ArticleJournal of dental research2024
Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network.
Article in Journal of dental research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiographic choice for skeletal maturation assessment: a systematic review of Cervical Vertebrae Method and Middle Phalanx Method.Oral radiology · 2026Pooled it
- Diagnostic performance of artificial intelligence for facial fracture detection: a systematic review.Oral radiology · 2026Pooled it
- Evaluation of Artificial Intelligence-based diagnosis for facial fractures, advantages compared with conventional imaging diagnosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- Evaluation of an AI-based segmentation model for mandibular fracture segmentation on CBCT using volumetric and 2D slice-based validation approaches.BMC oral health · 2026Article
- PSCA-YOLO: An enhanced YOLO with position-semantics coupled attention for mandibular fracture detection.iScience · 2026Article
- [Artificial intelligence in stomatology: Innovations in clinical practice, research, education, and healthcare management].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025Review
- Advanced Imaging in Dental Research: From Gene Mapping to AI Global Data.Journal of dental research · 2024Article
- Diagnostic Utility of the "Air Sign" as a Radiological Indicator for Mandibular Body and Angle Fractures.Journal of clinical medicine · 2024Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
After nasal bone fractures, fractures of the mandible are the most frequently encountered injuries of the facial skeleton. Accurate identification of fracture locations is critical for effectively managing these injuries. To address this need, JawFracNet, an innovative artificial intelligence method, has been developed to enable automated detection of mandibular fractures in cone-beam computed tomography (CBCT) scans. JawFracNet employs a 3-stage neural network model that processes 3-dimensional patches from a CBCT scan. Stage 1 predicts a segmentation mask of the mandible in a patch, which is subsequently used in stage 2 to predict a segmentation of the fractures and in stage 3 to classify whether the patch contains any fracture. The final output of JawFracNet is the fracture segmentation of the entire scan, obtained by aggregating and unifying voxel-level and patch-level predictions. A total of 164 CBCT scans without mandibular fractures and 171 CBCT scans with mandibular fractures were included in this study. Evaluation of JawFracNet demonstrated a precision of 0.978 and a sensitivity of 0.956 in detecting mandibular fractures. The current study proposes the first benchmark for mandibular fracture detection in CBCT scans. Straightforward replication is promoted by publicly sharing the code and providing access to JawFracNet on grand-challenge.org.
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