Workload reduction
Accuracy
Performance benchmarking

ECR 2022

13 July 2022

Authors

Hamm K, Hellingman D, Kotrini L, Vetter B, Jordan T, Entrup C, Engelke M, Janssen N, Schubotz B

AI-based strategy to reduce the recall rate and consensus meeting workload of double reading in breast cancer screening with digital mammography: a retrospective evaluation

Purpose
To evaluate Transpara when implemented as autonomous artificial intelligence (AI) based triaging strategy in breast cancer screening as compared to independent double reading with consensus.

Materials and Method
A consecutive cohort of 37674 digital mammography screening exams (including 210 screen-detected, 45 interval, and 110 next-round screen-detected (NRSD) cancers) were retrospectively collected from a German screening site. Transpara computed a cancer risk score (from 1 to 10) for each exam. Double reading of all exams was compared with an autonomous AI triaging strategy; no human reading is performed for the least suspicious exams classified by AI (score 1-6), only exams with score 7-10 are double read, and the top 1% most suspicious exams as classified by Transpara are automatically recalled. Cancer detection rate (CDR), recall rate (RR), and consensus workload were evaluated.

Results
Double reading of all exams resulted in a CDR of 5.6/1000, RR of 4.4% (1655/37674), and consensus workload of 4255 exams. A total of 27562 exams (73.2%), including 5 screen detected cancers and 636 false-positive recalls, had a Transpara score of 1-6. Transpara found 5 additional cancers (2 interval cancers and 3 NRSD cancers) and 173 additional false-positive recalls in the 1% most suspicious exams. Autonomous AI triaging based on Transpara would result in a similar CDR (5.6/1000), 28.0% lower RR (down to 3.2%), and 45.1% reduction of consensus workload compared to standard double reading.

Conclusion
Not reading exams with Transpara score 1-6 can reduce radiologists’ RR and workload in screening at the cost of missing some screen-detected cancers. However, recalling the top 1% might compensate this loss in CDR. Transpara-assisted double reading of all exams can potentially lower the RR and increase the CDR, but prospective studies should confirm this.


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