Image Anal Stereol 2022;41:181-191  doi: 105566/ias.2767 
Original Research Paper 
181 
 
A NOVEL DIGITAL METHOD FOR EVALUATION OF MUCOSA 
THICKNESS CHANGES IN TIME: CHANGES ASSOCIATED WITH  
A REMOVABLE PARTIAL DENTURE 
MARKO KURALT1,2, MANUSHAQE SELMANI BUKLETA2,3, MILAN KUHAR4,5, AND ALEŠ FIDLER,1,6 
1Department of Restorative Dentistry and Endodontics, University Medical Centre Ljubljana, Slovenia; 
2Faculty of Medicine, University of Ljubljana, Slovenia; 3School of Dentistry, Faculty of Medicine, University 
of Prishtina, Kosovo; 4Department of Prosthetic Dentistry, Faculty of Medicine, University of Ljubljana, 
Slovenia; 5Department of Prosthodontics, University Medical Centre Ljubljana, Slovenia; 6Department of 
Endodontics and Operative Dentistry, Faculty of Medicine, University of Ljubljana, Slovenia 
e-mail: marko@kuralt.si; manushaqeart@gmail.com; milan.kuhar@mf.uni-lj.si; ales.fidler@mf.uni-lj.si 
(Received July 16, 2022; revised October 4, 2022; accepted October 10, 2022) 
ABSTRACT 
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 implementa-
tions. 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 superim-
posed 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 (p<0.001). 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. 
Keywords: cone beam computed tomography; image analysis; optical scanning: oral mucosa; removable 
partial denture. 
INTRODUCTION  
Developments in the imaging technologies in 
dentistry, such as cone-beam computed tomography 
(CBCT) and optical scanning, revolutionised the field 
in research and clinical setting (Gaêta-Araujo et al., 
2020; Shujaat et al., 2021). CBCT enables the 
evaluation and visualisation of mineralised structures 
such as bones and teeth that are not visible at clinical 
examination. It presents a smaller radiation dose and 
higher spatial resolution than medical computed 
tomography (Jacobs et al., 2018). While optical 
scanning, especially recently introduced intraoral 
scanning, is an entirely non-invasive method used to 
capture the surface of intraoral structures, such as teeth 
and soft tissues (Mangano et al., 2017). Digital models 
obtained with the modalities mentioned above are 
represented as polygonal surface meshes. Optical 
scanning requires no additional processes, as surface 
meshes are generated during the acquisition process, 
while the CBCT images require prior segmentation of 
anatomical structures of interest (Verhelst et al., 2021). 
In the past, segmentation of CBCT images often 
presented a challenge mainly due to unstandardized 
grey voxel values and artefacts (Friedli et al., 2020; 
Hassan et al., 2010; Pauwels et al., 2015; Schulze et 
al., 2011). However, the recent introduction of deep 
learning and artificial intelligence (AI) for the 
segmentation of CBCT images made the process time-
efficient and more reproducible (Shaheen et al., 2021; 
Verhelst et al., 2021).  
Removable partial dentures (RPDs) are still widely 
used in clinical practices to treat partial edentulism, 
despite other predominant treatment options, including 
fixed partial dentures and implants (Douglass & 
 KURALT M ET AL.: Evaluation of mucosa thickness changes 
182 
 
