DePerio: Innovative Deep Learning-based Framework for Periodontal Disease Diagnosis and Severity Evaluation Using Saliva Samples
Bibliographic record
Abstract
Recent research has identified salivary oral polymorphonuclear neutrophils (oPMNs) as reliable cellular biomarkers for monitoring the progression of periodontal disease (PD). While conventional diagnostic tools such as periodontal probing and radiographic imaging are effective at detecting advanced disease stages, they lack the sensitivity required for early diagnosis. oPMNs, derived from circulating white blood cells (WBCs), transmigrate through the oral epithelium and appear in saliva with diverse morphological characteristics. Although deep learning-based WBC quantification has been extensively explored using peripheral blood datasets, the unique morphology of oPMNs necessitates the development of new image datasets and retraining of models tailored to their detection. To address these challenges, we introduce DePerio-an AI-powered diagnostic pipeline that integrates a novel oPMN isolation protocol with deep neural network (DNN) architectures. We evaluate the performance of multiple DNN models to achieve accurate detection and quantification of oPMNs. Validated against standard quantification methods for oPMNs and oral inflammatory load (OIL), DePerio achieved a detection error rate of less than 5%. In a clinical study involving 111 human saliva samples, spanning individuals from healthy to severe periodontitis cases, and successfully classified five distinct OIL levels. This robust and low-complexity platform provides a scalable and practical solution for early PD detection and longitudinal monitoring in clinical dental practice.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".