Accuracy of dentalmonitoring’s artificial intelligence in detecting aligner tracking issues: a retrospective multi-centric study
DM's AI system demonstrated high sensitivity and negative predictive values in identifying unseat events and in differentiating noticeable from slight unseats within the positive subset. These results indicate that the model performed reliably within the parameters and dataset evaluated, particularly in minimizing false-negative assessments of clinically meaningful misfits. Further validation in independent cohorts and across broader clinical contexts is warranted to confirm generalizability.
Authors
- William Dabney
- Julie McCray (ORCID: https://orcid.org/0000-0003-3475-2078)
- Naurine Shah (ORCID: https://orcid.org/0000-0002-4028-9043)
- Mohammed H. Elnagar (ORCID: https://orcid.org/0000-0002-2332-9250)
- Logan Smith
- Dylan Handlin
- Sarah Zaki
- Jessie Zhu
- Maria Roxas Regina Trinidad
- Rayan Skafi
Institutions
- Tanta University (EG)
- University of Dental Medicine (MM)
- University of Alabama at Birmingham (US)
- University of Illinois Chicago (US)
- Hill Engineering (United States) (US)
- Saint Louis University (US)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2026-01-07
- DOI
- https://doi.org/10.1186/s12903-025-07580-0
- Primary Topic
- Orthodontics and Dentofacial Orthopedics
- Type
- article
- Field-Weighted Citation Impact
- 0.00