ArticleClinical oral investigations2025
Innovations in gender affirmation: AI-enhanced surgical guides for mandibular facial feminization surgery.
Article in Clinical oral investigations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Mandibular angle osteotomy in Asian patients: A narrative review on techniques, emerging technologies, and personalized surgical strategies.JPRAS open · 2026Review
- Editorial for the Special Issue "Diagnosis and Treatment of Diseases of the Facial Skeleton, Oral Cavity, and Paranasal Sinuses".Medicina (Kaunas, Lithuania) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis 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. MATERIALS AND
methodsThree-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.
resultsThe 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.
conclusionsThe AI-based workflow is accurate, efficient, and cost-effective, significantly reducing planning time while maintaining clinical precision. CLINICAL RELEVANCE: 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.