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Comparing the Capacity of Intraoral Scanner and
Expert Scoring in Detecting Potential Deviations in
Guided Endodontics: An In Vitro Study

Pauline Binvignat, Marine Hénaut, Lieven Robberecht, Davide Mancino, Naji

Kharouf, Jean-Christophe Maurin, Cyril Villat, Maxime Ducret, Raphael

Richert

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To cite this version:

Pauline Binvignat, Marine Hénaut, Lieven Robberecht, Davide Mancino, Naji Kharouf, et al. Comparing the Capacity of Intraoral Scanner and Expert Scoring in Detecting Potential Deviations in Guided Endodontics: An In Vitro Study. Journal of Endodontics, In press, 10.1016/j.joen.2025.02.011. hal-05058043

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Submitted on 6 May 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Comparing the Capacity of Intraoral Scanner and Expert Scoring in Detecting Potential Deviations in Guided Endodontics: An In Vitro Study

Pauline Binvignat, MD, *†
Marine Hénaut, MD, DDS, ‡§
Lieven Robberecht, PhD,
DDS, ‡§ Davide Mancino, PhD,
DDS, ||¶ Naji Kharouf, PhD,
DDS, ||¶ Jean-
Christophe Maurin, PhD,
DDS, *†# Cyril Villat, PhD,
DDS, *†** Maxime Ducret, PhD,
DDS, *†# and Raphael Richert,
PhD, DDS*†††

## ABSTRACT

**Introduction:** Guided endodontics recently offers a conservative solution for conducting root canal treatment in cases of obliterated canals, but guide mispositioning remains a challenge. Recently, intraoral scanners (IOS) were proposed to detect deviations, but their effectiveness compared to expert visual assessments during guide insertion is uncertain. The objective of the present study was to primarily compare the risk of deviation predicted by an IOS vs expert assessment and secondarily identify factors influencing this risk. **Methods:** Using a design of experiments approach, 16 endodontic guides were 3D-printed to assess the influence of guide thickness, internal offset, number of supporting teeth, and presence of windows. Guide positioning was evaluated digitally with the Trios 4 IOS to calculate angular deviation and visually by 10 experts using a Likert scale. The overall agreement between the value of angular deviation (less than or greater than 2°) and the expert score (positive or negative) was calculated. **Results:** The mean angular deviation was 4.32° (SD = 2.40°) and the mean expert score was -0.29 (SD = 1.39). Angular deviation was most influenced by greater guide thickness (41.4%) and internal offset (27.0%), which both increased it. The most influential factor for expert scores was internal offset (90.5%), which decreased ratings. Agreement between IOS and expert scores averaged 60.6% (SD = 39.1%), with the highest agreement for guides with a high internal offset. **Conclusions:** Experts effectively detected positioning errors with high internal offset but struggled with other factors where IOS was more accurate. IOS shows promising potential for improving guide fitting in clinical practice. (J Endod 2025; ■:1-7.)

## KEY WORDS

3D printed template; accuracy; computer-aided design; guided endodontics; intraoral scanner; root canal preparation

Pulp canal obliteration is reported to occur between 4% and 24% of traumatized teeth, mostly on anterior teeth¹. This phenomenon is characterized by the deposition of calcified tissue, and leads to an extremely challenging treatment even under an operating microscope¹; this, in turn, leads to frequent canal deviation during the endodontic preparation and can ultimately give rise to root perforations¹. Recently, the novel concept of "guided endodontics" was proposed to help clinicians prepare the access cavity and root canal in a more conservative way²,³. This procedure is based on an ideal access cavity virtually planned and subsequently reproduced in the clinic using a physical guide or navigation system⁴. A mean angular deviation of 1.9° for physical guides and 2.9° for navigation systems has been reported in vitro using cone beam computed tomography (CBCT)³,⁵; however, root canal perforation has been reported despite use of a physical guide indicating root canal deviation⁶.

Numerous factors could lead to an inaccurate fitting of the guide, such as imprecise model or 3D printing⁷⁻¹¹. Visual control on windows of the printed guide has been proposed to avoid canal deviations

## SIGNIFICANCE

This new application of intraoral scan shows promising potential in clinical practice to assist practitioners during the fitting of guides.

From the *Faculté d'Odontologie, Université Claude Bernard Lyon 1, Lyon, France; †Hospices Civils de Lyon, Lyon, France; ‡Faculty of Odontology, University Lille, Lille, France; §Inserm U1008, University Lille, Lille, France; ||Faculty of Dental Medicine, Strasbourg University, Strasbourg, France; ¶Department of Biomaterials and Bioengineering, UMR_S 1121, Strasbourg, Strasbourg, France; #Laboratoire de Biologie Tissulaire et Ingénierie Thérapeutique, UMR 5305 CNRS/UCBL, Lyon, France; **Laboratoire des Multimatériaux et Interfaces, UMR 5615, Lyon, France; and ††INSA Lyon, CNRS, LaMCoS, UMR5259, Villeurbanne, France

Address requests for reprints to Raphael Richert, PhD, DDS, INSA Lyon, CNRS, LaMCoS, UMR5259, Bâtiment Sophie Germain, 27 bis Av. Jean Capelle O, 69100 Villeurbanne, France.
E-mail address: raphael.richert@insa-lyon.fr
0099-2399

