A Three-Tier Artificial Intelligence Model for COVID-19 Triage Using Pharyngeal Images (Preprint)
Notice bibliographique
Résumé
Background: SARS-CoV-2 remains a common cause of acute respiratory illness; however, symptom-based triage poorly discriminates it from other febrile conditions. A recently developed artificial intelligence (AI)-powered pharyngeal camera acquires pharyngeal images and clinical data to assist in influenza diagnosis; leveraging this workflow, we evaluated an adjunct AI algorithm (COVID-19-AI) that reports high, medium, or low suspicion to guide whether SARS-CoV-2 testing should subsequently be performed. Objective: This study aimed to report diagnostic accuracy outcomes and clinical utility of the COVID-19-AI as a triage support tool. Methods: We conducted a performance evaluation using a prospectively collected multicenter dataset from 26 Japanese institutions between December 2023 and March 2024. Patients with suspected influenza or COVID-19 were eligible. The COVID-19-AI algorithm, a stacked ensemble of a Swin Transformer and boosting models, was developed using pharyngeal images combined with routine clinical variables from 2133 patients, and it produced a 3-tier output. Classification thresholds were predefined to optimize clinical rule-out and rule-in utilities. Diagnostic performance was assessed in 696 independent patients against centralized reverse transcription polymerase chain reaction-confirmed SARS-CoV-2 infection under 2 prespecified operating criteria: inclusive (high or medium=positive and low=negative) and strict (high=positive and medium or low=negative). A subanalysis stratified accuracy by time from symptom onset (12-hour bins to 72 hours). Results: Among 696 analyzed participants (all Asian), 247 (35.5%) had reverse transcription polymerase chain reaction-confirmed SARS-CoV-2 infection. The COVID-19-AI categorized 12.4% (n=86), 72.8% (n=507), and 14.8% (n=103) patients as high, medium, and low suspicion, respectively. Under the inclusive criteria, sensitivity of COVID-19-AI was 93.9% (95% CI 90.4%-96.4%), specificity was 19.6% (95% CI 16.1%-23.5%), and negative predictive value was 85.4% (95% CI 77.6%-91.3%). Under the strict criteria, sensitivity was 24.7% (95% CI 19.6%-30.4%), specificity was 94.4% (95% CI 92.0%-96.3%), and positive predictive value was 70.9% (95% CI 60.7%-79.8%). Across 12-hour onset strata, sensitivity under the inclusive criteria remained ≥92.0% and specificity under the strict criteria remained ≥83.3%; no pronounced temporal trend was observed. Additionally, an integrated model using both pharyngeal images and clinical variables (area under the receiver operating characteristic curve [AUROC] 0.78) outperformed models using only clinical variables (AUROC 0.75) or images alone (AUROC 0.71); feature importance analysis further confirmed that pharyngeal image information was the most influential individual predictor, providing greater predictive value than any single clinical variable. Conclusions: Embedded within AI-powered pharyngeal camera workflows, a 3-tier AI suspicion output enables complementary operating behaviors-high sensitivity to rule out COVID-19 (inclusive criteria) and high specificity to support immediate infection control measures (strict criteria), although these criteria involve inherent trade-offs with low specificity and low sensitivity, respectively. Performance stability across onset times suggests robustness to symptom chronology, offering a standardized tool for clinical triage.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».