Precision dentistry—what it is, where it fails (yet), and how to get there

Schwendicke, Falk; Krois, Joachim

doi:10.1007/s00784-022-04420-1

Cited by 20 publications

(20 citation statements)

References 27 publications

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“…Furthermore, surface curvature analysis is an excellent example of overcoming evaluation with several standardised yet arbitrary preselected measuring sites, potentially omitting relevant information [ 49 ]. Such a comprehensive approach may also represent a considerable step toward personalized or precision dentistry [ 50 , 51 ].…”

Section: Discussionmentioning

confidence: 99%

Gingival shape analysis using surface curvature estimation of the intraoral scans

et al. 2022

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Background Despite many advances in dentistry, no objective and quantitative method is available to evaluate gingival shape. The surface curvature of the optical scans represents an unexploited possibility. The present study aimed to test surface curvature estimation of intraoral scans for objective evaluation of gingival shape. Methods The method consists of four main steps, i.e., optical scanning, surface curvature estimation, region of interest (ROI) definition, and gingival shape analysis. Six different curvature measures and three different diameters were tested for surface curvature estimation on central (n = 78) and interdental ROI (n = 88) of patients with advanced periodontitis to quantify gingiva with a novel gingival shape parameter (GS). The reproducibility was evaluated by repeating the method on two consecutive intraoral scans obtained with a scan-rescan process of the same patient at the same time point (n = 8). Results Minimum and mean curvature measures computed at 2 mm diameter seem optimal GS to quantify shape at central and interdental ROI, respectively. The mean (and standard deviation) of the GS was 0.33 ± 0.07 and 0.19 ± 0.09 for central ROI using minimum, and interdental ROI using mean curvature measure, respectively, computed at a diameter of 2 mm. The method’s reproducibility evaluated on scan-rescan models for the above-mentioned ROI and curvature measures was 0.02 and 0.01, respectively. Conclusions Surface curvature estimation of the intraoral optical scans presents a precise and highly reproducible method for the objective gingival shape quantification enabling the detection of subtle changes. A careful selection of parameters for surface curvature estimation and curvature measures is required.

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Section: Discussionmentioning

confidence: 99%

Gingival shape analysis using surface curvature estimation of the intraoral scans

et al. 2022

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“…By combining multi-modal and temporal images, mucosa thickness and thickness changes were evaluated. Adding the time component into the virtual patient concept by translating findings from research to the clinic setting also presents a future step towards personalised or precision dentistry (Joda et al, 2020;Lahoud et al, 2022;Schwendicke & Krois, 2022). Furthermore, with AI-assisted segmentation of the CBCT images, segmentation became simpler, increasing future possibilities (Fontenele et al, 2022;Shaheen et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

A Novel Digital Method for Evaluation of Mucosa Thickness Changes in Time: Changes Associated With a Removable Partial Denture

Kuralt¹,

Bukleta²,

Kuhar³

et al. 2022

Image Anal Stereol

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The aim of the study was to propose a method for three-dimensional evaluation and visualisation of mucosa thickness and mucosa thickness changes and to validate it in four different software implementations. Cone-beam computed tomography (CBCT) and digitised mandibular impression, i.e., optical scan, of five patients treated with a mandibular distal extension removable partial denture were acquired at baseline (T0) and 1-year follow-up (T1). CBCT images were automatically segmented and then superimposed with a corresponding optical scan and within a patient. To obtain mucosa thickness changes in the T0-T1 interval, firstly, the distances between T0 and T1 models were computed for mucosa surface, representing surface changes (dSurface) and bone surface, representing bone changes (dBone). The distances were saved as scalar field values in the mesh model. Finally, the mucosa thickness changes (dMucosa) were calculated by subtracting the corresponding dBone from dSurface values. Distance computation algorithms in four different software were tested and compared. No differences were found between all four tested software (p0.001). Mean (and standard deviation) of median dSurface, dBone, and dMucosa of right and left residual ridge (n=10) was -0.47mm (0.43), -0.44mm (0.62), and 0.00mm (0.35), respectively. High local variability of dBone and dMucosa was found on the colour-coded maps. A novel method facilitates precise three-dimensional evaluation and visualisation of mucosa thickness and thickness changes, regardless of the software used.

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“…According to Polverini, these challenges include gaps in education of the current knowledge related to the basis of personalized dental medicine, addressing issues such as reimbursement for preventive dental procedures, insufficient an integrated electronic health record, difficulty in protecting patient genetic information, and differences in access to genomic medicine by certain populations. However, the dental profession should act in tandem with medical profession to pace toward innovation and harness advanced technologies to ensure the development and progression of personalized healthcare environment [62,90].…”

Section: Personalized Dentistrymentioning

confidence: 99%

Implantogenomic: Conceptualizing Osseointegration toward Personalized Dental Implant Therapy

Refai¹

2023

Dentistry

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The spectrum of patients’ needs for dental implant treatment ranges between healthy individuals to those with complex diseases and compromised jaw bones. The aim of this review chapter is to introduce the application of personalized dental medicine to dental implant field as a therapeutic strategy that is best suited to individualized patient’s genetic makeup “Implantogenomics” to enhance their longevity and clinical outcomes. The ultimate goal of personalized medicine and dentistry is tailoring targeted treatment to the patient’s individual genetic makeup and having predictive outcomes. This approach will transfer the traditionally known “one size fits all” to an actionable model, tailoring therapy to individuals in a homogenous stratified group. In this review chapter, in analogy to pharmacogenomics, personalized dental implant and its implantogenomics concept have been proposed as a novel application of personalized dentistry. It is conceivable that the actionable model that integrates genomics and materiomics will accelerate the production of personalized implantable biomaterials and biomedical devices. Moreover, the convergence of multi-disciplines including biological sciences, material sciences, and computational tools may underpin the application of personalized dental implant therapy in the future. This approach will unleash the potential of advancing technologies to tailor dental implants targeting different subpopulations. Despite this optimistic goal, challengeable remains ahead of us where the conduction of well-directed scientific and clinical research is needed.

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Precision dentistry—what it is, where it fails (yet), and how to get there

Cited by 20 publications

References 27 publications

Gingival shape analysis using surface curvature estimation of the intraoral scans

Gingival shape analysis using surface curvature estimation of the intraoral scans

A Novel Digital Method for Evaluation of Mucosa Thickness Changes in Time: Changes Associated With a Removable Partial Denture

Implantogenomic: Conceptualizing Osseointegration toward Personalized Dental Implant Therapy

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