| Autor: |
Huang, Boyen, Estai, Mohamed, Pungchanchaikul, Patimaporn, Quick, Karin, Ranjitkar, Sarbin, Fashingbauer, Emily, Askar, Abdirahim, Wang, Josiah, Diefalla, Fatma, Shenouda, Margaret, Seyffer, Danae, Louie, Jeffrey P. |
| Předmět: |
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| Zdroj: |
Telemedicine & e-Health; Oct2024, Vol. 30 Issue 10, p2592-2600, 9p |
| Abstrakt: |
Background: Mobile health (mHealth) has an emerging potential for remote assessment of traumatic dental injuries (TDI) and support of emergency care. This study aimed to determine the diagnostic accuracy of TDI detection from smartphone-acquired photographs. Methods: The upper and lower anterior teeth of 153 individuals aged ≥ 6 years were photographed using a smartphone camera app. The photos of 148 eligible participants were reviewed independently by a dental specialist, two general dentists, and two dental therapists, using predetermined TDI classification and criteria. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and inter-rater reliability were estimated to evaluate the diagnostic performance of the photographic method relative to the reference standard established by the dental specialist. Results: Of the 1,870 teeth screened, one-third showed TDI; and one-seventh of the participants had primary or mixed dentitions. Compared between the specialist's reference standard and four dental professionals' reviews, the diagnostic sensitivity and specificity for TDI versus non-TDI were 59–95% and 47–93%, respectively, with better performance for urgent types of TDI (78–89% and 99–100%, separately). The diagnostic consistency was also better for the primary/mixed dentitions than the permanent dentition. Conclusion: This study suggested a valid mHealth practice for remote assessment of TDI. A better diagnostic performance in the detection of urgent types of TDI and examination of the primary/mixed dentition was also reported. Future directions include professional development activities involving dental photography and photographic assessment, incorporation of a machine learning technology to aid photographic reviews, and randomized controlled trials in multiple clinical settings. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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