An artificial intelligence decision support tool to interpret videos of the tympanic membrane can predict diagnosis of acute otitis media (AOM), according to a study published online March 4 in JAMA Pediatrics.
Nader Shaikh, M.D., from the University of Pittsburgh School of Medicine, and colleagues developed and internally validated an artificial intelligence decision-support tool to interpret videos of the tympanic membrane and enhance accuracy in the diagnosis of AOM. Otoscopic videos of the tympanic membrane captured using a smartphone during outpatient clinic visits at two sites in Pennsylvania were analyzed; 1,151 videos from 635 children (most younger than 3 years) were included in the study.
The researchers found that diagnostic accuracy was almost identical for the deep residual-recurrent neural network and the decision tree network. Tympanic membrane videos were classified into AOM versus no AOM categories with a sensitivity and specificity of 93.8 and 93.5 percent, respectively, with the finalized deep residual-recurrent neural network, while the decision tree model had corresponding sensitivity and specificity of 93.7 and 93.3 percent. Bulging of the tympanic membrane aligned with the predicted diagnosis most closely; in the test set, bulging was present in all 230 cases in which the diagnosis was predicted to be AOM.
“With appropriate training, this tool could be used by a wide range of medical personnel to enhance teaching of otoscopic examination, discussion with colleagues, documentation in the electronic health record, and discussion with parents,” the authors write.
Abstract/Full Text (subscription or payment may be required)
Editorial (subscription or payment may be required)
Copyright © 2024 HealthDay. All rights reserved.
