AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study.
Humans
Mammography
/ methods
Female
Breast Neoplasms
/ diagnostic imaging
Feasibility Studies
Retrospective Studies
Middle Aged
Artificial Intelligence
Aged
Early Detection of Cancer
/ methods
Deep Learning
Radiographic Image Interpretation, Computer-Assisted
/ methods
Mass Screening
/ methods
Sensitivity and Specificity
Reproducibility of Results
Breast
Convolutional Neural Network (CNN)
Diagnosis
Epidemiology
Mammography
Neoplasms-Primary
Screening
Journal
Radiology. Artificial intelligence
ISSN: 2638-6100
Titre abrégé: Radiol Artif Intell
Pays: United States
ID NLM: 101746556
Informations de publication
Date de publication:
Nov 2024
Nov 2024
Historique:
medline:
2
10
2024
pubmed:
4
9
2024
entrez:
4
9
2024
Statut:
ppublish
Résumé
Mammography screening supported by deep learning-based artificial intelligence (AI) solutions can potentially reduce workload without compromising breast cancer detection accuracy, but the site of deployment in the workflow might be crucial. This retrospective study compared three simulated AI-integrated screening scenarios with standard double reading with arbitration in a sample of 249 402 mammograms from a representative screening population. A commercial AI system replaced the first reader (scenario 1: integrated AI
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM