Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women

Background 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. Methods The subjects were 310 Japanese female outpatients who...

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Bibliographic Details
Published in:Breast cancer (Tokyo, Japan) Japan), 2020-07, Vol.27 (4), p.642-651
Main Authors: Sasaki, Michiro, Tozaki, Mitsuhiro, Rodríguez-Ruiz, Alejandro, Yotsumoto, Daisuke, Ichiki, Yumi, Terawaki, Aiko, Oosako, Shunichi, Sagara, Yasuaki, Sagara, Yoshiaki
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Artificial Intelligence
Breast - diagnostic imaging
Breast cancer
Breast Neoplasms - diagnosis
Cancer
Cancer Research
Comparative analysis
Decision Support Systems, Clinical
Diagnosis
Early Detection of Cancer - methods
Early Detection of Cancer - standards
Female
Humans
Japan
Mammography
Mammography - methods
Mammography - standards
Medicine
Medicine & Public Health
Middle Aged
Oncology
Original Article
Radiographic Image Interpretation, Computer-Assisted - methods
Radiologists - standards
Reference Standards
Retrospective Studies
ROC Curve
Surgery
Surgical Oncology
Women
Young Adult
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Summary:Background 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. Methods 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. Results 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  
ISSN:1340-6868
1880-4233
DOI:10.1007/s12282-020-01061-8