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Dental Erosion Evaluation with Intact-Tooth Smartphone Application: Preliminary Clinical Results from September 2019 to March 2022

Dental erosion is a process of deterioration of the dental hard tissue; it is estimated that about 30% of permanent teeth are affected in adolescence. The Intact-Tooth application allows for the better estimation of the problem, inserting itself in the diagnosis process, and better care and preventi...

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Detalles Bibliográficos
Autores principales: Butera, Andrea, Maiorani, Carolina, Gallo, Simone, Pascadopoli, Maurizio, Buono, Sergio, Scribante, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319592/
https://www.ncbi.nlm.nih.gov/pubmed/35890813
http://dx.doi.org/10.3390/s22145133
Descripción
Sumario:Dental erosion is a process of deterioration of the dental hard tissue; it is estimated that about 30% of permanent teeth are affected in adolescence. The Intact-Tooth application allows for the better estimation of the problem, inserting itself in the diagnosis process, and better care and prevention for the patient. It provides him with scientifically validated protocols, which the patient can consult at any time. The purpose of this report was to conduct an initial evaluation on the use of the application, which has been available since September 2019: the analysis of the collected data allowed the first investigation of the incidence of the problem and the degree of susceptibility in the registered patients. Photos of 3894 patients with dental erosion were uploaded, through which the degree of susceptibility and the BEWE (basic erosive wear examination index) index could be assessed; of these, 99.72% had a susceptibility grade of 0 to 8, while 0.28% had a medium-high susceptibility grade; this result is related to the age and sex of the patients. The management of patients through the help of the application could promote the diagnosis and treatment of enamel diseases and encourage the self-learning of the learning machine, thanks to the number of clinical cases uploaded.