TY - JOUR
T1 - Use of Artificial Intelligence for Digital Breast Tomosynthesis Screening
T2 - A Preliminary Real-world Experience
AU - Letter, Haley
AU - Peratikos, Meridith
AU - Toledano, Alicia
AU - Hoffmeister, Jeffrey
AU - Nishikawa, Robert
AU - Conant, Emily
AU - Shisler, Julie
AU - Maimone, Santo
AU - Diaz De Villegas, Hector
N1 - Publisher Copyright:
© 2023 The Authors.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - Objective: The purpose of this study is to assess the "real-world"impact of an artificial intelligence (AI) tool designed to detect breast cancer in digital breast tomosynthesis (DBT) screening exams following 12 months of utilization in a subspecialized academic breast center. Methods: Following IRB approval, mammography audit reports, as specified in the BI-RADS atlas, were retrospectively generated for five radiologists reading at three locations during a 12-month time frame. One location had the AI tool (iCAD ProFound AI v2.0), and the other two locations did not. The co-primary endpoints were cancer detection rate (CDR) and abnormal interpretation rate (AIR). Secondary endpoints included positive predictive values (PPVs) for cancer among screenings with abnormal interpretations (PPV1) and for biopsies performed (PPV3). Odds ratios (OR) with two-sided 95% confidence intervals (CIs) summarized the impact of AI across radiologists using generalized estimating equations. Results: Nonsignificant differences were observed in CDR, AIR, and PPVs. The CDR was 7.3 with AI and 5.9 without AI (OR 1.3, 95% CI: 0.9-1.7). The AIR was 11.7% with AI and 11.8% without AI (OR 1.0, 95% CI: 0.8-1.3). The PPV1 was 6.2% with AI and 5.0% without AI (OR 1.3, 95% CI: 0.97-1.7). The PPV3 was 33.3% with AI and 32.0% without AI (OR 1.1, 95% CI: 0.8-1.5). Conclusion: Although we are unable to show statistically significant changes in CDR and AIR outcomes in the two groups, the results are consistent with prior reader studies. There is a nonsignificant trend toward improvement in CDR with AI, without significant increases in AIR.
AB - Objective: The purpose of this study is to assess the "real-world"impact of an artificial intelligence (AI) tool designed to detect breast cancer in digital breast tomosynthesis (DBT) screening exams following 12 months of utilization in a subspecialized academic breast center. Methods: Following IRB approval, mammography audit reports, as specified in the BI-RADS atlas, were retrospectively generated for five radiologists reading at three locations during a 12-month time frame. One location had the AI tool (iCAD ProFound AI v2.0), and the other two locations did not. The co-primary endpoints were cancer detection rate (CDR) and abnormal interpretation rate (AIR). Secondary endpoints included positive predictive values (PPVs) for cancer among screenings with abnormal interpretations (PPV1) and for biopsies performed (PPV3). Odds ratios (OR) with two-sided 95% confidence intervals (CIs) summarized the impact of AI across radiologists using generalized estimating equations. Results: Nonsignificant differences were observed in CDR, AIR, and PPVs. The CDR was 7.3 with AI and 5.9 without AI (OR 1.3, 95% CI: 0.9-1.7). The AIR was 11.7% with AI and 11.8% without AI (OR 1.0, 95% CI: 0.8-1.3). The PPV1 was 6.2% with AI and 5.0% without AI (OR 1.3, 95% CI: 0.97-1.7). The PPV3 was 33.3% with AI and 32.0% without AI (OR 1.1, 95% CI: 0.8-1.5). Conclusion: Although we are unable to show statistically significant changes in CDR and AIR outcomes in the two groups, the results are consistent with prior reader studies. There is a nonsignificant trend toward improvement in CDR with AI, without significant increases in AIR.
KW - artificial intelligence
KW - breast cancer screening
KW - digital breast tomosynthesis
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U2 - 10.1093/jbi/wbad015
DO - 10.1093/jbi/wbad015
M3 - Article
AN - SCOPUS:85160711184
SN - 2631-6110
VL - 5
SP - 258
EP - 266
JO - Journal of Breast Imaging
JF - Journal of Breast Imaging
IS - 3
ER -