Innovations in gender affirmation: AI-enhanced surgical guides for mandibular facial feminization surgery.
Authors
Affiliations (7)
Affiliations (7)
- Department of Oral and Cranio-Maxillofacial Surgery and 3D Print Lab, University Hospital Basel, Basel, Switzerland. [email protected].
- Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland. [email protected].
- Department of Oral and Cranio-Maxillofacial Surgery and 3D Print Lab, University Hospital Basel, Basel, Switzerland.
- Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
- Department of Prosthetic Dentistry and Dental Materials, Iuliu Hatieganu University of Medicine and Pharmacy, 32 Clinicilor Street, Cluj-Napoca, 400006, Romania.
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, the Netherlands.
- Department of Oral and Maxillofacial Surgery, Lucerne Cantonal Hospital, Spitalstrasse, Lucerne, 6000, Switzerland.
Abstract
This study presents a fully automated digital workflow using artificial intelligence (AI) to create patient-specific cutting guides for mandible-angle osteotomies in facial feminization surgery (FFS). The goal is to achieve predictable, accurate, and safe results with minimal user input, addressing the time and effort required for conventional guide creation. Three-dimensional CT images of 30 male patients were used to develop and validate a workflow that automates two key processes: (1) segmentation of the mandible using a convolutional neural network (3D U-Net architecture) and (2) virtual design of osteotomy-specific cutting guides. Segmentation accuracy was assessed through comparison with expert manual segmentations using the dice similarity coefficient (DSC) and mean surface distance (MSD). The precision of the cutting guides was evaluated based on osteotomy line accuracy and fit. Workflow efficiency was measured by comparing the time required for automated versus manual planning by expert and novice users. The AI-based workflow achieved a median DSC of 0.966 and a median MSD of 0.212 mm, demonstrating high accuracy. The median planning time was reduced to 1 min and 38 s with the automated system, compared to 19 min and 37 s for an expert and 26 min and 39 s for a novice, representing 10- and 16-fold time reductions, respectively. The AI-based workflow is accurate, efficient, and cost-effective, significantly reducing planning time while maintaining clinical precision. This workflow improves surgical outcomes with precise and reliable cutting guides, enhancing efficiency and accessibility for clinicians, including those with limited experience in designing cutting guides.