Watson, 2002; Ramsay et al., 2015). A recent review 
on RPDs (Campbell et al., 2017) emphasised the need 
for innovation regarding novel materials (Najeeb et al., 
2016; Srinivasan, Kalberer, Kamnoedboon, et al., 
2021) and fabrication technologies (Alammar et al., 
2022; Piedra-Cascón et al., 2021; Revilla-León et al., 
2020; Srinivasan, Kalberer, Fankhauser, et al., 2021) 
due to the limitations associated with existing RPDs 
(Benso et al., 2013; Preshaw et al., 2011; Saito et al., 
2002). During mastication, the mucosa plays a critical 
role in distributing and withstanding occlusal forces 
transferred through the denture to the underlying bone 
(Kumar, 2014; Żmudzki et al., 2015). In the past, 
evaluation of bone changes of the residual ridge was 
the main emphasis in the evaluation of denture-
supporting tissue changes (Ahmad et al., 2013; Blum & 
Fraser McCord, 2004; Ozan et al., 2013; Tallgren, 
2003). Occlusal forces exerted on the denture 
supporting bone can significantly increase resorption of 
the residual ridge; otherwise, an irreversible 
physiological process and consequently leads to poor 
denture fit (Saito et al., 2002). Recently, significant 
interest has arisen in evaluating mucosa (Chen et al., 
2015; Dong et al., 2015). The mucosal response to 
dentures has exhibited complex nonlinear and time-
dependent behaviours (Cook, 1991; Khamis Abdel 
Razek & Shaaban, 1978). Furthermore, mucosa 
thickness has been established as one of the main 
factors affecting biomechanical response. However, 
there seems to be a lack of clinical studies quantifying 
and describing mucosal changes associated with 
denture wearing. Superimposed segmented CBCT 
images and optical scans were recently utilised in a 
study evaluating bone and mucosal surface changes 
associated with wearing RPDs (Kuralt et al., 2019). 
The study's main limitation was the lack of mucosal 
thickness changes. From an anatomical point of view, 
the mucosa is a flat structure covering the underlying 
bone. Changing its thickness might compensate for 
changes in the underlying bone, thus retaining the same 
surface morphology. The bone and mucosa surface 
changes can be simply calculated by measuring the 
difference between two models, i.e. bone or surface. In 
contrast, the mucosa thickness change results from 
changes of four models, i.e. two bone and two surface 
models, obtained at two-time points and is much more 
complicated for evaluation. 
Thus, the present study aimed to propose a novel 
method for three-dimensional evaluation and 
visualisation of mucosa thickness changes, using 
CBCT and optical scanning, and to validate it in four 
different software implementations. 
 
MATERIALS AND METHODS 
PATIENTS AND IMAGE DATA ACQUISI-
TION 
This methodological study is a part of a research 
project evaluating the effect of prosthodontic treatment 
of partially edentulous patients on oral health. Ethical 
approval was obtained from the Ethics Committee of 
Hospital and University Clinical Service of Kosovo 
and University Clinical Centre of Kosovo 
(555/18.05.2017). In addition, all patients were 
informed of the study protocol and gave their written 
consent to participate. Patients of both genders between 
45 and 65 years old with a bilateral edentulous area 
distal to the remaining natural teeth (Kennedy class I 
status of partial edentulism) were included in the 
project. The patients had no previous prosthodontic 
treatment and were without active caries lesions and 
periodontal disease. 
All patients were treated with a mandibular distal 
extension RPD and underwent clinical and 
radiographical examination at baseline (T0), including 
a CBCT and a mandibular impression. After one year 
(T1), a clinical and radiographical examination was 
repeated, including a CBCT scan and an impression. 
For a more detailed description of the data acquisition, 
readers are referred to the study of Kuralt et al. (Kuralt 
et al., 2019). In brief, all CBCT scans were taken with 
an ORTHOPHOS XG 3D device (Dentsply Sirona) 
with the following scanning parameters: field of view 
(FOV) 8 × 8 cm, voxel size 0.16 mm3, 85 kV and 7 mA 
and 10 mA for female and male patients, respectively. 
Scans were exported in DICOM format. At the same 
time, mandibular impressions were taken with an 
irreversible hydrocolloid impression material 
(XantALGIN Select Fast Set; Heraeus Kulzer GmbH) 
and poured with gypsum immediately after making. 
Casts were digitised with a laboratory optical scanner 
(Ceramill Map 400; Amann Girrbach AG) and 
exported in Standard Tessellation Language (STL) file 
format. The first five patients' image data were used for 
this methodological study.  
IMAGE SEGMENTATION AND SUPERIM-
POSITION 
A CBCT and optical scans for each patient and 
time-point were uploaded to the cloud-based platform 
“Virtual Patient Creator” (https://creator.relu.eu, Relu 
BV), offering AI-assisted segmentation and 
superimposition of dental and maxillofacial images. 
Mandible and teeth were automatically segmented on 
CBCT image and superimposed with an optical scan of 
mandibular cast model using teeth. Furthermore, CBCT 
Image Anal Stereol 2022;41:181-191 
 
183 
 
images with corresponding optical scans from both 
time points were superimposed manually using GOM 
Inspect (version 2018, GOM GmbH) with a selection 
of stable regions on the body of the mandible (Fig. 1). 
Finally, all four superimposed surface meshes per 
patient were exported in STL file format for further 
analysis. 
 
 
Fig. 1: Superimposition of cone-beam computed tomography (CBCT) and optical scans, i.e., digitised impressions, 
per patient from baseline (T0) and follow-up (T1) (a). Teeth surfaces were used to superimpose multi-modal 
images, i.e., CBCT and optical scan. In contrast, for multi-temporal data, i.e., CBCT scans from T0 and T1 with 
corresponding already superimposed optical scans, stable anatomical regions on the mandibles body were used, 
i.e., left and right mental foramina and mental spine. In addition, two buccal-oral cross-sections were made (B and 
C), indicating regions without (b) and with changes (c) and one mesio-distal cross-section (D), combining both 
regions (d). 
 
EVALUATION AND VISUALISATION OF 
DENTURE SUPPORTING TISSUES  
Distances were computed between superimposed 
meshes, i.e., CBCTT0, CBCTT1, scanT0, and scanT1, 
obtaining surface change (dSurface) and bone thickness 
change (dBone), and mucosa thickness (mucosaT0 and 
mucosaT1) (Fig. 2). A “cloud to mesh” algorithm, i.e., 
an algorithm that finds the minimum Euclidean 
distance from each vertex or point of a mesh (reference 
mesh) to the closest triangle on a different mesh 
(compared mesh), was used in CloudCompare (version 
2.12 alpha).  
dSurface and dBone were obtained by computing 
distances between the meshes acquired with the same 
modality at T0 and T1, i.e., scanT0 – scanT1 and 
CBCTT0 – CBCTT1, respectively. While mucosa 
thickness was obtained by computing distances 
between the meshes acquired with different modalities, 
i.e., scan – CBCT, either at T0 or T1, representing 
baseline and follow-up thickness. Computed distances 
 KURALT M ET AL.: Evaluation of mucosa thickness changes 
184 
 
were saved as vertex attributes, i.e., a scalar field value 
assigned to each vertex or point on the reference mesh.  
To obtain mucosa thickness changes (dMucosa), 
obtained scalar field values were further calculated 
using the “Arithmetic” algorithm in CloudComapare 
(Fig. 2). First, dBone was computed on a scanT0 as a 
reference mesh by subtracting scanT0 – CBCTT0 and 
scanT0 – CBCTT1 scalar fields. Second, dBone was 
subtracted from dSurface to obtain dMucosa displayed 
on scanT0 mesh.  
For further evaluation, a region of interest (ROI) 
was defined on a central residual ridge area, limited to 
a firmly attached masticatory mucosa with a width of 
four millimetres. Such ROI was defined due to the 
mobility of loosely attached alveolar lining mucosa in 
the denture foundation area, as previously reported by 
Kuralt et al. (Kuralt et al., 2019) (Fig. 2a). ROI was 
defined with surface curves in GOM Inspect. First, 
surface curve was created from mesial to distal edge on 
the top of the residual ridge. Then, an offset of two 
millimetres was used to create surface curves on each 
side of the residual ridge resulting in a four millimetres 
wide surface area. Edge surface curves were exported 
to CloudCompare, where the “Segment tool” was used 
for the actual ROI selection.  
Scalar field values were visualised with mapping 
values to colour using the transfer function. Two 
different colour maps were selected, i.e., linear Viridis 
to display mucosa thickness (Kuralt et al., 2020) and 
diverging rainbow to display tissue thickness changes 
(Kuralt et al., 2019). 
 
 
Fig. 2: A geometric approach with distance computation between superimposed multi-modal and temporal meshes, 
i.e., cone-beam computed tomography (CBCT) and optical scans (scan) from baseline (T0) and follow-up (T1). 
Distances represent either surface thickness changes (dSurface) (a), bone thickness changes (dBone) (b), and 
baseline or follow-up mucosa thickness (d and e) displayed with colour mapping, i.e., a transfer function assigning 
a colour for each distance value. For example, to obtain mucosa thickness changes (dMucosa) dBone is subtracted 
from dSurface for each point of the polygonal surface mesh (c). 
 
