Publications by authors named "Michael Meinikheim"
Article Synopsis
- This study assessed how an AI clinical decision support system affected the diagnostic performance and confidence of endoscopists evaluating Barrett's esophagus (BE) using standardized endoscopy videos.
- The study involved 22 endoscopists analyzing 96 videos, comparing assessments with and without the AI system for its impact on detecting Barrett's esophagus-related neoplasia (BERN).
- Results indicated that while AI outperformed human experts in sensitivity, specificity, and accuracy, non-expert endoscopists showed notable improvement when using AI, suggesting that while AI can enhance decision-making, various factors may affect how endoscopists incorporate AI recommendations.
Background: Texture and color enhancement imaging (TXI) was recently proposed as a substitute for standard high definition white-light imaging (WLI) to increase lesion detection during colonoscopy. This international, multicenter randomized trial assessed the efficacy of TXI in detection of colorectal neoplasia.
Methods: Consecutive patients aged ≥ 40 years undergoing screening, surveillance, or diagnostic colonoscopies at five centers (Italy, Germany, Japan) between September 2021 and May 2022 were enrolled.
Barrett's esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett's esophagus during routine endoscopy are challenging, even for expert endoscopists.
View Article and Find Full Text PDFBackground And Aims: Celiac disease with its endoscopic manifestation of villous atrophy (VA) is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of VA at routine EGD may improve diagnostic performance.
Methods: A dataset of 858 endoscopic images of 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet18 deep learning model to detect VA.