Low-Cost Active Thermography using Cellphone Infrared Cameras: from Early Detection of Dental Caries to Quantification of THC in Oral Fluid
Bibliographic record
Abstract
Active thermography (AT) is a widely studied non-destructive testing method for the characterization and evaluation of biological and industrial materials. Despite its broad range of potential applications, commercialization and wide-spread adaption of AT has long been impeded by the cost and size of infrared (IR) cameras. In this paper, we demonstrate that this cost and size limitation can be overcome using cell-phone attachment IR cameras. A software development kit (SDK) is developed that controls camera attributes through a simple USB interface and acquires camera frames at a constant frame rate up to 33 fps. To demonstrate the performance of our low-cost AT system, we report and discuss our experimental results on two high impact potential applications. The first set of experiments is conducted on a dental sample to investigate the clinical potential of the developed low-cost technology for detecting early dental caries, while the second set of experiments is conducted on the oral-fluid based lateral flow immunoassay to determine the viability of our technology for detecting and quantifying cannabis consumption at the point-of-care. Our results suggest achievement of reliable performance in the low-cost platform, comparable to those of costly and bulky research-grade systems, paving the way for translation of AT techniques to market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".