Tongue Image–Based Diagnosis of Acute Respiratory Tract Infection Using Machine Learning: Algorithm Development and Validation
Bibliographic record
Abstract
Background: Human adenoviruses (HAdVs) and COVID-19 are prominent respiratory pathogens with overlapping clinical presentations, including fever, cough, and sore throat, posing significant diagnostic challenges without viral testing. Tongue image diagnosis, a noninvasive method used in traditional Chinese medicine, has shown correlations with specific respiratory infections, but its application remains underexplored in differentiating HAdVs from COVID-19. Advances in artificial intelligence offer opportunities to enhance tongue image analysis for more objective and accurate diagnostics. Objective: This study aims to develop and validate artificial intelligence-based predictive models using tongue image features to differentiate COVID-19 from adenoviral respiratory infections, thereby improving diagnostic accuracy and integrating traditional diagnostic methods with modern medical technologies. Methods: A total of 280 tongue images were collected from 58 patients with COVID-19, 84 patients with HAdVs, and 30 healthy controls. Deep learning methods were applied to extract tongue features, including color, coating, fissures, papillae, tooth marks, and granules. Four machine learning classifiers, logistic regression, random forest, gradient boosting model, and extreme gradient boosting, were developed to differentiate COVID-19 and HAdV infections. The key features identified by the machine learning algorithms were further visualized in a 2D space. Results: Nine tongue features showed significant differences among groups (all P<.05), including coating color (red, green, and blue), presence of tooth marks, coating crack ratio, moisture level, texture directionality, roughness, and contrast. The extreme gradient boosting model achieved the highest diagnostic performance with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.78-0.90) and an area under the precision-recall curve above 0.70. Shapley additive explanations analysis indicated tongue color, moisture, and texture as key contributors. Conclusions: Our findings demonstrate the potential of tongue diagnosis in identifying pathogens responsible for acute respiratory tract infections at the time of admission. This approach holds significant clinical implications, offering the potential to reduce clinician workloads while improving diagnostic accuracy and the overall quality of medical care.
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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 | 0.000 |
| 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".