AI helps detect proximal caries and guides treatment decisions
Official title: Artificial Intelligence for Proximal Caries Detection and Its Impact on Treatment Threshold: Randomized Trial
Run by University of Michigan · NCT07648212 · Data updated 2026-11-12
AIM: See if using artificial intelligence to help detect proximal caries changes how dental students and junior clinicians decide on treatment.
Quick facts
| Study type | Interventional |
|---|---|
| Ages | 18 Years to 99 Years |
| Sex | All |
| Enrollment | 60 (estimated) |
| Sponsor | University of Michigan Academic / other |
| Locations | 1 site (Ann Arbor, Michigan) |
| Status | Recruiting |
| Started | October 5, 2026 |
| Main results expected | May 2027 |
| Study ends | May 2027 |
| Registry record updated | October 14, 2026 |
| Trial ID | NCT07648212 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are dental students in years 2–4 or residents/junior faculty with at least 2 years of clinical caries exposure at the University of Michigan.
✕Probably not a fit
Participants without prior caries detection experience or those not using radiographs frequently may see limited benefit.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This study tests whether an AI tool improves early learners’ ability to detect proximal caries on radiographs and influences their treatment choices compared with more experienced learners. Participants include dental students in years 2–4 and dental residents or junior faculty at the University of Michigan. The design involves reviewing radiograph images to determine caries presence and appropriate treatment thresholds, with comparisons between groups. Outcomes focus on diagnostic decisions and potential shifts in treatment recommendations. The study is sponsored by the University of Michigan with Delta Dental Plan of Michigan as a collaborator.
Why it matters
Potential benefit: If successful, AI support could standardize caries detection and help learners make more consistent treatment decisions.
How similar studies have gone: Prior studies in dental imaging AI have shown potential improvements in detection consistency, but results vary by setting and experience level.
Who can join: the full rules
Show full inclusion / exclusion criteria
Early learner Inclusion Criteria: * Provide informed consent * Willing to comply with all study procedures and be available for the duration of the study * Dental student in second, third, or fourth year of dental school at University of Michigan School of Dentistry Late learner Inclusion Criteria: * Provide informed consent * Willing to comply with all study procedures and be available for the duration of the study * Dental resident or junior faculty having at least 2 years of clinical experience outside of a dental education setting Exclusion Criteria for participants: * Dental students who do not have previous experience with caries detection (such as first year dental students) * Residents that do not have frequent exposure to caries detection, such as orthodontic, periodontic, oral surgery graduate residents.
Where it's running
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University of Michigan
Ann Arbor, Michigan, United States Recruiting
Study contacts
- Principal investigator: Margherita Fontana, DDS, PhD — University of Michigan
- Study coordinator: Carmen Boren, DDS, FAGD
- Email: drcarmen@umich.edu
- Phone: 701-240-8191
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.