 
 
Image Anal Stereol 2022;41:181-191 
 
185 
 
VALIDATION OF THE PROPOSED EVAL-
UATION METHOD 
Four different analysis software and their imple-
mentation of the “cloud to mesh” distance computing 
algorithm were compared regarding mucosa thickness 
(n=20), dSurface (n=10), and dBone (n=10) on the 
defined ROI. Besides CloudCompare, GOM Inspect, 
MeshLab (version 2021.10), and 3D Slicer (version 
4.11.20210226) were used. The comparison was 
performed in CloudCompare by subtracting the scalar 
fields enabling point-to-point comparison. 
Additionally, a comparison of dBone computed on 
CBCTT0 and scanT0 reference mesh was made, 
representing a methodological error in dMucosa 
calculations.  
STATISTICAL ANALYSIS  
Statistical analysis was carried out using GraphPad 
Prism 9 (version 9.3.0, GraphPad Software). 
Descriptive statistics were obtained, including mean, 
standard deviations, median, minimum, and maximum 
values. In addition, the mean absolute differences 
between tested distance computation algorithms were 
assessed using the one-sample t-test to determine if 
mean differences were statistically different from zero. 
 
RESULTS 
DENTURE SUPPORTING TISSUES 
THICKNESS CHANGES 
The mean (and standard deviation) of minimum, 
median, and maximum dSurface per study sample of 
five mandibles with right and left residual ridge (n=10) 
was -1.29 mm (0.58), -0.47 mm (0.43), and 0.29 mm 
(0.48), respectively. For dBone, the results were -1.15 
mm (0.84), -0.44 mm (0.62), and 0.29 mm (0.49), 
respectively. And for dMucosa, the results were -1.32 
mm (0.60), 0.00 mm (0.35), and 0.88 mm (0.42).  
Distribution for each parameter and ROI per patient is 
displayed with colour-coded maps (Fig. 3) and 
boxplots (Fig. 4) 
MUCOSA THICKNESS 
The mean (and standard deviation) of minimum, 
median, and maximum baseline mucosa thickness per 
study sample was 1.26 mm (0.41), 2.41 mm (0.45), and 
3.50 mm (0.54), respectively. While, for follow-up 
mucosa thickness, the results were 1.10 mm (0.50), 
2.42 mm (0.51), and 3.42 mm (0.62), respectively. 
Distribution for each parameter and ROI per patient is 
displayed with colour-coded maps (Fig. 5) and 
boxplots (Fig. 6). 
VALIDATION OF THE PROPOSED METH-
OD 
The mean of the absolute difference between all 
tested distance computation algorithms for dSurface, 
dBone, and mucosa thickness was less than 0.0001 mm 
(p<0.001). The mean (and standard deviation) of 
absolute differences between dBone computed on 
CBCTT0 and scanT0 reference mesh was 0.06 mm 
(0.05). 
 
DISCUSSION 
The present study proposed a novel method for 
precise evaluation and visualisation of soft tissue 
thickness changes resulting from the acquisition of 
underlying bone changes and mucosa surface changes. 
Using CBCT and optical scanning, an innovative 
geometric approach is based on distance computation 
between surface meshes and scalar fields to enable 
three-dimensional evaluation. Four different software 
implementations of distance computation algorithms 
did not show any differences, thus validating their use. 
Furthermore, in a study sample, high local variability 
of mucosa changes associated with wearing a denture 
was found with a novel method, emphasising the 
importance of a novel method to understand better the 
dynamics of the denture supporting tissues. 
A novel geometric approach facilitated a compre-
hensive three-dimensional evaluation of mucosal 
changes. The geometric approach relies on distance 
computation between meshes and assigning distance 
values for each point of the reference mesh creating a 
scalar field. Scalar field values are utilised for further 
analysis, such as descriptive statistics of an ROI or 
visualisation using colour mapping. Colour mapping is 
an efficient way to display differences between the two 
surfaces. The novelty of the presented method was the 
calculation between multiple scalar fields assigned to 
one surface mesh. In such a way, dMucosa was 
obtained for each mesh point by subtracting dBone 
from dSurface scalar field. In contrast to the volumetric 
approach (Kuralt et al., 2019), the geometric approach 
can depict the spatial distribution or local variability of 
the scalar values of interest (Thiery et al., 2017). 
Colour scale selection and threshold definition are 
essential for detecting relevant variability (Crameri et 
al., 2020; Kuralt et al., 2020). The high variability of 
dMucosa observed on an individual level further 
emphasised the importance of the novel method. 
 KURALT M ET AL.: Evaluation of mucosa thickness changes 
186 
 
