Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms.
BMC Cancer
; 23(1): 460, 2023 May 19.
Article
en En
| MEDLINE
| ID: mdl-37208717
ABSTRACT
BACKGROUND:
Double reading (DR) in screening mammography increases cancer detection and lowers recall rates, but has sustainability challenges due to workforce shortages. Artificial intelligence (AI) as an independent reader (IR) in DR may provide a cost-effective solution with the potential to improve screening performance. Evidence for AI to generalise across different patient populations, screening programmes and equipment vendors, however, is still lacking.METHODS:
This retrospective study simulated DR with AI as an IR, using data representative of real-world deployments (275,900 cases, 177,882 participants) from four mammography equipment vendors, seven screening sites, and two countries. Non-inferiority and superiority were assessed for relevant screening metrics.RESULTS:
DR with AI, compared with human DR, showed at least non-inferior recall rate, cancer detection rate, sensitivity, specificity and positive predictive value (PPV) for each mammography vendor and site, and superior recall rate, specificity, and PPV for some. The simulation indicates that using AI would have increased arbitration rate (3.3% to 12.3%), but could have reduced human workload by 30.0% to 44.8%.CONCLUSIONS:
AI has potential as an IR in the DR workflow across different screening programmes, mammography equipment and geographies, substantially reducing human reader workload while maintaining or improving standard of care. TRIAL REGISTRATION ISRCTN18056078 (20/03/2019; retrospectively registered).Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Risk_factors_studies
/
Screening_studies
Límite:
Female
/
Humans
Idioma:
En
Revista:
BMC Cancer
Asunto de la revista:
NEOPLASIAS
Año:
2023
Tipo del documento:
Article
País de afiliación:
Reino Unido