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Mobile Health Assessment of Traumatic Dental Injuries Using Smartphone-Acquired Photographs: A Multicenter Diagnostic
Boyen Huang1, Mohamed Estai2, Patimaporn Pungchanchaikul3
1University of Minnesota School of Dentistry, Minneapolis, Minnesota, USA.
Summary
Smartphone photos show potential for remote assessment of traumatic dental injuries (TDI). The mHealth approach demonstrated diagnostic accuracy, particularly for urgent TDIs and primary/mixed dentitions.
Area of Science:
- Dentistry
- Digital Health
- Public Health
Background:
- Mobile health (mHealth) offers emerging potential for remote assessment of traumatic dental injuries (TDI).
- Emergency care support through remote TDI assessment is a growing area of interest.
- Smartphone-acquired photographs are being explored for diagnostic capabilities in dentistry.
Purpose of the Study:
- To determine the diagnostic accuracy of detecting traumatic dental injuries (TDI) using smartphone-acquired photographs.
- To evaluate the performance of mHealth in remote TDI assessment.
- To compare diagnostic consistency across different dental professionals and dentition types.
Main Methods:
- 153 individuals aged ≥ 6 years had their anterior teeth photographed using a smartphone app.
- Photos from 148 eligible participants were independently reviewed by a dental specialist, two general dentists, and two dental therapists.
- Diagnostic sensitivity, specificity, accuracy, and inter-rater reliability were calculated against a specialist's reference standard.
Main Results:
- One-third of 1,870 screened teeth exhibited TDI; one-seventh of participants had primary/mixed dentitions.
- Diagnostic sensitivity for TDI ranged from 59-95% and specificity from 47-93%.
- Better performance was observed for urgent TDI types (sensitivity 78-89%, specificity 99-100%) and primary/mixed dentitions.
Conclusions:
- Smartphone-based mHealth is a valid practice for remote TDI assessment.
- The method shows improved diagnostic performance for urgent TDIs and primary/mixed dentitions.
- Future research should focus on professional training, AI integration, and multi-center trials.

