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TRUVACE RECORD VERSION
record: TRV-2026-0908
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-27T06:06:21.629729Z
status: published
lens: trace
sector: health
headline: International Application of Artificial Intelligence for Lesion Detection on Digital Breast Tomosynthesis: Comparing Western and Eastern Databases
dek: Rationale and objectives The international application of artificial intelligence (AI) for lesion detection based on digital breast tomosynthesis (DBT) is limited due to disease variations among populations. We hypothesized that lesion detection models trained on either the Western or Eastern DBT dataset would exhibit reduced performance on another dataset. We proposed transfer learning to enhance lesion detection across DBT databases. Materials and methods The Western database (94 patients) was obtained from th…
gain_title: Transfer learning enhanced AI lesion detection performance when models were adapted from one regional DBT database to another.
problem_title: AI lesion detection models trained on Western or Eastern DBT data showed reduced performance when applied to the other population due to differences in lesion types.
trace_subject: AI lesion detection on digital breast tomosynthesis across Western and Eastern databases
gain_reading: Transfer learning enhanced AI lesion detection performance when models were adapted from one regional DBT database to another.
gain_evidence: The applied transfer learning effectively enhanced the model performance in the target dataset | could benefit the international application of DBT lesion detection
problem_reading: AI lesion detection models trained on Western or Eastern DBT data showed reduced performance when applied to the other population due to differences in lesion types.
problem_evidence: lesion detection models trained on either the Western or Eastern DBT dataset would exhibit reduced performance on another dataset | The difference in DBT lesion types between Western and Eastern databases significantly impacted model performance
quick_read: A study tested YOLO-based AI models for breast lesion detection on digital breast tomosynthesis using a 94-patient Western database from the Cancer Imaging Archive and a 157-patient Eastern database from a single medical center, with lesions grouped into six types.

The work matters because population differences in lesion types reduced cross-dataset performance, raising questions about international deployment of DBT AI, while transfer learning showed promise for adaptation; uncertainty remains about performance beyond these two limited datasets and six lesion categories.
limitation: Findings are based on small samples of 94 and 157 patients with Eastern data from a single anonymous center, limiting broad international generalizability.
tag: Dual reading
key_points: Study compared Western database of 94 patients from Cancer Imaging Archive and Eastern database of 157 patients from one anonymous medical center. | All breast lesions were stratified into six types and evaluated with YOLO lesion detection models. | Performance was measured using Intersection over Union (IoU), sensitivity, and precision to assess generality across databases.
rundown: Researchers trained YOLO lesion detection models on either the Western or Eastern DBT dataset, with and without transfer learning to the other dataset, stratifying lesions into six types.

Evaluation used IoU, sensitivity, and precision, finding significant differences in lesion types between databases that impacted model generality, while transfer learning improved performance in the target dataset.
sources:
- peer_reviewed | Academic Radiology | https://doi.org/10.1016/j.acra.2026.08.039 | 2026-08-25
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