rabat, Mohammed V University of Rabat, Rabat, 10000, Morocco.
Gastrointestinal (GI) disease examination presents significant challenges to doctors due to the intricate structure of the human digestive system. Colonoscopy and wireless capsule endoscopy are the most commonly used tools for GI examination. However, the large amount of data generated by these technologies requires the expertise and intervention of doctors for disease identification, making manual analysis a very time-consuming task.
View Article and Find Full Text PDFA multi-gene blood-based methylation assay for early diagnosis of colorectal cancer.
Transl Cancer Res
December 2024
Department of Anorectal Surgery, the Second Hospital of Tianjin Medical University, Tianjin, China.
Background: Early detection for colorectal cancer (CRC) can enhance the patient prognosis. We aimed to validate the combined multi-gene detection in plasma of , , , and for early diagnosing of CRC in this prospective study.
Methods: Overall, 124 participants including 45 CRC patients, 8 advanced adenoma patients, 34 small polyp patients, and 37 normal controls who underwent colonoscopy were enrolled.
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