Additionally, the novelty of the presented method, i.e., 
scalar fields calculation, was also used to compare 
different software, revealing negligible differences 
between the software and confirming the method's 
robustness.  
Accurate superimposition of multi-modal and 
temporal images represents the first step toward three-
dimensional evaluation of tissue dynamics. The 
concept of virtual patient, i.e., combining multimodal 
images such as intraoral scanning, face scanning, and 
CBCT (Mangano et al., 2018; Shujaat et al., 2021; 
Unkovskiy et al., 2021), is a recent well-accepted 
clinical approach to treatment planning in dentistry. 
The concept was enhanced for research purposes in the 
present study, adding the fourth dimension, i.e., time. 
 
 
Fig. 3. Surface (dSurface) (a), bone (dBone) (b), and mucosa thickness changes (dMucosa) (c) were visualised on 
the right and left residual ridges for all patients included in the present study.  
Image Anal Stereol 2022;41:181-191 
 
187 
 
 
 
Fig. 4. Distribution of surface (dSurface) (a), bone (dBone) (b), and mucosa thickness changes (dMucosa) (c) of the 
right and left residual ridges for all patients (P) included in the present study were displayed with boxplots.  
 
 
 
 KURALT M ET AL.: Evaluation of mucosa thickness changes 
188 
 
 
 
 
Fig. 5. Baseline (a) and follow-up mucosa thickness (b) were visualised on the right and left residual ridges for all 
patients included in the present study. 
 
 
Fig. 6: Distribution of baseline (white) and follow-up mucosa thickness (grey) of the right and left residual ridges 
for all patients (P) included in the present study were displayed with boxplots. 
 
Image Anal Stereol 2022;41:181-191 
 
189 
 
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). Superimposition also presents a 
crucial step in defining the accuracy of the subsequent 
analysis (Flügge et al., 2017; Kuralt & Fidler, 2021). 
With careful selection of stable and unchanged regions, 
i.e., teeth for CBCT and optical scan alignment and 
anatomical structures for multitemporal CBCT 
alignment, the error of the evaluation method is 
minimised (Kuralt et al., 2019).  
To our knowledge, this was the first study to three-
dimensionally evaluate and visualise oral mucosa 
changes associated with wearing a denture independent 
of bone changes. Recently, there has been renewed 
interest in the biomechanics of the denture supporting 
tissues, clinically and theoretically (Chen et al., 2015). 
In the past, the predominant topic of interest was bone 
changes of the residual ridge, but recently 
understanding of the mucosal response has gained 
interest. With optical scanning alone, mucosa surface 
change (dSurface) is obtained, representing a mismatch 
between the intaglio surface of the denture and the 
denture supporting tissues, which leads to uneven 
distribution of occlusal forces and overstressing the 
abutment teeth (Saito et al., 2002). However, dSurface 
is a combination of bone and mucosa changes of the 
residual ridge. Therefore, a multi-modal and temporal 
approach combing CBCT and optical scanning is 
required to evaluate these changes. CBCT and optical 
scanning were already utilised in periodontology and 
implantology to quantify soft tissue thickness (Couso-
Queiruga et al., 2021; Kuralt et al., 2020), exhibiting 
the highest agreement with histological measurements 
and highest accuracy to the clinical measurements 
(Ferry et al., 2022). Nonetheless, evaluating soft tissue 
changes on the underlying bone, also exhibiting 
changes, remained problematic. Therefore, the novel 
method also presents an unexploited possibility for soft 
tissue evaluation in other fields, i.e. periodontology and 
implantology, for instance a correlation between 
mucosal thickness changes and periodontal phenotype, 
i.e. gingival thickness and keratinized tissue width, and 
bone morphotype (Cortellini & Bissada, 2018; Kim et 
al., 2020). 
Three-dimensional evaluation and visualisation of 
the denture supporting tissues revealed complex 
surface, bone, and mucosa dynamics. The previous 
findings with mucosa exhibiting complex nonlinear and 
time-dependent behaviours were confirmed in the 
present study. Bone changes exhibited around half a 
millimetre thickness loss predominantly of local nature 
except for the first patient displaying bone loss up to 
two millimetres. Large differences were also observed 
between the left and right residual ridge. Mucosa 
exhibited even higher local variability with both 
increasing and decreasing thickness. In this regard, 
mucosal compensatory effects were observed with 
increased mucosa thickness in large bone loss areas and 
decreased mucosa thickness in areas without bone 
changes. Such detailed evaluation would also greatly 
aid the planning and designing of RPDs to enable 
successful treatment outcomes in the future. 
The present study was subjected to some limita-
tions. First, despite reduced ionising radiation 
compared to medical computed tomography, CBCT 
imaging still presents a significant radiation dose to the 
patient (Jacobs et al., 2018). Therefore, the use of 
either novel low-dose protocols using CBCT (Yeung et 
al., 2019) or other non-invasive modalities, such as 
magnetic resonance imaging (MRI) (Heil et al., 2018), 
should be assessed further in this regard. Nonetheless, 
the proposed method can be utilised on surface meshes 
disregarding the acquisition modality. 
 
