Ritter Ilia

Moldova

Prevalence of Complete Edentulism and Innovative Perspectives in Diagnosis and Treatment

Ilya Ritter 1, Alexander Postolaki1

1 – Department of Prosthodontics Dentistry “Ilarion Postolaki”, State University Medicine and Farmacy “Nicolae Testemitanu”, Chisinau, Republic of Moldova

Abstract

Background

Complete edentulism (CE) is a dental pathology that remains an unresolved issue in medicine. The lowest prevalence rates of CE in the population aged 20 years and older have been recorded in Africa at 3.34%, while the highest rates are observed in Europe at 12.42%. According to the WHO (2022), CE is identified in 7% of the global population over the age of 20 and in 23% of those over 60 years old. CE is a polyetiological pathology and represents the most severe clinical situation in prosthetic dentistry. It poses significant challenges in diagnosis and in selecting an appropriate treatment method due to shortcomings in research techniques. For example, 87.1% of all orthopantomograms contain errors [Fairozekhan A., et al., 2020].

Methods

A literature review on the prevalence of CE was conducted to identify morphohistological changes in the maxillofacial region. The search utilized databases was Cyberleninka, PubMed, Google Scholar, IBN, websites of the WHO and the UN. A total of 63 sources were selected on the topic. Based on statistical data from 19 countries, we compiled a table.

Results

CE is a widespread polyetiological pathology causing anatomical, functional, and morphohistological changes, categorized into four main groups: 1) Dental complications; 2) TMJ and jaw bone tissue; 3) Muscles mobilizing the mandible; 4) Mucosal-fibrous base and blood vessels. Chewing, speech, swallowing, and aesthetics are entirely disrupted, significantly affecting patients’ physical and mental health. These challenges complicate CE diagnosis and treatment, which aim to restore lost structures and functions. The integration of artificial intelligence (AI) offers promising solutions, as AI can reduce diagnostic and treatment planning errors and outperform practitioners in disease diagnosis [Goh E., et al., 2024].

Conclusions

A literature review revealed a lack of information on etiological factors, which prevents conducting a comprehensive statistical analysis. It is likely that such data would be beneficial in the future for reducing errors and enabling accurate prognoses. Reports indicate the successful implementation of AI-based programs (Diagnocat, Webceph) in dentistry, offering hope for an innovative approach to improving the quality of diagnosis, prevention, and treatment [Dvoyris V., 2023].