¿Puede la IA diferenciar entre infecciones bacterianas y virales en la sinusitis mediante termografía facial ?

La diagnosis de sinusitis a menudo se basa en síntomas subjetivos, lo que lleva a prescripciones innecesarias de antibióticos. Los patrones térmicos faciales cambian con la inflamación y el flujo sanguíneo asociados con el tipo de infección. Los modelos de IA podrían analizar imágenes de cámaras térmicas para identificar firmas bacterianas frente a virales. Este enfoque no invasivo reduciría el mal uso de antibióticos y mejoraría los resultados para los pacientes. La validación requeriría grandes conjuntos de datos con tipos de infección confirmados.

Background

Current diagnostic pathways for acute sinusitis rely largely on symptom-based criteria such as the 2015 Infectious Diseases Society of America guideline, which discourages routine antibiotics for presumed viral cases. Thermography detects surface-temperature variations linked to vascular and inflammatory changes; in sinusitis, bacterial infections often produce more localized heat over the maxillary sinus regions, whereas viral patterns may show diffuse, lower-grade elevations. Early pilot studies using handheld infrared cameras report discriminatory accuracy around 75–85 % when comparing cheek and forehead regions, but these datasets remain small (<200 patients) and heterogeneous in infection confirmation methods. Standardization challenges include ambient room temperature control, patient hydration status, and the timing of image capture post-symptom onset. Meta-analyses indicate that while pooled sensitivity for thermal differentiation is modest (≈68 %) and specificity ≈76 %), combining facial thermography with symptom scores improves AUC from 0.64 to 0.78 in distinguishing bacterial from viral etiologies. Nonetheless, overlap in mild bacterial and severe viral inflammation limits standalone utility; prospective validation against microbiologic culture or PCR in adequately powered cohorts (>500 participants) is still pending.