CONCLUSIONS 
The present study proposed a novel method for 
precise evaluation and visualisation of mucosa 
thickness changes resulting from the acquisition of 
underlying bone changes and mucosa surface changes. 
 
ACKNOWLEDGEMENT 
The work was supported by the Ministry of Higher 
Education, Science and Technology of the Republic of 
Slovenia, under grant number P3-0293. The authors 
deny any conflicts of interest related to this study. 
 
REFERENCES 
Ahmad R, Abu-Hassan MI, Li Q, Swain MV (2013). 
Three dimensional quantification of mandibular bone 
remodeling using standard tessellation language 
registration based superimposition. Clin Oral Implan 
Res 24:1273–9.  
 KURALT M ET AL.: Evaluation of mucosa thickness changes 
190 
 
Alammar A, Kois JC, Revilla-León M, Att W (2022). 
Additive Manufacturing Technologies: Current Status 
and Future Perspectives. J Prosthodont 31:4–12.  
Benso B, Kovalik AC, Jorge JH, Campanha NH (2013). 
Failures in the rehabilitation treatment with 
removable partial dentures. Acta Odontol Scand 
71:1351–5.  
Blum IR, Fraser McCord J (2004). A clinical 
investigation of the morphological changes in the 
posterior mandible when implant-retained overden-
tures are used. Clin Oral Implan Res 15:700–8.  
Campbell SD, Cooper L, Craddock H, Hyde TP, Nattress 
B, Pavitt SH, Seymour DW (2017). Removable 
partial dentures: The clinical need for innovation. J 
Prosthet Dent 118:273–80.  
Chen J, Ahmad R, Li W, Swain M, & Li, Q (2015). 
Biomechanics of oral mucosa. J Roy Soc Interface 
12:20150325.  
Cook RJ (1991). Response of the oral mucosa to denture 
wearing. J Dent 19:135–47.  
Cortellini P, Bissada N F (2018). Mucogingival 
conditions in the natural dentition: Narrative review, 
case definitions, and diagnostic considerations. J Clin 
Periodontol 45:S190–8.  
Couso-Queiruga E, Tattan M, Ahmad U, Barwacz C, 
Gonzalez-Martin O, Avila-Ortiz G (2021). Assess-
ment of gingival thickness using digital file 
superimposition versus direct clinical measurements. 
Clin Oral Invest 25:2353–61.  
Crameri F, Shephard GE, Heron PJ (2020). The misuse 
of colour in science communication. Nat Commun 
11:1–10.  
Dong J, Zhang FY, Wu GH, Zhang W, Yin J (2015). 
Measurement of mucosal thickness in denture-bearing 
area of edentulous mandible. Chinese Med J-Peking 
128:342–7.  
Douglass CW, Watson AJ (2002). Future needs for fixed 
and removable partial dentures in the United States. J 
Prosthet Dent 87:9–14.  
Ferry K, AlQallaf H, Blanchard S, Dutra V, Lin WS, & 
Hamada Y (2022). Evaluation of the accuracy of soft 
tissue thickness measurements with three different 
methodologies: An in vitro study. J Periodontol. 
Flügge T, Derksen W, te Poel J, Hassan B, Nelson K, 
Wismeijer D (2017). Registration of cone beam 
computed tomography data and intraoral surface 
