DePerio: Deep Learning‐Based Oral Inflammatory Load Quantification for Periodontal Applications
Bibliographic record
Abstract
Periodontal disease (PD) is a chronic condition associated with systemic risks like cardiovascular disease and diabetes. Traditional diagnostics detect advanced PD but often miss early‐stage cases, where timely intervention is critical. Oral polymorphonuclear neutrophils (oPMNs) are emerging as key biomarkers for periodontal health. This study presents DePerio, an AI‐driven deep neural network (DNN) method that isolates and quantifies oPMNs from saliva using their natural hydrophilic adhesion on treated surfaces. Trained on thousands of annotated bright‐field images, DePerio accurately detects and counts oPMNs within milliseconds. Validation against standard techniques confirms its precision in measuring oral inflammatory load (OIL). Clinical testing on 51 samples from healthy and periodontitis patients demonstrates DePerio's capability to distinguish five OIL levels, assisting in PD severity assessment. This low‐complexity, AI‐powered tool offers a rapid, reliable approach for early PD detection and management in dental practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".