Modern Methods of Studying Gum Recession (literature Review)
Year: 2024
Type: article
Abstract: The tasks of modern implantology and periodontology include both ensuring successful engraftment of a dental implant and achieving functional restoration of the dentition along with obtaining a highly aesthetic treatment result. To achieve a successful result of surgical intervention in the treatment of gingival recession, it is extremely important to find accurate methods for determining the thickness of keratinized soft tissues of the oral cavity before surgical treatment. Currently, existing methods have a number of disadvantages, first of all, they are invasive and traumatic for the patient. Therefore, an important task is to develop an objective non-invasive method for determining the thickness of keratinized soft tissues before surgical treatment. The article presents literature data describing the most common methods for determining the thickness of keratinized gums before surgical treatment. The review and analysis of literary sources was carried out using keywords on the electronic resources of the Scopus, Web of Science, MedLine, The Cochrane Library, and Russian Science Citation Index (RSCI) databases. Foreign and domestic sources were used to write the review article. In addition to parametric measurements of gingival recession, the results of using modern computer technologies during clinical examination are also of great diagnostic value for the prognosis and prevention of soft tissue recession around the implant. Recently, 3D modeling based on non-invasive methods, such as computed tomography for assessing the volume of gum tissue, has become increasingly widespread in maxillofacial surgery and orthodontics. Measuring the thickness of the attached gingiva is also an important indicator in the diagnosis of gingival recession, which plays an important role in predicting and planning a treatment.
Author: Akbarov A.N., Khomidov M.A., Aslanova G.B.
Language: English
Subfield: Oral Surgery
Field: Dentistry
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6968048980
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