Assessment of the quality of root canal fillings—an <i>ex vivo</i> comparison of CBCT scans, conventional intraoral sensors, and a novel photon-counting sensor
Bibliographic record
Abstract
OBJECTIVES: To compare a novel photon-counting sensor, 2 CBCT protocols and 2 CMOS sensors on the detection of gaps between a gutta-percha cone and root canal walls. METHODS: Twenty-five mandibular incisors were prepared to 45/0.04 (size/taper) at working length. Teeth were placed in a partially dentate mandible and single gutta-percha cones of 7 sizes were placed at length, one at a time, for image acquisition with a photon-counting sensor, 2 CBCT protocols (90 µm3, 120 µm3) and 2 CMOS sensors. Three calibrated observers assessed images for gap presence. Sensitivity, specificity, accuracy, AUC, and agreement with gold standard were determined using ANOVA and Tukey test (P ≤ .05). RESULTS: Photon-counting sensor showed superior sensitivity and accuracy (88.47%, 81.57%), significantly higher than the CBCT protocols (50.70%-56.33%, 45.87%-53.17%). Contrarily, the photon-counting sensor showed the lowest specificity (40.27%), significantly lower than the CBCT protocols (90.27%, 97.23%). CMOS sensors showed sensitivity, specificity, and accuracy between 72.23%-74.53%, not differing from other modalities. All intraoral sensors showed AUC around 82.87%-84.03%, significantly higher than CBCT protocol 120 µm3 (74.07%). The file size was inversely related to gap size and percentual agreement with gold standard. CONCLUSIONS: CMOS sensors showed consistent results, while the photon-counting sensor had the highest sensitivity but lacked specificity. CBCT protocols excelled in specificity but had lower sensitivity. ADVANCES IN KNOWLEDGE: Novel photon-counting sensors and CBCT imaging provided no significant advantage over conventional sensors in assessing gaps as an indicator of quality of root canal filling. Furthermore, smaller gaps were more difficult to detect, regardless of the imaging technique used.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".