Digital Imaging Capability for Caries Detection
Bibliographic record
Abstract
The objective of this study was to identify the diagnostic capability of photostimulable phosphor plates (PSPs) and direct digital sensors (DDSs) in the detection of interproximal caries. Studies were identified that evaluated the diagnostic capability of PSPs and DDSs in detecting interproximal caries in human teeth, in both dentin and enamel. Histologic sections were the gold standard. This systematic review searched several electronic databases. In addition, Google Scholar and reference lists of the finally included studies were screened. QUADAS-2 was applied to evaluate the risk of bias among included studies. Six studies were finally included; 4 of which were considered homogeneous enough to conduct a meta-analysis. The meta-analysis evaluated 668 interproximal human tooth surfaces. All studies used extracted human teeth ranging from no caries present to caries into dentin. Each tooth was radiographed by both PSP and DDS technologies and then submitted for histologic analysis as the gold standard. Meta-analysis showed that intraoral digital imaging is of high specificity but low sensitivity in the detection of interproximal caries. The sensitivity and specificity for different studies with PSPs varied substantially from 15% to 54% and from 84% to 100%, respectively. Direct sensor analysis sensitivity and specificity ranged from 16% to 56% and from 90% to 100%, respectively. Newer PSP and DDS technologies had statistically significant higher sensitivities, yet the differences in diagnostic capabilities between the older and newer technologies were clinically insignificant. Both digital systems were excellent in identifying surfaces without caries (specificity) but were not sensitive enough to reliably identify interproximal surfaces with caries. Clinicians must therefore remain vigilant in performing a careful clinical examination and other diagnostic tests rather than relying solely on radiographic imaging to diagnose interproximal caries. Knowledge Transfer Statement: This study will help clinicians make an evidence-based decision when deciding which digital radiography system to use when evaluating interproximal caries. Time, patient radiation safety, cost, and image quality are factors to be considered. The performance of the different available digital imaging systems was compared with the current gold standard-a histologic analysis-via meta-analysis.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".