DIGITAL DENTISTRY AND ITS IMPACT ON ORAL HEALTH-RELATED QUALITY OF LIFE

Validation of a novel tool for automated tooth modelling by fusion of CBCT-derived roots with the respective IOS-derived crowns

OBJECTIVES

To validate a novel artificial intelligence (AI)-based tool for automated tooth modelling by fusing cone beam computed tomography (CBCT)-derived roots with corresponding intraoral scanner (IOS)-derived crowns.

METHODS

A retrospective dataset of 30 patients, comprising 30 CBCT scans and 55 IOS dental arches, was used to evaluate the fusion model at full arch and single tooth levels. AI-fused models were compared with CBCT tooth segmentation using point-to-point surface distances-reported as median surface distance (MSD), root mean square distance (RMSD), and Hausdorff distance (HD)- alongside visual assessments. Qualitative assessment included visual inspection of CBCT multiplanar views. The automated fused model was also compared to expert-manual fusions for single tooth analysis in terms of accuracy, time efficiency, and consistency.

RESULTS

AI-based fusion evaluation showed mean values of MSD, RMSD, and HD of 4 μm, 114 μm, and 940 μm for full arch; 5 μm, 104 μm, and 503 μm for single tooth analysis. Qualitative assessment showed discrepancies between fused tooth outline and CBCT tooth margin lower than 1 voxel for 59% of cases. AI-based fusion showed high similarity with expert-manual fusions with median MSD, RMSD, and HD values of 28 μm, 104 μm, and 576 μm, respectively. However, AI-based fusion was 32 times faster than manual fusion. Considering the time required for manual fusion, intra-observer agreement was high (ICC 0.93), while inter-observer agreement was moderate (ICC 0.48).

CONCLUSION

The AI-based CBCT/IOS fusion demonstrated clinically acceptable accuracy, efficiency, and consistency, offering substantial time savings and robust performance across different patients and imaging devices.

CLINICAL SIGNIFICANCE

Manual CBCT/IOS fusion performed by experts is effective but labor-intensive and time-consuming. AI algorithms show a remarkable ability to minimize human variability, resulting in more reliable and efficient fusion. This capability demonstrates the potential to provide a more personalized, precise and standardized approach for treatment planning and dental procedures.

The Efficacy of Diagnostic Plaster Models in Orthodontic Diagnosis and Treatment Planning

BACKGROUND

The growing integration of digital technologies in orthodontics is shifting the orthodontic diagnosis and recordkeeping paradigm, replacing conventional plaster models with intraoral scanning and 3D photography. This study investigated the impact of orthodontic plaster models on orthodontic diagnosis and treatment planning decisions by orthodontists.

METHODS

Thirty-three orthodontists assessed six patients' records with different malocclusion cases. Each case was assessed twice by each orthodontist evaluating a case: the first evaluation with digital records without diagnostic casts and the second evaluation with the added diagnostic orthodontic plaster model. Diagnostic and treatment plan decisions for each malocclusion case were compared with and without the aid of the diagnostic orthodontic plaster models to assess the plaster model's impact on the treatment plan's soundness.

RESULTS

Statistically insignificant differences were found for the diagnoses and treatment plans with or without the aid of orthodontic plaster models. Intraclass correlation coefficients revealed agreement among orthodontists for both evaluated situations (0.685; p < 0.0001). Plaster models were found to significantly influence orthodontists' decisions about the need for surgical intervention (p = 0.026), but they did not significantly impact the overall malocclusion diagnostic classification nor the decision regarding the extent of treatment, whether comprehensive or limited (p = 0.146) and extraction versus non-extraction approaches (p = 0.266).

CONCLUSIONS

These findings support the idea that digital record alternatives may be viable for orthodontic recordkeeping purposes. Within the limitations of this study, it can be concluded that the presence or absence of orthodontic plaster models does not significantly impact the orthodontic diagnosis or treatment planning process.

DENTAL PATIENT-REPORTED OUTCOMES UPDATE 2023

The emergence and rapid development of disruptive innovations are quickly turning our profession into personalized dentistry, built upon evidence-based, data-oriented, and patient-centered research. In order to help improve the quality and quantity of patient-centered evidence in dentistry, further promote the wide and standard use of dental patient-reported outcomes (dPROs) and dental patient-reported outcome measures (dPROMs), the Journal of Evidence-Based Dental Practice has put together this special issue, the third of a series entitled Dental Patient-Reported Outcomes Update. A total of 7 solicited articles are collected in this issue. To put them into a broader perspective, this review provides a concise summary of key, selected PRO and dPRO articles published during 2023. A brief introduction to those articles included in this Special Issue follows. Four main domains are covered in this Special Issue: (1) dPROs and digital dentistry, (2) standardization of dPRO-related methodology, (3) current usage of dPROs and dPROMs in published research, and (iv) the significance and relevance of dPRO usage.

The oral health impact of dental hygiene and dental therapy populations: a systematic review

OBJECTIVES

Oral health-related quality of life (OHRQoL) is pivotal in patient care, reflecting oral health through dental patient-reported outcomes (dPROs). This systematic review aims to outline the 4-dimensional (4-D) impact of OHRQoL within patient populations routinely treated by dental hygiene and/or dental therapy providers, as there is limited literature present for these oral health care professionals.

METHODS

The study extracted and analyzed characteristics and multidimensional impact of OHRQoL, using the Oral Health Impact Profile (OHIP) as the primary dental patient-reported outcome measure (dPROM). The search strategy spanned 7 databases: Medline via the Ovid interface (Ovid MEDLINE(R) ALL), Embase via Ovid, Cinahl, APA PsycINFO via Ovid, Dentistry and Oral Sciences Search, Scopus, and Web of Science (Core Collection). It commenced September 2, 2022, with a refinement search on July 5, 2023. English language criteria yielded 645 articles postduplication removal. A screening procedure involving 3 reviewers encompassed title, abstract, and full-text review.

RESULTS

After application of inclusion and exclusion criteria, 5 articles were subjected to data extraction, capturing domain-specific information including baseline and follow-up OHRQoL data. An additional set of 13 articles containing summarized OHRQoL data underwent separate analysis. The Joanna Briggs Institute (JBI) critical appraisal tools were utilized for risk bias assessment of the included articles. The 4-D impact scores reported for baseline OHRQoL data, ranged from 3.10 to 4.20 for Oral Function, 0.84-2.70 for Orofacial Pain, 1.70-4.50 for Orofacial Appearance, and 0.44-2.50 for Psychosocial Impact. In follow-up OHRQoL data, the range for Oral Function was 1.52-3.60, Orofacial Pain 0.60-2.10, Orofacial Appearance 0.91-2.25, and Psychosocial Impact 0.10-0.60.

CONCLUSIONS

This review highlights a critical call for standardization in OHRQoL data collection for dental hygiene and dental therapy patient populations as only 26% of the predetermined distinct populations were found to have studies completed with 4-D impact of OHRQoL. Moreover, the presence of limited research in describing the multi-dimensional impact in patients routinely treated by these providers shows the urgency of substantive research in this area.