This work sought to quantify pathologists' diagnostic bias over time in their evaluation of colorectal polyps to assess how this may impact the utility of statistical process control (SPC). All colorectal polyp specimens(CRPS) for 2011-2017 in a region were categorized using a validated free text string matching algorithm. Pathologist diagnostic rates (PDRs) for high grade dysplasia (HGD), tubular adenoma (TA_ad), villous morphology (TVA + VA), sessile serrated adenoma (SSA) and hyperplastic polyp (HP), were assessed (1) for each pathologist in yearly intervals with control charts (CCs), and (2) with a generalized linear model (GLM). The study included 64,115 CRPS. Fifteen pathologists each interpreted > 150 CRPS/year in all years and together diagnosed 38,813. The number of pathologists (of 15) with zero or one (p < 0.05) outlier in seven years, compared to their overall PDR, was 13, 9, 9, 5 and 9 for HGD, TVA + VA, TA_ad, HP and SSA respectively. The GLM confirmed, for the subset where pathologists/endoscopists saw > 600 CRPS each(total 52,760 CRPS), that pathologist, endoscopist, anatomical location and year were all strongly correlated (all p < 0.0001) with the diagnosis. The moderate PDR stability over time supports the hypothesis that diagnostic rates are amendable to calibration via SPC and outcome data.
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http://dx.doi.org/10.1038/s41598-021-95862-2 | DOI Listing |
Ther Adv Gastrointest Endosc
December 2024
Department of Internal Medicine, Hanoi Medical University, Hanoi, Vietnam.
The utilization of artificial intelligence (AI) in gastrointestinal (GI) endoscopy has witnessed significant progress and promising results in recent years worldwide. From 2019 to 2023, the European Society of Gastrointestinal Endoscopy has released multiple guidelines/consensus with recommendations on integrating AI for detecting and classifying lesions in practical endoscopy. In Vietnam, since 2019, several preliminary studies have been conducted to develop AI algorithms for GI endoscopy, focusing on lesion detection.
View Article and Find Full Text PDFBMC Res Notes
December 2024
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
This dataset contains demographic, morphological and pathological data, endoscopic images and videos of 191 patients with colorectal polyps. Morphological data is included based on the latest international gastroenterology classification references such as Paris, Pit and JNET classification. Pathological data includes the diagnosis of the polyps including Tubular, Villous, Tubulovillous, Hyperplastic, Serrated, Inflammatory and Adenocarcinoma with Dysplasia Grade & Differentiation.
View Article and Find Full Text PDFGastrointest Endosc
December 2024
Division of Gastroenterology, Thomas Jefferson University Hospital, Philadelphia, PA. Electronic address:
Background And Aims: Interest in cold endoscopic mucosal resection (EMR) for colorectal polyps has been growing lately. We conducted a meta-analysis of RCTs to compare cold and hot EMR for colorectal polyps.
Methods: We reviewed several databases from inception to October 06, 2024.
Background: Artificial intelligence (AI) has significantly impacted medical imaging, particularly in gastrointestinal endoscopy. Computer-aided detection and diagnosis systems (CADe and CADx) are thought to enhance the quality of colonoscopy procedures.
Summary: Colonoscopy is essential for colorectal cancer screening, but often misses a significant percentage of adenomas.
Am J Gastroenterol
August 2024
US Navy, Washington, DC, USA.
Article Title: Adenomas and Sessile Serrated Lesions in 45-49-Year-Old Individuals Undergoing Colonoscopy: A Systematic Review and Meta-Analysis.
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