Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy

作者：Karoline Freeman, Julia Geppert, Chris Stinton, Daniel Todkill, Samantha Johnson, Aileen Clarke, Sian Taylor‐Phillips · 发表于：BMJ · 年份：2021 · 被引用次数：350 · 研究领域：AI in cancer detection、Artificial Intelligence in Healthcare and Education、Radiomics and Machine Learning in Medical Imaging

OBJECTIVE: To examine the accuracy of artificial intelligence (AI) for the detection of breast cancer in mammography screening practice. DESIGN: Systematic review of test accuracy studies. DATA SOURCES: Medline, Embase, Web of Science, and Cochrane Database of Systematic Reviews from 1 January 2010 to 17 May 2021. ELIGIBILITY CRITERIA: Studies reporting test accuracy of AI algorithms, alone or in combination with radiologists, to detect cancer in women's digital mammograms in screening practice, or in test sets. Reference standard was biopsy with histology or follow-up (for screen negative women). Outcomes included test accuracy and cancer type detected. STUDY SELECTION AND SYNTHESIS: Two reviewers independently assessed articles for inclusion and assessed the methodological quality of included studies using the QUality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. A single reviewer extracted data, which were checked by a second reviewer. Narrative data synthesis was performed. RESULTS: Twelve studies totalling 131 822 screened women were included. No prospective studies measuring test accuracy of AI in screening practice were found. Studies were of poor methodological quality. Three retrospective studies compared AI systems with the clinical decisions of the original radiologist, including 79 910 women, of whom 1878 had screen detected cancer or interval cancer within 12 months of screening. Thirty four (94%) of 36 AI systems evaluated in these studies were le...