Journal article
Comparisons of the reliability of airway measurements on cone beam computed tomography scans among human raters and a convolutional neural network
Oral surgery, oral medicine, oral pathology and oral radiology, Vol.141(2), pp.255-264
02/2026
DOI: 10.1016/j.oooo.2025.10.001
PMID: 41238482
Abstract
Objective
To evaluate the performance of commercially available AI tools in airway evaluation in clinical conditions.
Study Design
100 anonymized cone beam computed tomography datasets obtained from the records of the University of Iowa were oriented and analyzed by two calibrated oral and maxillofacial radiology residents using InVivo software (InVivo6, Anatomage, San Jose, CA). Measurements made were total airway volume and minimum cross-sectional surface area of the airway. These measurements were then compared to those produced by uploading the same datasets to Diagnocat AI software (Diagnocat, San Francisco, CA).
Results
The results indicate that all comparisons showed good to excellent reliability (ICC > 0.75), suggesting high agreement between raters. Specifically: The intraclass correlation coefficients of .954 (CI: 0.757 – 0.983) and .944 (CI: 0.732 – 0.978) between the human and Diagnocat measurements indicate excellent reliability.
Conclusion
Diagnocat (Diagnocat, San Francisco, CA) artificial intelligence software is capable of analyzing patients’ oropharyngeal airway volume and minimum cross-sectional area in cone beam computed tomography datasets with minimal to moderate motion artifact at a level comparable to qualified human practitioners.
Details
- Title: Subtitle
- Comparisons of the reliability of airway measurements on cone beam computed tomography scans among human raters and a convolutional neural network
- Creators
- Steven DorrisSindura AnamaliJuan P. CastroShareef DabdoubTrishul Allareddy
- Resource Type
- Journal article
- Publication Details
- Oral surgery, oral medicine, oral pathology and oral radiology, Vol.141(2), pp.255-264
- DOI
- 10.1016/j.oooo.2025.10.001
- PMID
- 41238482
- NLM abbreviation
- Oral Surg Oral Med Oral Pathol Oral Radiol
- ISSN
- 2212-4403
- eISSN
- 2212-4411
- Publisher
- Elsevier
- Language
- English
- Electronic publication date
- 10/17/2025
- Date published
- 02/2026
- Academic Unit
- Oral Pathology, Radiology and Medicine; Dental Research; Periodontics
- Record Identifier
- 9985019043302771
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