Utility of Diagnostic Mammography as the Primary Imaging Modality for Palpable Lumps in Women With Almost Entirely Fatty Breasts

Linden, Olivia E.; Hayward, Jessica H.; Price, Elissa R.; Kelil, Tatiana; Joe, Bonnie N.; Lee, Amie Y.

doi:10.2214/ajr.19.21760

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…However, the rate of false-negative results is still relatively low, reported in 1.0 to 1.5 per 1,000 women [11]. Although its accuracy continues to improve with technical improvements, diagnostic mammography is the gold standard for evaluation of breast cancer [12]. In order to further increase the accuracy and to further reduce the rates of false positives and false negatives, recent advances in artificial intelligence (AI) have been exploited to develop software capable of aiding radiologists in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

A Review of Applications of Machine Learning in Mammography and Future Challenges

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%