Diagnostic accuracy of infrared thermal imaging for detecting COVID‐19 infection in minimally symptomatic patients
Bibliographic record
Abstract
INTRODUCTION: Despite being widely used as a screening tool, a rigorous scientific evaluation of infrared thermography for the diagnosis of minimally symptomatic patients suspected of having COVID-19 infection has not been performed. METHODS: A consecutive sample of 60 adult individuals with a history of close contact with COVID-19 infected individuals and mild respiratory symptoms for less than 7 days and 20 confirmed COVID-19 negative healthy volunteers were enrolled in the study. Infrared thermograms of the face were obtained with a mobile camera, and RT-PCR was used as the reference standard test to diagnose COVID-19 infection. Temperature values and distribution of the face of healthy volunteers and patients with and without COVID-19 infection were then compared. RESULTS: Thirty-four patients had an RT-PCR confirmed diagnosis of COVID-19 and 26 had negative test results. The temperature asymmetry between the lacrimal caruncles and the forehead was significantly higher in COVID-19 positive individuals. Through a random forest analysis, a cut-off value of 0.55°C was found to discriminate with an 82% accuracy between patients with and without COVID-19 confirmed infection. CONCLUSIONS: Among adults with a history of COVID-19 exposure and mild respiratory symptoms, a temperature asymmetry of ≥ 0.55°C between the lacrimal caruncle and the forehead is highly suggestive of COVID-19 infection. This finding questions the widespread use of the measurement of absolute temperature values of the forehead as a COVID-19 screening tool.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".