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International Application of Artificial Intelligence for Lesion Detection on Digital Breast Tomosynthesis: Comparing
Yun-Xuan Tang1, Bing-Fong Lin2, Yi-Hsien Lin1
1Department of Radiology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan.
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 the Cancer Imaging Archive Breast Cancer Screening DBT, and the Eastern database (157 patients) was collected from one anonymous medical center. All breast lesions were stratified into six types. The YOLO (You Only Look Once) lesion detection models were trained using one of the databases with/without transfer learning to another dataset. Intersection over Union (IoU), sensitivity, and precision were used to evaluate model performance and generality.
Results:
Significant differences were observed in lesion types (P < 0.001) between the two databases, with calcification more prevalent in the Eastern database (60%). A significant reduction in lesion detection performance (P < 0.001) was observed when applying detection models to another dataset. After transfer learning, the detection models showed significant improvements in detection performance on the target datasets (P < 0.001), with the Eastern-to-Western model achieving an IoU of 0.53 ± 0.23, sensitivity of 0.90 ± 0.05, and precision of 0.93 ± 0.04 on the Western target dataset, and the Western-to-Eastern model achieving an IoU of 0.56 ± 0.22, sensitivity of 0.89 ± 0.05, and precision of 0.87 ± 0.05 on the Eastern target dataset.
Conclusion:
The difference in DBT lesion types between Western and Eastern databases significantly impacted model performance. The applied transfer learning effectively enhanced the model performance in the target dataset, which could benefit the international application of DBT lesion detection.