To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women.
The subjects were 310 Japanese female outpatients who underwent digital mammographic examinations between January 2018 and October 2018. A panel of three HR provided a Breast Imaging Reporting and Data System (BI-RADS) score, and Transpara system provided an interactive decision support score and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared under each of reading conditions.
The AUC was higher for human readers than with stand-alone Transpara system (human readers 0.816; Transpara system 0.706; difference 0.11; P < 0.001). The sensitivity of the unaided HR for diagnosis was 89% and specificity was 86%. The sensitivity of stand-alone Transpara system for cutoff scores of 4 and 7 were 93% and 85%, and specificities were 45% and 67%, respectively.
Although the diagnostic performance of Transpara system was statistically lower than that of HR, the recent advances in AI algorithms are expected to reduce the difference between computers and human experts in detecting breast cancer.
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The author would like to thank Nico Karssemeijer, ScreenPoint Medical BV, Nijmegen, The Netherlands, for editing a draft of this manuscript.
Conflict of interest
Alejandro Rodríguez-Ruiz is employee of ScreenPoint Medical BV, who provided crucial technical support with the AI system but is not involved in data acquisition and analysis. The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Sasaki, M., Tozaki, M., Rodríguez-Ruiz, A. et al. Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women. Breast Cancer (2020). https://doi.org/10.1007/s12282-020-01061-8
- Breast cancer
- Digital mammograms
- Artificial intelligence (AI)
- An interactive decision support score and an examination-based cancer likelihood score