Kan AI bacteriële en virale infecties bij sinusitis differentiëren met behulp van thermische gezichtsbeelden ?

Sinusitisdiagnose berust vaak op subjectieve symptomen, wat leidt tot onnodige antibioticavoorschriften. Gezichtsthermische patronen veranderen bij ontsteking en bloedstroom die gepaard gaat met het type infectie. AI-modellen zouden thermische camerabeelden kunnen analyseren om bacteriële versus virale kenmerken te identificeren. Deze niet-invasieve aanpak zou het misbruik van antibiotica verminderen en de patiëntuitkomsten verbeteren. Validatie zou grote datasets met bevestigde infectietypen vereisen.

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.