Assessment of Gingival Tissue Hemodynamics by Optical Spectroscopy in Diagnosis of Periodontal Disease - Multicenter Clinical Trials
Bibliographic record
Abstract
Background: Currently used diagnostic methods in periodontics are unable to identify disease activity and progression until significant attachment loss has happened. New diagnostic modalities and parameters are needed to monitor disease progression and detect disease activity at an early stage. Aims: To determine the features of the in vivo optical spectra characteristic of periodontitis, gingivitis and healthy gingival tissue and the potential to use these spectral signatures to differentiate periodontitis from gingivitis and healthy gingiva and determine the risk of progression from gingivitis to periodontitis. Materials and Methods: 12 cross-sectional clinical trials were conducted at 6 university based dental clinics or research centers in Canada, China, Italy and Brazil from 2007 to 2014 including 562 patients with moderate to severe chronic periodontitis. Optical spectra were obtained at the chair-side using a custom designed optical probe from 705 periodontitis, 1306 gingivitis and 1691 healthy sites in situ. A modified Beer-Lambert unmixing model was used to determine tissue oxygen saturation and relative contribution of oxygenated and deoxygenated hemoglobin components. Results: Multiple hemodynamic parameters were simultaneously derived from the optical spectra of gingival tissue including tissue oxygen saturation, oxygenated hemoglobin, deoxygenated hemoglobin and total hemoglobin indices. The tissue oxygen saturation and oxygenated hemoglobin index in periodontitis was significantly lower than gingivitis and healthy gingiva (p < 0.0001) but no significant difference in oxygenated hemoglobin between gingivitis and healthy gingiva (p > 0.05). On the other hand, deoxygenated hemoglobin in periodontitis was significantly increased compared to gingivitis and healthy gingiva (p < 0.0001 ). A classification model was established to predict the risk level of gingivitis based on the features of the optical spectra of characteristic of health gum and periodontitis. Conclusions: 1) multiple local hemodynamic profiles such as tissue oxygenation and perfusion can be simultaneously generated by optical spectroscopy to reflect subclinical gingival inflammation. 2) Decreased tissue oxygenation saturation in periodontitis and gingivitis was mainly due to increased concentration of deoxygenated hemoglobin. 3) Optical spectroscopy has the potential to diagnose periodontal disease and monitor disease progression at an early stage. More longitudinal studies are needed to validate this potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".