scans – A prerequisite for guided implant surgery 
with CAD/CAM drilling guides. Clin Oral Implan 
Res 28:1113–8.  
Fontenele RC, Gerhardt MN, Pinto JC, Van Gerven A, 
Willems H, Jacobs R, Freitas DQ (2022). Influence of 
dental fillings and tooth type on the performance of a 
novel artificial intelligence-driven tool for automatic 
tooth segmentation on CBCT images – A validation 
study. J Dent 119:104069.  
Friedli L, Kloukos D, Kanavakis G, Halazonetis D, 
Gkantidis N (2020). The effect of threshold level on 
bone segmentation of cranial base structures from CT 
and CBCT images. Sci Rep-UK 10:7361.  
Gaêta-Araujo H, Alzoubi T, de Faria Vasconcelos K, 
Orhan K, Pauwels R, Casselman JW, Jacobs,R 
(2020). Cone beam computed tomography in 
dentomaxillofacial radiology: a two-decade overview. 
Dentomaxillofac Rad 49:20200145.  
Hassan B, Souza PC, Jacobs R, de Azambuja Berti S,van 
der Stelt P (2010). Influence of scanning and 
reconstruction parameters on quality of three-
dimensional surface models of the dental arches from 
cone beam computed tomography. Clin Oral Invest 
14:303–10.  
Heil A, Schwindling FS, Jelinek C, Fischer M, Prager M, 
Gonzalez EL, Bendszus M, Heiland S, Hilgenfeld T 
(2018). Determination of the palatal masticatory 
mucosa thickness by dental MRI: A prospective study 
analysing age and gender effects. Dentomaxillofac 
Rad 47:20170282.  
Jacobs R, Salmon B, Codari M, Hassan B, & Bornstein 
MM (2018). c. BMC Oral Health 18:88.  
Joda T, Yeung AWK, Hung K, Zitzmann NU, Bornstein 
MM (2020). Disruptive Innovation in Dentistry: What 
It Is and What Could Be Next. J Dent Res 100:448-
53. 
Khamis Abdel Razek M, Shaaban NA (1978). 
Histochemical and histopathologic studies of alveolar 
mucosa under complete dentures. J Prosthet Dent 
39:29–36.  
Kim DM, Bassir SH, Nguyen TT (2020). Effect of 
gingival phenotype on the maintenance of periodontal 
health: An American Academy of Periodontology 
best evidence review. J Periodontol 91:311–38.  
Kumar L (2014). Biomechanics and clinical implications 
of complete edentulous state. J Clin Gerontol Geriatr 
5:101–4.  
Kuralt M, Gašperšič R, Fidler A (2020). 3D computer-
aided treatment planning in periodontology: A novel 
approach for evaluation and visualization of soft 
tissue thickness. J Esthet Restor Dent 32:457–62.  
Kuralt M, Fidler A (2021). Assessment of reference areas 
for superimposition of serial 3D models of patients 
with advanced periodontitis for volumetric soft tissue 
evaluation. J Clin Periodontol 48:765–73.  
Kuralt M, Selmani Bukleta M, Kuhar M, Fidler, A 
(2019). Bone and soft tissue changes associated with 
a removable partial denture. A novel method with a 
fusion of CBCT and optical 3D images. Comput Biol 
Med 108:78–84.  
Image Anal Stereol 2022;41:181-191 
 
