Kan AI skilja mellan bakteriella och virala infektioner vid bihåleinflammation med hjälp av termisk ansiktsavbildning ?

Bihåleinflammation diagnostiseras ofta utifrån subjektiva symtom, vilket leder till onödiga antibiotikaförskrivningar. Ansiktets termiska mönster förändras vid inflammation och blodflöde som är förknippade med infektionstyp. AI-modeller skulle kunna analysera termiska kamerabilder för att identifiera bakteriella respektive virala signaturer. Detta icke-invasiva tillvägagångssätt skulle minska missbruk av antibiotika och förbättra patientresultaten. Validering skulle kräva stora datamängder med bekräftade infektionstyper.

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.