Deep learning for automatic segmentation of bone sequestra on orthopantomographs: a UNet-Based approach
NCT ID NCT07809048
First seen Sep 09, 2026 · Last updated Sep 09, 2026
Summary
This study developed and evaluated a UNet-based deep learning model for automatic segmentation of bone sequestra on orthopantomographs (OPGs), with the goal of improving diagnostic efficiency and reducing inter-observer variability in osteomyelitis, osteoradionecrosis, and medication-related osteonecrosis of the jaw. A total of 120 anonymized OPGs with 134 annotated sequestra were used for training. Images were preprocessed with min-max normalization and augmented to enhance robustness. Manual expert annotations served as ground truth. A UNet architecture, optimized with Dice loss and the AdamW optimizer, was trained for 300 epochs. Performance was assessed using Dice coefficient, Intersection over Union (IoU), precision, recall, F1-score, and accuracy. ROC analysis and confusion matrix evaluations were performed. Agreement with clinicians was quantified using the Intraclass Correlation Coefficient (ICC). The model achieved a best-checkpoint validation Dice of 0.79 and IoU of 0.93. On the test set, performance included a Dice of 0.79, IoU of 0.74, recall of 0.81, precision of 0.81, and F1-score of 0.81. ROC analysis showed balanced discriminative performance with an AUC of 0.76. The confusion matrix indicated strong lesion/background classification, with minor under-segmentation. ICC analysis showed excellent agreement with an experienced surgeon (ICC = 0.85), though lower with a less experienced dentist (ICC = 0.57). The proposed UNet-based model enables efficient segmentation of sequestra, reducing annotation time by \>90% while maintaining diagnostic accuracy. The model highlights the clinical utility of AI-assisted decision support in maxillofacial radiology.
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Study facts
What this study's own registry entry says, in plain language.
- Participants
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120 people
The number who actually took part.
- Started
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Mar 2024
- Finished
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Mar 2025
- Lead sponsor
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Other sponsor
The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.
Who can take part
This study's own entry requirements. Only the study team can say for certain whether you qualify.
Who is studied
Patients will be selected among the patients with osteomyelitis with sequestrum
- Ages
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18 to 99 years
- Sex
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Anyone
- Healthy volunteers
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Not accepted
This study is not open to healthy volunteers. The entry requirements below say who it is open to.
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Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.
Inclusion Criteria: * Patient with exposed bone over 8 weeks with a history of bisphosphonates or antiangiogenic drugs, * Patient with a history of previous radiotherapy, * Patients with necrotic exposed bone with or without a history of trauma * Patients with osteomyelitis. Exclusion Criteria: * Patients with no clear vision of the borders of the sequestra, * Panoramic images with artifacts in and around the lesion.
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Conditions
The condition(s) this trial relates to.
As listed by the trial registrant
The condition terms exactly as the trial's registrant entered them.
Contacts and locations
Locations
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Izmir Katip Celebi University
Izmir, Çiğli, 35640, Turkey (Türkiye)