Open AccessA Review of Applications of Machine Learning in Mammography and Future Challenges
Author(s) -
Sai Batchu,
Fan Liu,
Ahmad Amireh,
Joseph Waller,
Muhammad Umair
Publication year - 2021
Publication title -
oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.987
H-Index - 98
eISSN - 1423-0232
pISSN - 0030-2414
DOI - 10.1159/000515698
Subject(s) - context (archaeology) , mammography , medicine , machine learning , false positive paradox , artificial intelligence , breast cancer , scopus , medical physics , medline , cancer , computer science , law , biology , paleontology , political science
Background: The aim of this study is to systematically review the literature to summarize the evidence surrounding the clinical utility of artificial intelligence (AI) in the field of mammography. Databases from PubMed, IEEE Xplore, and Scopus were searched for relevant literature. Studies evaluating AI models in the context of prediction and diagnosis of breast malignancies that also reported conventional performance metrics were deemed suitable for inclusion. From 90 unique citations, 21 studies were considered suitable for our examination. Data was not pooled due to heterogeneity in study evaluation methods. Summary: Three studies showed the applicability of AI in reducing workload. Six studies demonstrated that AI can aid in diagnosis, with up to 69% reduction in false positives and an increase in sensitivity ranging from 84 to 91%. Five studies show how AI models can independently mark and classify suspicious findings on conventional scans, with abilities comparable with radiologists. Seven studies examined AI predictive potential for breast cancer and risk score calculation. Key Messages: Despite limitations in the current evidence base and technical obstacles, this review suggests AI has marked potential for extensive use in mammography. Additional works, including large-scale prospective studies, are warranted to elucidate the clinical utility of AI.