AI Model Versus Clinician Otoscopy in the Operative Setting for Otitis Media Diagnosis.

Autor: Suresh K; Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, USA.; Department of Otolaryngology-Head & Neck Surgery, Boston, Massachusetts, USA., Wu MP; Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, USA.; Department of Otolaryngology-Head & Neck Surgery, Boston, Massachusetts, USA., Benboujja F; Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, USA.; Department of Otolaryngology-Head & Neck Surgery, Boston, Massachusetts, USA., Christakis B; Department of Pediatrics, Massachusetts General Hospital, Boston, Massachusetts, USA., Newton A; Department of Pediatrics, Massachusetts General Hospital, Boston, Massachusetts, USA., Hartnick CJ; Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, USA.; Department of Otolaryngology-Head & Neck Surgery, Boston, Massachusetts, USA., Cohen MS; Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, USA.; Department of Otolaryngology-Head & Neck Surgery, Boston, Massachusetts, USA.
Jazyk: angličtina
Zdroj: Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery [Otolaryngol Head Neck Surg] 2024 Jun; Vol. 170 (6), pp. 1598-1601. Date of Electronic Publication: 2023 Oct 11.
DOI: 10.1002/ohn.559
Abstrakt: Prior work has demonstrated improved accuracy in otitis media diagnosis based on otoscopy using artificial intelligence (AI)-based approaches compared to clinician evaluation. However, this difference in accuracy has not been shown in a setting resembling the point-of-care. In this study, we compare the diagnostic accuracy of a machine-learning model to that of pediatricians using standard handheld otoscopes. We find that the model is more accurate than clinicians (90.6% vs 59.4%, P = .01). This is a step towards validation of AI-based diagnosis under more real-world conditions. With further validation, for example on different patient populations and in deployment, this technology could be a useful addition to the clinician's toolbox in accurately diagnosing otitis media.
(© 2023 American Academy of Otolaryngology–Head and Neck Surgery Foundation.)
Databáze: MEDLINE
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