Evaluation of Static Thermal and Near-Infrared Hyperspectral Imaging for the Diagnosis of Acute Maxillary Rhinosinusitis
Bibliographic record
Abstract
Although acute maxillary rhinosinusitis may be confidently diagnosed based on a history and physical examination by trained specialists, its diagnosis by primary health workers is less dependable, with a tendency for overdiagnosis, often resulting in inappropriate treatment. It is commonly perceived among the otolaryngology community that a new and objective diagnostic tool would be beneficial, facilitating the widespread and reliable diagnosis of rhinosinusitis. Numerous merits of thermal imaging make it an attractive modality to fulfill this role. Although modern systems possess ample sensitivity to detect small thermal abnormalities that accompany various physiologic conditions, reservations remain over whether a rhinosinusitis-induced thermal response in the overlying tissues is dominant enough to yield reliable diagnostic information in a normal clinical setting. Hence, a small preliminary study was conducted with the objective of testing the hypothesis that acute maxillary rhinosinusitis results in hyperthermia over the affected site and subsequent contralateral thermal asymmetry that is clearly distinguished from the normal population. The complementary yet distinct modality of near-infrared hyperspectral imaging, which detects changes in tissue perfusion, was assessed concurrently. We have not found a diagnostic test based on static thermal imaging or near-infrared hyperspectral imaging as viable options for the widespread and routine diagnosis of human sinus conditions. The presence and prevalence of visually inconspicuous epidermal features have been identified as representing a major confounding factor for facial thermal imaging. This article also serves as an overview of diagnostic imaging techniques employed in the detection of maxillary rhinosinusitis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".