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Overview of trials on artificial intelligence algorithms in breast cancer screening - A roadmap for international evaluation and implementation.

Published version
Peer-reviewed

Repository DOI


Type

Article

Change log

Authors

van Nijnatten, TJA 
Payne, NR 
Hickman, SE 
Ashrafian, H 
Gilbert, FJ 

Abstract

Accumulating evidence from retrospective studies demonstrate at least non-inferior performance when using AI algorithms with different strategies versus double-reading in mammography screening. In addition, AI algorithms for mammography screening can reduce work load by moving to single human reading. Prospective trials are essential to avoid unintended adverse consequences before incorporation of AI algorithms into UK's National Health Service (NHS) Breast Screening Programme (BSP). A stakeholders' meeting was organized in Newnham College, Cambridge, UK to undertake a review of the current evidence to enable consensus discussion on next steps required before implementation into a screening programme. It was concluded that a multicentre multivendor testing platform study with opt-out consent is preferred. AI thresholds from different vendors should be determined while maintaining non-inferior screening performance results, particularly ensuring recall rates are not increased. Automatic recall of cases using an agreed high sensitivity AI score versus automatic rule out with a low AI score set at a high sensitivity could be used. A human reader should still be involved in decision making with AI-only recalls requiring human arbitration. Standalone AI algorithms used without prompting maintain unbiased screening reading performance, but reading with prompts should be tested prospectively and ideally provided for arbitration.

Description

Keywords

Algorithms, Artificial Intelligence, Breast imaging, Evaluation, Screening, Humans, Female, Early Detection of Cancer, Artificial Intelligence, Breast Neoplasms, Prospective Studies, Retrospective Studies, State Medicine, Algorithms

Journal Title

Eur J Radiol

Conference Name

Journal ISSN

0720-048X
1872-7727

Volume Title

167

Publisher

Elsevier BV
Sponsorship
Cancer Research UK (A26884)