Radiographic assessment of post-endodontic filling features on PAN and CBCT: diagnostic agreement of an AI platform against CBCT consensus.
Authors
Affiliations (9)
Affiliations (9)
- Kazimierczak Clinic, Dworcowa 13/u6a, 85-009, Bydgoszcz, Poland.
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland.
- Indpendent Researcher, Bydgoszcz, Poland.
- Faculty of Medicine, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland.
- Jankowscy Dental Practice, Czerwonego Krzyża 24, 68-200, Żary, Poland.
- Faculty of Medicine, Bydgoszcz University of Science and Technology, Kaliskiego 7, 85-796, Bydgoszcz, Poland.
- Department of Interdisciplinary Dentistry, Pomeranian Medical University in Szczecin, 70-111, Szczecin, Poland.
- Kazimierczak Clinic, Dworcowa 13/u6a, 85-009, Bydgoszcz, Poland. [email protected].
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland. [email protected].
Abstract
This retrospective diagnostic accuracy study evaluated the performance of an artificial intelligence (AI) platform (Diagnocat) in assessing endodontic treatment features via panoramic (PAN) and cone-beam computed tomography (CBCT) images from 163 patients. Two experienced observers (a radiologist and a dentist) provided consensus readings on the CBCT images, which served as the reference standard. Because the platform applies modality-specific processing pipelines, analyses were conducted and reported separately for CBCT and PAN. The AI analyzed five treatment variables-adequate obturation, adequate density, overfilling, voids in filling, and short filling-and its performance was compared against the reference standard for both the PAN and the CBCT. Diagnostic accuracy, precision, recall (sensitivity), and F1-scores were calculated. Diagnocat exhibited excellent diagnostic performance on CBCT images, achieving overall accuracy above 94% and perfect (100%) sensitivity for overfilling. On PAN benchmarked against the CBCT reference, performance was lower (accuracies 68.25-84.66%), with limited precision and F1-scores for adequate obturation and adequate density, while sensitivity remained comparatively high for voids and short fillings. These results demonstrate modality-dependent platform performance under the studied acquisition protocols and reference standard.