Digital Otoscopy With Computer-Aided Composite Image Generation: Impact on the Correct Diagnosis, Confidence, and Time

Authors

• Seda Camalan
• Carl D Langefeld
• Amy Zinnia
• Brigham McKee
• Matthew L Carlson
• Nicholas L Deep
• Michael S Harris
• Taha A Jan
• Vivian F Kaul
• Nathan R Lindquist
• Jameson K Mattingly
• Jay Shah
• Kevin Y Zhan
• Metin N Gurcan
• Aaron C Moberly

Language

English

Publication Date

1-1-2025

Journal

Otolaryngology–Head and Neck Surgery

DOI

10.1002/ohn.965

PMID

39221462

PMCID

PMC11905175

PubMedCentral® Posted Date

1-1-2026

PubMedCentral® Full Text Version

Author MSS

Abstract

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.

Keywords

Humans, Otoscopy, Ear Diseases, Video Recording, Time Factors, Clinical Competence, Artificial Intelligence, Computer-assisted diagnosis, eardrum, image stitching, otoscopy, telemedicine

Published Open-Access

yes

Recommended Citation

Camalan, Seda; Langefeld, Carl D; Zinnia, Amy; et al., "Digital Otoscopy With Computer-Aided Composite Image Generation: Impact on the Correct Diagnosis, Confidence, and Time" (2025). Faculty, Staff and Student Publications. 4067.
https://digitalcommons.library.tmc.edu/uthmed_docs/4067