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An AI-based Mammography Screening Protocol: Outcome and Radiologists Workload

Transpara could detect normal, moderate-risk, and suspicious mammograms in a breast cancer screening program, which may reduce the radiologist workload.

Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program

The proportion of screen-detected cancers not selected by Transpara at three evaluated thresholds was less than 20%. According to cancer

Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms

The combined assessment of the Transpara score and breast density measurements enabled identification of a larger proportion of women who

Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation

Transpara could replace radiologists’ readings in breast screening, achieving a noninferior sensitivity, with a lower recall rate for digital mammography

Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance

Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis

Conclusion: Using a single-view digital breast tomosynthesis (DBT) and artificial intelligence setup could allow for a more effective screening program