ArticleBioengineering (Basel, Switzerland)2023
Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparisons of artificial intelligence automated segmentation techniques to manual segmentation techniques of the maxilla and maxillary sinus for CT or cone-beam CT scans-a systematic review.Dento maxillo facial radiology · 2025Pooled it
- Automated localization of mandibular landmarks in the construction of mandibular median sagittal plane.European journal of medical research · 2024Trial
- Changes in Orbital Volume following Reconstruction with Alloplastic Materials in Patients with Orbital Trauma.Journal of dentistry (Shiraz, Iran) · 2026Article
- Artificial Intelligence-enabled Automatic Computed Tomography Segmentation for Craniomaxillofacial Surgery Using a Real Surgical Database.Plastic and reconstructive surgery. Global open · 2026Article
- Diagnostic Support in Dentistry Through Artificial Intelligence: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- Fully Automated AI-Based Digital Workflow for Mirroring of Healthy and Defective Craniofacial Models.Journal of imaging · 2025Article
- Segmenting beyond the imaging data: creation of anatomically valid edentulous mandibular geometries for surgical planning using artificial intelligence.Clinical oral investigations · 2025Article
- Innovations in gender affirmation: AI-enhanced surgical guides for mandibular facial feminization surgery.Clinical oral investigations · 2025Article
- The Contribution of Real-Time Artificial Intelligence Segmentation in Maxillofacial Trauma Emergencies.Diagnostics (Basel, Switzerland) · 2025Article
- BounTI (boundary-preserving threshold iteration): A user-friendly tool for automatic hard tissue segmentation.Journal of anatomy · 2024Article
- Design and evaluation of a deep learning-based automatic segmentation of maxillary and mandibular substructures using a 3D U-Net.Clinical and translational radiation oncology · 2024Article
- Realizing in-house algorithm-driven free fibula flap set up within 24 hours: a pilot study evaluating accuracy with open-source tools.Frontiers in surgery · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical image segmentation, whether semi-automatically or manually, is labor-intensive, subjective, and needs specialized personnel. The fully automated segmentation process recently gained importance due to its better design and understanding of CNNs. Considering this, we decided to develop our in-house segmentation software and compare it to the systems of established companies, an inexperienced user, and an expert as ground truth. The companies included in the study have a cloud-based option that performs accurately in clinical routine (dice similarity coefficient of 0.912 to 0.949) with an average segmentation time ranging from 3'54″ to 85'54″. Our in-house model achieved an accuracy of 94.24% compared to the best-performing software and had the shortest mean segmentation time of 2'03″. During the study, developing in-house segmentation software gave us a glimpse into the strenuous work that companies face when offering clinically relevant solutions. All the problems encountered were discussed with the companies and solved, so both parties benefited from this experience. In doing so, we demonstrated that fully automated segmentation needs further research and collaboration between academics and the private sector to achieve full acceptance in clinical routines.
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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.