Deep Learning-based Model for Breast Implant Classification in Ultrasonography: A Multi-Institutional Model Development and Validation Study.
Authors
Affiliations (3)
Affiliations (3)
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Plastic and Reconstructive Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Abstract
The increasing prevalence of implant-based breast surgeries highlights a critical gap in patient knowledge regarding implant information, exacerbated by inadequate record-keeping and emerging safety concerns. This study addresses the need for reliable implant identification methods by developing a deep learning model capable of classifying breast implants using ultrasound images. Retrospective data of 28,712 breast ultrasound PNG files from 4,136 breast implants in 2,580 patients obtained from multiple institutions were utilized to train and validate this model. Our findings demonstrate that the deep learning model achieved high diagnostic accuracy, with a balanced accuracy of 0.893 for manufacturer classification and 0.971 for implant texture classification in external test datasets. The model's performance was enhanced by employing Grad-CAM for interpretability. By automating the identification process, this tool alleviates the reliance on specialized training among plastic surgeons regarding breast ultrasound, streamlining patient care. Despite limitations, the model shows promise for improving clinical workflows and patient outcomes.