Does orthodontic treatment improve the Oral Health-Related Quality of Life when assessed using the Malocclusion Impact Questionnaire-a 3-year prospective longitudinal cohort study

Image-based multi-omics analysis for oral science: Recent progress and perspectives

OBJECTIVES

The diagnosis and treatment of oral and dental diseases rely heavily on various types of medical imaging. Deep learning-mediated multi-omics analysis can extract more representative features than those identified through traditional diagnostic methods. This review aims to discuss the applications and recent advances in image-based multi-omics analysis in oral science and to highlight its potential to enhance traditional diagnostic approaches for oral diseases.

STUDY SELECTION, DATA, AND SOURCES

A systematic search was conducted in the PubMed, Web of Science, and Google Scholar databases, covering all available records. This search thoroughly examined and summarized advances in image-based multi-omics analysis in oral and maxillofacial medicine.

CONCLUSIONS

This review comprehensively summarizes recent advancements in image-based multi-omics analysis for oral science, including radiomics, pathomics, and photographic-based omics analysis. It also discusses the ongoing challenges and future perspectives that could provide new insights into exploiting the potential of image-based omics analysis in the field of oral science.

CLINICAL SIGNIFICANCE

This review article presents the state of image-based multi-omics analysis in stomatology, aiming to help oral clinicians recognize the utility of combining omics analyses with imaging during diagnosis and treatment, which can improve diagnostic accuracy, shorten times to diagnosis, save medical resources, and reduce disparity in professional knowledge among clinicians.

Impact of orthodontic treatment on OHRQoL of adolescents: a longitudinal study

OBJECTIVE

The aim of this study was to evaluate the extent to which orthodontic treatment need is perceived by the patients and by the orthodontist, as well as the possible impacts on the OHRQoL (Oral Health-Related Quality of Life) over the course of conventional orthodontic treatment in adolescent patients.

METHODS

The sample consisted of 55 adolescents. The perception of patients and orthodontists relative to the malocclusion was evaluated by the IOTN (Index of Orthodontic Treatment Need). The OHRQoL was evaluated by the Child-OIDP (Child-Oral Impacts on Daily Performances) questionnaire before the conventional orthodontic appliance was bonded (T0); and at the following time intervals: after one week (T1), one month (T2), three months (T3), six months (T4), and after the end of orthodontic treatment (T5).

RESULTS

Adolescents who had large orthodontic treatment needs had a poor OHRQoL, according to their self-perception (p=0.003) and according to the orthodontist's perception (p<0.001), when compared with patients with small and moderate needs. There was statistically significant difference in the OHRQoL between the time intervals T0 and T1 (p=0.021), T2 and T3 (p<0.001), T3 and T4 (p=0.033), and T0 and T5 (p<0.002). At the end of treatment, all evaluated participants reported an improvement in OHRQoL.

CONCLUSIONS

It was concluded that adolescents and orthodontists agreed with regard to the perception of orthodontic treatment need. In the first week and in the first month of orthodontic treatment, there was a negative impact on the OHRQoL. After three months, an improvement of OHRQoL was detected, which has progressed over time.

Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives

In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.