Deep Learning for Root Canal Filling Quality on Panoramic Radiographs with Detection and Grading Measured Separately
Background/Objectives: The European Society of Endodontology names two filling requirements with a reproducible link to outcome: apical extent and material density. Panoramic radiography shows every treated tooth in one exposure, but systems built on it pool detection with grading into one average precision that hides which of the two has failed. This study graded both axes on panoramic radiographs with detection and discrimination measured separately. Methods: In this retrospective study, two specialist endodontists read 830 panoramic radiographs independently, and a third adjudicated every disagreement. The 1668 root fillings of 806 radiographs were encoded into a binary and an axis-resolved label space, and 160 radiographs were locked away before training. The remaining 646 were evaluated under five-fold cross-validation, with four architectural interventions tested alongside input resolution. Localisation was measured by class-agnostic recall, grading by matched-box accuracy and per-axis F1, and label spaces were compared by paired bootstrap. Results: Refining the label space left detection recall at 92.6% against 93.1%, but cost 11.41 points of matched-box accuracy, 8.20 to 14.46, and 12.16 on the locked test. Asked the same binary question, it fell 1.12 points behind in cross-validation and 3.13 points behind on the locked test, where the binary decision reached 76.25% sensitivity given detection and 68.75% specificity at the best of ten readings, or 71.52% and 68.21% at the development-selected one. At 1024 pixels, no architectural change improved density F1 beyond the 8.2 to 10.8 points this design can detect, and enlarging training from 207 to 517 radiographs gained 11.42, 5.92 to 16.56. Conclusions: Separating detection from grading locates the cost of a finer label space in discrimination, bounds four architectural interventions below the resolution of this design, and supports a binary but not an axis-resolved quality decision as a potential teaching and audit aid, pending external validation beyond this single centre, not a screening test.
Authors
- Faruk Öztekin (ORCID: https://orcid.org/0000-0002-5131-0063)
- Oğuzhan KATAR (ORCID: https://orcid.org/0000-0002-5628-3543)
- Mehmet Eskibağlar (ORCID: https://orcid.org/0000-0003-0183-3824)
- Sümeyye Coşgun Baybars (ORCID: https://orcid.org/0000-0002-4166-3754)
- Furkan Konuş (ORCID: https://orcid.org/0009-0000-1107-4337)
- Mehmet Rasit Atik
- Nurefsan Ozdogan
Institutions
- Fırat University (TR)
- Yozgat Bozok Üniversitesi (TR)
Publication Details
- Journal
- Diagnostics
- Published
- 2026-10-16
- DOI
- https://doi.org/10.3390/diagnostics16203371
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Firat Üniversitesi