A Deep Learning Model to Triage Screening Mammograms: A Simulation                     Study

Yala, Adam; Schuster, Tal; Miles, Randy C.; Barzilay, Regina; Lehman, Constance

doi:10.1148/radiol.2019182908

Cited by 140 publications

(98 citation statements)

References 23 publications

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“…We have to keep in mind that the results are point estimates with mostly broad confidence intervals; the percentage of missed cancers may be as few as 3% and as many as 18%. The magnitude of normal exams identified in this study was similar to the results presented by Rodriguez-Ruiz et al using the same AI system, but on a study population with both clinical and screening mammography exams [25], and by Yala et al using a different AI system than the one used in this study, on a large screening data set [26].…”

Section: Discussionsupporting

confidence: 88%

Identifying normal mammograms in a large screening population using artificial intelligence

et al. 2020

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Objectives To evaluate the potential of artificial intelligence (AI) to identify normal mammograms in a screening population. Methods In this retrospective study, 9581 double-read mammography screening exams including 68 screen-detected cancers and 187 false positives, a subcohort of the prospective population-based Malmö Breast Tomosynthesis Screening Trial, were analysed with a deep learning-based AI system. The AI system categorises mammograms with a cancer risk score increasing from 1 to 10. The effect on cancer detection and false positives of excluding mammograms below different AI risk thresholds from reading by radiologists was investigated. A panel of three breast radiologists assessed the radiographic appearance, type, and visibility of screen-detected cancers assigned low-risk scores (≤ 5). The reduction of normal exams, cancers, and false positives for the different thresholds was presented with 95% confidence intervals (CI). Results If mammograms scored 1 and 2 were excluded from screen-reading, 1829 (19.1%; 95% CI 18.3-19.9) exams could be removed, including 10 (5.3%; 95% CI 2.1-8.6) false positives but no cancers. In total, 5082 (53.0%; 95% CI 52.0-54.0) exams, including 7 (10.3%; 95% CI 3.1-17.5) cancers and 52 (27.8%; 95% CI 21.4-34.2) false positives, had low-risk scores. All, except one, of the seven screen-detected cancers with low-risk scores were judged to be clearly visible. Conclusions The evaluated AI system can correctly identify a proportion of a screening population as cancer-free and also reduce false positives. Thus, AI has the potential to improve mammography screening efficiency. Key Points • Retrospective study showed that AI can identify a proportion of mammograms as normal in a screening population. • Excluding normal exams from screening using AI can reduce false positives.

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Section: Discussionsupporting

confidence: 88%

Identifying normal mammograms in a large screening population using artificial intelligence

et al. 2020

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“…Machine learning and deep learning algorithms have been developed to improve workflows in radiology or to assist the radiologist by automating tasks such as lesion detection or medical imaging quantification. Workflow improvements include prioritizing worklists for radiologists (2,3), triaging screening mammograms (4), reducing or eliminating gadolinium-based contrast media for MRI (5,6), and reducing the radiation dose of CT imaging by advancing image noise reduction (7)(8)(9). Automatic lesion detection by using machine learning has been applied to many imaging modalities and includes detection of pneumothorax (10,11), intracranial hemorrhage (12), Alzheimer disease (13), and urinary stones (14).…”

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confidence: 99%

Preparing Medical Imaging Data for Machine Learning

Willemink¹,

Koszek²,

Hardell³

et al. 2020

Radiology

554

364

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Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.

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“…Currently, the most common approach being investigated is the automated identification of normal cases that either do not need to be evaluated by radiologists at all, or that could be single read instead of double read (in the common double reading screening scenario in Europe) [ [36] , [37] , [38] , [39] ]. In this light, Rodriguez Ruiz et al., in another study with the same dataset mentioned earlier, evaluated the impact on the overall outcome when the cases with the lowest AI-generated probability-of-malignancy scores are pre-designated as normal and therefore not interpreted by radiologists.…”

Section: Options For Use As Stand-alonementioning

confidence: 99%

Stand-alone artificial intelligence - The future of breast cancer screening?

Sechopoulos

Mann

2020

The Breast

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Although computers have had a role in interpretation of mammograms for at least two decades, their impact on performance has not lived up to expectations. However, in the last five years, the field of medical image analysis has undergone a revolution due to the introduction of deep learning convolutional neural networks – a form of artificial intelligence (AI). Because of their considerably higher performance compared to conventional computer aided detection methods, these AI algorithms have resulted in renewed interest in their potential for interpreting breast images in stand-alone mode. For this, first the actual capability of the algorithms, compared to breast radiologists, needs to be well understood. Although early studies have pointed to the comparable performance between AI systems and breast radiologists in interpreting mammograms, these comparisons have been performed in laboratory conditions with limited, enriched datasets. AI algorithms with performance comparable to breast radiologists could be used in a number of different ways, the most impactful being pre-selection, or triaging, of normal screening mammograms that would not need human interpretation. Initial studies evaluating this proposed use have shown very promising results, with the resulting accuracy of the complete screening process not being affected, but with a significant reduction in workload. There is a need to perform additional studies, especially prospective ones, with large screening data sets, to both gauge the actual stand-alone performance of these new algorithms, and the impact of the different implementation possibilities on screening programs.

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A Deep Learning Model to Triage Screening Mammograms: A Simulation Study

Cited by 140 publications

References 23 publications

Identifying normal mammograms in a large screening population using artificial intelligence

Identifying normal mammograms in a large screening population using artificial intelligence

Preparing Medical Imaging Data for Machine Learning

Stand-alone artificial intelligence - The future of breast cancer screening?

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