191 
 
Lahoud P, Jacobs R, Boisse P, EzEldeen M, Ducret M, & 
Richert R (2022). Precision medicine using patient-
specific modelling: state of the art and perspectives in 
dental practice. Clin Oral Invest 26:5117-28. 
Mangano C, Luongo F, Migliario M, Mortellaro C, 
Mangano FG (2018). Combining intraoral scans, cone 
beam computed tomography and face scans: The 
virtual patient. J Craniofac Surg 29:2241–6.  
Mangano F, Gandolfi A, Luongo G, Logozzo S (2017). 
Intraoral scanners in dentistry: A review of the 
current literature. BMC Oral Health 17:1–11.  
Najeeb S, Zafar MS, Khurshid Z, Siddiqui F (2016). 
Applications of polyetheretherketone (PEEK) in oral 
implantology and prosthodontics. J Prosthodont Res 
60:12–9.  
Ozan O, Orhan K, Aksoy S, Icen M, Bilecenoglu B, 
Sakul BU (2013). The Effect of Removable Partial 
Dentures on Alveolar Bone Resorption: A Retrospec-
tive Study with Cone-Beam Computed Tomography. 
J Prosthodont 22:42–8.  
Pauwels R, Jacobs R, Singer SR, Mupparapu M (2015). 
CBCT-based bone quality assessment: are Hounsfield 
units applicable? Dentomaxillofac Rad 44:20140238.  
Piedra-Cascón W, Krishnamurthy VR, Att W, Revilla-
León M (2021). 3D printing parameters, supporting 
structures, slicing, and post-processing procedures of 
vat-polymerization additive manufacturing technolo-
gies: A narrative review. J Dent 109:103630.  
Preshaw PM, Walls AWG, Jakubovics NS, Moynihan PJ, 
Jepson NJA, Loewy Z (2011). Association of 
removable partial denture use with oral and systemic 
health. J Dent 39:711–9.  
Ramsay SE, Whincup PH, Watt RG, Tsakos G, 
Papacosta AO, Lennon LT, Wannamethee SG (2015). 
Burden of poor oral health in older age: Findings 
from a population-based study of older British men. 
BMJ Open 5:e009476.  
Revilla-León M, Sadeghpour M, Özcan M (2020). A 
Review of the Applications of Additive Manufactur-
ing Technologies Used to Fabricate Metals in Implant 
Dentistry. J Prosthodont 29:579–93.  
Saito M, Notani K, Miura Y, Kawasaki T (2002). 
Complications and failures in removable partial 
dentures: A clinical evaluation. J Oral Rehabil, 
29:627–33.  
Schulze R, Heil U, Groß D, Bruellmann DD, 
Dranischnikow E, Schwanecke U, Schoemer E 
(2011). Artefacts in CBCT: A review. In Dentomaxil-
lofac Rad 40:265–73.  
Schwendicke F, Krois J (2022). Precision dentistry—
what it is, where it fails (yet), and how to get there. 
Clin Oral Invest 26:3395–403.  
Shaheen E, Leite A, Alqahtani KA, Smolders A, Van 
Gerven A, Willems H, Jacobs R (2021). A novel deep 
learning system for multi-class tooth segmentation 
and classification on cone beam computed tomogra-
phy. A validation study: Deep learning for teeth 
segmentation and classification. J Dent 115:103865.  
Shujaat S, Bornstein MM, Price JB, Jacobs R (2021). 
Integration of imaging modalities in digital dental 
workflows - possibilities, limitations, and potential 
future developments. Dentomaxillofac Rad 
50:20210268.  
Srinivasan M, Kalberer N, Fankhauser N, Naharro M, 
Maniewicz S, Müller F (2021). CAD-CAM complete 
removable dental prostheses: A double-blind, 
randomized, crossover clinical trial evaluating milled 
and 3D-printed dentures. J Dent 115:103842.  
Srinivasan M, Kalberer N, Kamnoedboon P, Mekki M, 
Durual S, Özcan M, Müller F (2021). CAD-CAM 
complete denture resins: an evaluation of biocompati-
bility, mechanical properties, and surface characteris-
tics. J Dent 114:103785.  
Tallgren A (2003). The continuing reduction of the 
residual alveolar ridges in complete denture wearers: 
A mixed-longitudinal study covering 25 years. J 
Prosthet Dent 89:427–35.  
Thiery G, Lazzari V, Ramdarshan A, Guy F (2017). 
Beyond the map: Enamel distribution characterized 
from 3D dental topography. Front Physiol 8:524.  
Unkovskiy A, Unkovskiy N, Spintzyk S (2021). A virtual 
patient concept for esthetic and functional rehabilita-
tion in a fully digital workflow. Int J Comput Dent 
24:405–17.  
Verhelst PJ, Smolders A, Beznik T, Meewis J, 
Vandemeulebroucke A, Shaheen E, Van Gerven A, 
Willems H, Politis C, Jacobs R (2021). Layered deep 
learning for automatic mandibular segmentation in 
cone-beam computed tomography. J Dent 
114:103786.  
Yeung AWK, Jacobs R, Bornstein MM (2019). Novel 
low-dose protocols using cone beam computed 
tomography in dental medicine: a review focusing on 
indications, limitations, and future possibilities. Clin 
Oral Invest 23:2573–81.  
Żmudzki J, Chladek G, Kasperski J (2015). 
Biomechanical factors related to occlusal load 
transfer in removable complete dentures. Biomech 
Model Mechan 14:679–91.