02/06/2026
As implant-based breast surgery continues to rise, gaps in implant documentation and patient awareness present ongoing clinical and safety challenges in plastic and aesthetic surgery. 🩺
This retrospective, multi-institutional study evaluated a deep learning model trained on 28,712 breast ultrasound images from over 4,100 implants to automate implant identification. The model demonstrated strong external validity, achieving a balanced accuracy of 0.893 for manufacturer classification and 0.971 for implant texture classification. 📊
Importantly, Gradient-Weighted Class Activation Mapping (Grad-CAM) was used to enhance interpretability, offering clinicians visual insight into how the model makes its predictions. By reducing dependence on specialized ultrasound training, this approach has the potential to streamline clinical workflows, improve implant tracking, and enhance patient care—particularly in revision surgery and long-term surveillance.
While further validation is needed, these findings highlight how AI-driven imaging tools may meaningfully support plastic surgeons and improve outcomes in breast surgery.
🔗Read the full paper here: https://doi.org/10.1093/asj/sjaf220
