Multi-class Identification and Quantification of Breast Implant-related Complications in Ultrasonography Using a...

Breast ultrasonography (US) is cost-effective for detecting breast implant-related complications, but its reliability remains highly operator-dependent.

This study developed and validated a deep learning model for the automated multi-class identification and quantitative evaluation of breast implant complications (rupture, capsular contracture, and hematoma).

We retrospectively analyzed 14,679 breast US images across 1,022 breast implant sequences from 908 patients. The proposed architecture integrates multiscale visual encoding, automated probe position mapping, and a pathology-informed mask graph layer with a Gradient Reversal Layer for lumen adversarial debiasing. Four convolutional backbones were benchmarked using an independent patient-level test dataset (N=197 implants).

The optimized DenseNet-based framework achieved 96.97% balanced accuracy with expert-guided spatial priors and 88.67% under autonomous baseline conditions. The model yielded 100% sensitivity for normal, rupture, and hematoma cohorts, and 87.88% sensitivity for capsular contracture; minor false-negatives were traced to morphological heterogeneity and coexisting pathologies. Furthermore, the system extracted quantitative morphometric profiles, yielding a mean relative capsule thickness of 2.4±0.4% for capsular contractures and a mean relative fluid area of 13.9±6.4% for hematomas.

The proposed multiscale model delivers a comprehensive, clinical-grade output, comprising accurate complication classification, five-zone spatial localization, and objective morphometric metrics, while mitigating operator dependency. This system holds strong potential as an accessible, high-performance decision support tool for long-term safety screening of patients with breast implants.