Intraoral Scanners as Periodontal and Dental Pathologies Diagnosis Tools

Overview

Periodontal diseases and dental pathologies are highly prevalent oral diseases. Thirty-three to fifty percent of adult population presented at least one untreated caries and more than 50% of French population are affected by severe periodontitis. These diseases affect dental organ or periodontal attached system but could have negative impact on general health, quality of life, word and individual well-being. Association between chronic diseases as diabetes, rheumatoid arthritis, cardiovascular diseases, and oral health have been well investigated. Dental and periodontal diagnosis is dependent of various clinical parameters time consuming and dependent operator. It represents a public health challenge. Informatic analysis detecting diseases could be a time gain and a more precise diagnosis tool. Today, any software or algorithm allow automatized detection, clinical qualitative or quantitative indices recording while these informations are present in numeric models

The present study explores the effectiveness of intraoral scanners in the field of dental caries and periodontal diseases diagnosis Patients presenting at least on recession are recruited without randomization. A clinical examination is performed, and a 3D impression of their mouth is obtained by intraoral scanners. The investigators hypothesized that the 3D intra oral representations could improve oral diagnosis in patients compared to clinical examination in term of time consummation and precision. In a first time, this could be achieved by the comparison of the recession measurement obtained by a clinical measure (Ramfjord, 1959) or obtained by artificial intelligence. The secondary objectives are : * To compare clinical and numerical dento-prosthetic diagram * To compare clinical and numerical plaque control record index (O'Leary et al., 1973) * To compare clinical and numerical Decayed, Missed and Filled Teeth index (Klein and Palmer, 1940) * To compare clinical and numerical detection of mucosal lesions