A qualitative systematic review of anonymous/unspecified living kidney and liver donors' perspectives.

Objectives & Background: Anonymous live organ donors or unspecified donors are individuals willing to be organ donors for any transplant recipient with whom they have no biological or antecedent emotional relationship. Despite excellent recipient outcomes and the potential to help address organ scarcity, controversy surrounds the unconditional act of gifting one's organs to an unrelated recipient. This qualitative systematic review provides insights into the first-hand experiences, motivations, and challenges that unspecified donors face.

Computer-assisted detection versus conventional colonoscopy for proximal colonic lesions: a multicenter, randomized, tandem-colonoscopy study.

Background And Aims: Computer-assisted detection (CADe) is a promising technologic advance that enhances adenoma detection during colonoscopy. However, the role of CADe in reducing missed colonic lesions is uncertain. The aim of this study was to determine the miss rates of proximal colonic lesions by CADe and conventional colonoscopy.

Feedback from artificial intelligence improved the learning of junior endoscopists on histology prediction of gastric lesions.

Artificial intelligence (AI)-assisted image classification has been shown to have high accuracy on endoscopic diagnosis. We evaluated the potential effects of use of an AI-assisted image classifier on training of junior endoscopists for histological prediction of gastric lesions. An AI image classifier was built on a convolutional neural network with five convolutional layers and three fully connected layers A Resnet backbone was trained by 2,000 non-magnified endoscopic gastric images.

Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence.

 We evaluated use of artificial intelligence (AI) assisted image classifier in determining the feasibility of curative endoscopic resection of large colonic lesion based on non-magnified endoscopic images  AI image classifier was trained by 8,000 endoscopic images of large (≥ 2 cm) colonic lesions. The independent validation set consisted of 567 endoscopic images from 76 colonic lesions. Histology of the resected specimens was used as gold standard.