Title :

Impact of artificial intelligence in breast cancer screening with mammography

Author(s) :

Dang, Lan-Anh [Auteur]
Centre hospitalier [Valenciennes, Nord]
Chazard, Emmanuel [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Poncelet, Edouard [Auteur]
Centre hospitalier [Valenciennes, Nord]
Serb, Teodora [Auteur]
Centre hospitalier [Valenciennes, Nord]
Rusu, Aniela [Auteur]
Centre hospitalier [Valenciennes, Nord]
Pauwels, Xavier [Auteur]
Centre hospitalier [Valenciennes, Nord]
Parsy, Clémence [Auteur]
Centre hospitalier [Valenciennes, Nord]
Poclet, Thibault [Auteur]
Centre hospitalier [Valenciennes, Nord]
Cauliez, Hugo [Auteur]
Centre hospitalier [Valenciennes, Nord]
Engelaere, Constance [Auteur]
Centre hospitalier [Valenciennes, Nord]
Ramette, Guillaume [Auteur]
Centre hospitalier [Valenciennes, Nord]
Brienne, Charlotte [Auteur]
Centre hospitalier [Valenciennes, Nord]
Dujardin, Sofiane [Auteur]
Centre hospitalier [Valenciennes, Nord]
Laurent, Nicolas [Auteur]
Centre hospitalier [Valenciennes, Nord]

Journal title :

Breast Cancer

Abbreviated title :

Breast Cancer

Volume number :

29

Pages :

967–977

Publication date :

2022-07-06

ISSN :

1340-6868

English keyword(s) :

Artificial intelligence
Breast cancer
Mammography
BI-RADS classification

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

Objectives To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, ...
Show more >Objectives To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time. Methods A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or “continuous BI-RADS 100”. Cohen’s kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed. Results On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528–0.571) without AI and κ = 0.626, 95% CI (0.607–0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754). Conclusions When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CHU Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Submission date :

2023-11-15T03:47:21Z
2024-04-05T06:38:34Z