Evaluation of an artificial intelligence support system for breast cancer screening in Chinese people based on mammogram

Background To evaluate the diagnostic performance of radiologists on breast cancer with or without artificial intelligence (AI) support. Methods A retrospective study was performed. In total, 643 mammograms (average age: 54 years; female: 100%; cancer: 62.05%) were randomly allocated into two groups...

Full description

Saved in:
Bibliographic Details
Published in:Cancer medicine (Malden, MA) MA), 2023-02, Vol.12 (3), p.3718-3726
Main Authors: Bao, Chengzhen, Shen, Jie, Zhang, Yue, Zhang, Yan, Wei, Wei, Wang, Ziteng, Ding, Jia, Han, Lili
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
AUC
Biopsy
Breast cancer
Breast Neoplasms - diagnosis
Calcification
Cancer screening
Early Detection of Cancer
East Asian People
Female
Humans
Mammography
Medical screening
Middle Aged
Prospective Studies
Retrospective Studies
sensitivity
Software
specificity
Work stations
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background To evaluate the diagnostic performance of radiologists on breast cancer with or without artificial intelligence (AI) support. Methods A retrospective study was performed. In total, 643 mammograms (average age: 54 years; female: 100%; cancer: 62.05%) were randomly allocated into two groups. Seventy‐five percent of mammograms in each group were randomly selected for assessment by two independent radiologists, and the rest were read once. Half of the 71 radiologists could read mammograms with AI support, and the other half could not. Sensitivity, specificity, Youden's index, agreement rate, Kappa value, the area under the receiver operating characteristic curve (AUC) and the reading time of radiologists in each group were analyzed. Results The average AUC was higher if the AI support system was used (unaided: 0.84; with AI support: 0.91; p 
ISSN:2045-7634
2045-7634
DOI:10.1002/cam4.5231