Detection of early occlusal and proximal dental caries using long-wavelength infrared thermophotonic lock-in imaging
Bibliographic record
Abstract
Detection of early dental caries, as one of the most prevalent oral diseases worldwide, has always been a major challenge in dental practice. Conventionally, the dental standard of care relies on inspection methods such as visual/tactile assessment and X-ray radiography which lack sufficient specificity and sensitivity to detect caries at early stages of progression. This increases the risk of cavity formation, and consequently, the need for treatment through surgical intervention. However, over the past few years, dentistry has begun to shift away from costly and labor-intensive surgical treatment towards nonsurgical management and prevention which relies on early detection of caries when they can be healed (i.e., remineralized). In accordance with this paradigm shift, in this study, we demonstrate the capabilities of a clinically and commercially viable long-wavelength infrared (LWIR) thermophotonic imaging technology in detection of very early proximal and occlusal caries. The system incorporates intensity-modulated light and detects the early caries based on the altered thermal-wave field, caused due to greater light absorption at caries sites, which reaches the IR camera though infrared emission. A low-cost LWIR (8-14 μm) camera has been integrated into the thermophotonic imaging system. The IR camera used in this study consists of an uncooled microbolometer LWIR detector which is highly suitable for implementation in clinically viable imaging systems due to its reduced overall weight and cost in comparison to the conventional cryogenically cooled detectors (e.g., mid-wavelength infrared (MWIR: 3-5 μm) detectors). In addition to low weight and affordability, LWIR detectors have improved detection abilities as the masking effect of the direct thermal emission from subsurface caries is suppressed due to the minimal transmittance of enamel in the LWIR band. In order to simulate the inception and progression of early caries into the enamel, a controlled demineralization procedure is followed in this study. This method effectively mimics the process of mineral loss in enamel and generates a caries lesion below an intact layer as commonly encountered in mineral profile of dental caries. The artificial caries are formed on proximal and occlusal surfaces of the tooth samples as the most prone surfaces to mineral loss and caries formation. The capabilities of the imaging system are evaluated by monitoring the progress of caries formation though a follow-up study within a 10-day timeframe. The results show the ability of the system to detect very early caries, demonstrating its suitability as a sensitive and clinically viable dental diagnostic system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".