Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study

PURPOSE: To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload. METHODS AND MATERIALS: A total of 2652 DM exams (653 cancer) and interpretations by 101 radiologists were gat...

ver descrição completa

Na minha lista:
Detalhes bibliográficos
Publicado no:Eur Radiol
Main Authors: Rodriguez-Ruiz, Alejandro, Lång, Kristina, Gubern-Merida, Albert, Teuwen, Jonas, Broeders, Mireille, Gennaro, Gisella, Clauser, Paola, Helbich, Thomas H., Chevalier, Margarita, Mertelmeier, Thomas, Wallis, Matthew G., Andersson, Ingvar, Zackrisson, Sophia, Sechopoulos, Ioannis, Mann, Ritse M.
Formato: Artigo
Idioma:Inglês
Publicado em: Springer Berlin Heidelberg 2019
Assuntos:
Breast
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC6682851/
https://ncbi.nlm.nih.gov/pubmed/30993432
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s00330-019-06186-9
Tags: Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!

Registos relacionados