AI-Assisted 3D Chest Reconstruction for Personalized Gynecomastia Surgical Planning: A Digital Twin Framework for Precision Aesthetic Surgery

Abstract

Gynecomastia surgery is largely dependent on subjective clinical evaluation and two-dimensional imaging, which limits the reproducibility and precision of preoperative planning. This variability in anatomical interpretation contributes to inconsistent aesthetic outcomes and highlights the need for more objective, patient-specific planning tools. This study proposes an artificial intelligence (AI)-assisted three-dimensional (3D) reconstruction framework for personalised surgical planning in gynecomastia correction. The proposed framework integrates deep learning-based semantic segmentation with volumetric 3D reconstruction techniques to generate high-resolution, patient-specific digital chest models. These digital twins enable detailed preoperative simulation of tissue distribution, glandular excision planning, and bilateral symmetry assessment. The system is designed as a clinical decision-support tool aimed at enhancing surgical planning accuracy and standardisation rather than replacing clinician judgment. From a methodological perspective, the framework supports structured anatomical visualisation and quantitative assessment of chest morphology, enabling improved spatial understanding of deformity characteristics. Compared to conventional 2D-based assessment, the approach facilitates more consistent interpretation of anatomical variability and improves the robustness of preoperative decision-making. However, the translation of such systems into clinical practice remains constrained by imaging variability, dataset limitations, and the absence of large-scale prospective validation studies. Future research should focus on multi-centre clinical validation, integration with intraoperative guidance systems, and evaluation of patient-reported and surgeon-reported outcome measures.Overall, the study demonstrates that AI-driven 3D reconstruction has strong potential to advance precision, reproducibility, and standardisation in gynecomastia surgical planning within the emerging paradigm of digital and personalised surgical medicine.