Objective: This study investigated the comparative performance of ear, nose, and throat (ENT) physicians in correctly detecting ear abnormalities when reviewing digital otoscopy imaging using 3 different visualization methods, including computer-assisted composite images called "SelectStitch," single video frame "Still" images, and video clips. The study also explored clinicians' diagnostic confidence levels and the time to make a diagnosis.
Study Design: Clinician diagnostic reader study.
Setting: Online diagnostic survey of ENT physicians.
Methods: Nine ENT physicians reviewed digital otoscopy examinations from 86 ears with various diagnoses (normal, perforation, retraction, middle ear effusion, tympanosclerosis). Otoscopy examinations used artificial-intelligence (AI)-based computer-aided composite image generation from a video clip (SelectStitch), manually selected best still frame from a video clip (Still), or the entire video clip. Statistical analyses included comparisons of ability to detect correct diagnosis, confidence levels, and diagnosis times.
Results: The ENT physicians' ability to detect ear abnormalities (33.2%-68.7%) varied depending on the pathologies. SelectStitch and Still images were not statistically different in detecting abnormalities (P > .50), but both were different from Video (P < .01). However, the performance improvement observed with Videos came at the cost of significantly longer time to determining the diagnosis. The level of confidence in the diagnosis was positively associated with correct diagnoses, but varied by particular pathology.
Conclusion: This study explores the potential of computer-assisted techniques like SelectStitch in enhancing otoscopic diagnoses and time-saving, which could benefit telemedicine settings. Comparable performance between computer-generated and manually selected images suggests the potential of AI algorithms for otoscopy applications.
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| http://dx.doi.org/10.1002/ohn.965 | DOI Listing |
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