Feasibility of an AI-driven Classification of Tuberous Breast Deformity: A Siamese Network Approach with a Continuous Tuberosity Score.

Authors

Vaccari S,Paderno A,Furlan S,Cavallero MF,Lupacchini AM,Di Giuli R,Klinger M,Klinger F,Vinci V

Affiliations (4)

  • Department of Medical Biotechnology and Translational Medicine BIOMETRA, Plastic Reconstructive and Aesthetic Plastic Surgery School, Università degli Studi Di Milano, Milan, Italy.
  • Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy.
  • IRCCS Humanitas Research Hospital, Milan, Italy.
  • Department of Health Sciences, Ospedale San Paolo, University of Milan, Milan, Italy.

Abstract

Tuberous breast deformity (TBD) is a congenital condition characterized by constriction of the breast base, parenchymal hypoplasia, and areolar herniation. The absence of a universally accepted classification system complicates diagnosis and surgical planning, leading to variability in clinical outcomes. Artificial intelligence (AI) has emerged as a powerful adjunct in medical imaging, enabling objective, reproducible, and data-driven diagnostic assessments. This study introduces an AI-driven diagnostic tool for tuberous breast deformity (TBD) classification using a Siamese Network trained on paired frontal and lateral images. Additionally, the model generates a continuous Tuberosity Score (ranging from 0 to 1) based on embedding vector distances, offering an objective measure to enhance surgical planning and improved clinical outcomes. A dataset of 200 expertly classified frontal and lateral breast images (100 tuberous, 100 non-tuberous) was used to train a Siamese Network with contrastive loss. The model extracted high-dimensional feature embeddings to differentiate tuberous from non-tuberous breasts. Five-fold cross-validation ensured robust performance evaluation. Performance metrics included accuracy, precision, recall, and F1-score. Visualization techniques, such as t-SNE clustering and occlusion sensitivity mapping, were employed to interpret model decisions. The model achieved an average accuracy of 96.2% ± 5.5%, with balanced precision and recall. The Tuberosity Score, derived from the Euclidean distance between embeddings, provided a continuous measure of deformity severity, correlating well with clinical assessments. This AI-based framework offers an objective, high-accuracy classification system for TBD. The Tuberosity Score enhances diagnostic precision, potentially aiding in surgical planning and improving patient outcomes.

Topics

Journal Article

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.