Otolaryngologist perceptions of AI-based sinus CT interpretation

Conner J Massey; Annapoorani Asokan; Caroline Tietbohl; Megan Morris; Vijay R Ramakrishnan

doi:10.1016/j.amjoto.2023.103932

Otolaryngologist perceptions of AI-based sinus CT interpretation

Am J Otolaryngol. 2023 Sep-Oct;44(5):103932. doi: 10.1016/j.amjoto.2023.103932. Epub 2023 May 21.

Authors

Conner J Massey¹, Annapoorani Asokan², Caroline Tietbohl³, Megan Morris⁴, Vijay R Ramakrishnan⁵

Affiliations

¹ Department of Otolaryngology - Head & Neck Surgery, University of Colorado School of Medicine, Aurora, CO, United States of America. Electronic address: Conner.Massey@cuanschutz.edu.
² University of Texas Southwestern Medical School, Dallas, TX, United States of America.
³ Department of Family Medicine, University of Colorado School of Medicine, Aurora, CO, United States of America; Qualitative and Mixed Methods Research Core, Adult and Child Center for Outcomes Research Delivery Science (ACCORDS), University of Colorado School of Medicine, Aurora, CO, United States of America.
⁴ Qualitative and Mixed Methods Research Core, Adult and Child Center for Outcomes Research Delivery Science (ACCORDS), University of Colorado School of Medicine, Aurora, CO, United States of America; Department of Internal Medicine, University of Colorado School of Medicine, Aurora, CO, United States of America.
⁵ Department of Otolaryngology - Head & Neck Surgery, Indiana University School of Medicine, Indianapolis, IN, United States of America.

PMID: 37245324
DOI: 10.1016/j.amjoto.2023.103932

Abstract

Background: Overcoming non-standardization, vagueness, and subjectivity in sinus CT radiology reports is an ongoing need, particularly in keeping with data-driven healthcare initiatives. Our aim was to explore otolaryngologists' perceptions of quantitative objective disease measures as enabled by AI-based analysis, and determine preferences for sinus CT interpretation.

Methods: A multi-methods design was used. We administered a survey to American Rhinologic Society members and conducted semi-structured interviews with a purposeful sample of otolaryngologists and rhinologists from varying backgrounds, practice settings and locations during 2020-2021. Interview topics included sinus CT reports, familiarity with AI-based analysis, and potential requisites for its future implementation. Interviews were then coded for content analysis. Differences in survey responses were calculated using Chi-squared test.

Results: 120 of 955 surveys were returned, and 19 otolaryngologists (8 rhinologists) were interviewed. Survey data revealed more trust in conventional radiologist reports, but that AI-based reports would be more systematic and comprehensive. Interviews expanded on these results. Interviewees believed that conventional sinus CT reports had limited utility due to inconsistent content. However, they described relying on them for reporting incidental extra-sinus findings. Reporting could be improved with standardization and more detailed anatomical analysis. Interviewees expressed interest in AI-derived analysis given potential for standardization, although they desired evidence of accuracy and reproducibility to gain trust in AI-based reports.

Conclusions: Sinus CT interpretation has shortcomings in its current state. Standardization and objectivity could be aided with deep learning-enabled quantitative analysis, although clinicians desire thorough validation to gain trust in the technology prior to its implementation.

Keywords: Artificial intelligence; Chronic rhinosinusitis; Computed tomography; Paranasal sinus; Qualitative research.

MeSH terms

Humans
Otolaryngologists*
Paranasal Sinuses* / diagnostic imaging
Reproducibility of Results
Surveys and Questionnaires
Tomography, X-Ray Computed / methods
United States