Copyright © 2025 The Authors. Published by Elsevier Inc. on behalf of American Association of Endodontists. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://doi.org/10.1016/j.joen.2025.02.011
<figure><img src="image_4.png" /><figcaption>**FIGURE 1** – Experiment workflow followed in the study with occlusal views of (A) the study model, and (B) the ideal endodontic guide (blue) with the red arrow indicating the access cavity hole and the white arrows indicating the window holes, (C) printing of the test guides following the design of experiment (red arrow indicating access cavity hole and the white arrows indicating the window holes), (D) expert scoring of the fitting of each test guide on the study model, and (E) angular deviation α between the positioning of the ideal endodontic guide (blue) and the test guide (green) using intraoral scans.¹⁶</figcaption></figure>

in the clinic. However, there is a high risk for a subjective clinical assessment since the practitioner might focus on a single parameter based on their own experience rather than considering all potential factors. To evaluate this *in vitro*, CBCT has been used but only provides a postoperative evaluation once there is canal deviation. Recently, the intraoral scanner (IOS) has been used for various assessment applications, such as evaluating the postoperative implant positioning¹², or the fit accuracy of digitally manufactured crowns¹³, but also to monitor dental changes due to tooth wear progression¹⁴ or tooth displacement following the use of a mandibular advancement orthosis¹⁵. In the context of root canal treatments, IOS has also been proposed as an innovative tool for detecting errors of positioning of the endodontic guide and for predicting potential drilling deviations¹⁶. It is, however, unclear whether this device is truly necessary for an objective assessment, or if visual assessment using windows is sufficient to predict errors.

<table><caption>**TABLE 1** - Investigated Parameters and Associated Levels</caption><thead><tr><th>Investigated factor</th><th>Level -1</th><th>Level +1</th></tr></thead><tbody><tr><td>Guide thickness</td><td>1.2 (mm)</td><td>3 (mm)</td></tr><tr><td>Internal offset</td><td>0.2 (mm)</td><td>2 (mm)</td></tr><tr><td>Number of supporting teeth</td><td>Tooth 13–22</td><td>Tooth 15–25</td></tr><tr><td>Presence or absence of Windows</td><td>Absence</td><td>Presence</td></tr></tbody></table>

The primary objective of this study was to compare the risk of deviation during the preparation of the endodontic access cavity as predicted digitally by an IOS with that assessed visually by experienced dental practitioners. The null hypothesis of the study was that there would be no significant differences between these 2 methods. The secondary objective was to classify the factors influencing this risk predicted by the IOS and assessed by expert scoring.

## MATERIALS AND METHODS

### Study Model

A pre-existing educational maxillary model was used, created from 14 human teeth using a previously reported methodology³,¹⁶. The model was scanned using the Planmeca ProMax 3D CBCT scanner (Planmeca, Helsinki, Finland) and a highly accurate reference laboratory scanner, the Medit T500 (Medit, Seoul, Korea) to create a digital reference dataset, as described previously (Fig. 1A)¹⁶. The DICOM and the STL files were uploaded into the BlueskyPlan planning software (BlueskyBio, Grayslake, IL, USA) and 6 reference points (distopalatal cusp tip of the left and right first molars, cusp tip the left and right canines, mesial corner of the incisal edge of the left and right central incisors) were chosen to align the STL file on the segmented surface of the CBCT following a published protocol¹⁷. The software allowed the creation of a virtual image of a commercially available drill that had a diameter of 1 mm (Stecco-system-technik GmbH & Co KG, Hamburg, Germany). The virtual drill was superimposed on the right central incisor with the aim of creating a direct access to the median third of

the root canal and the position of the drill was checked in the 3 dimensions.

### Design of Experiments

A full factorial design of experiments approach was then used to compare the influence of 4 factors on the guide positioning: guide thickness, internal offset, number of supporting teeth, and presence or absence of windows. Two extreme levels were defined for each factor and were encoded as [-1; +1]. In this approach, the main effect of a factor represents the variation in guide positioning when the factor changes from its lower level (-1) to its upper level (+1). The interaction effect refers to how the combined influence of ≥2 factors differs from the sum of their individual effects. An orthogonal array was created to model the 16 possible combinations according to the level of each factor, leading to the fabrication of 16 test guides (Table 1).

### Conception and Fabrication

A drill position was planned to perform the access cavity on the right central incisor and subsequently 16 test guides were designed (Fig. 1B). The completed STL files of the 16 test guides were then marked with a random number, and uploaded to the computer-aided manufacturing software BlueskyPlan (BlueskyBio, Grayslake, IL).

The test guides were printed using a Formlabs 3B 405 nm wavelength 3D printer and associated printable resin (Model resin V2; Formlabs, Somerville, MA, USA; Fig. 1C). The layer thickness was 50 µm and supports were oriented with a 100-degree angle. After impression, the device was cleaned twice
<table><caption>TABLE 2 - Mean and Standard Deviation of Angular Deviation α (°) and Expert Scores for the Investigated Factors</caption><thead><tr><th>Investigated factor</th><th>Level -1 (α)</th><th>Level +1 (α)</th><th>Level -1 (expert score)</th><th>Level +1 (expert score)</th></tr></thead><tbody><tr><td>Guide thickness</td><td>3.17 ± 1.62</td><td>5.48 ± 2.58</td><td>-0.23 ± 1.44</td><td>-0.22 ± 1.45</td></tr><tr><td>Internal offset</td><td>3.58 ± 1.87</td><td>5.08 ± 2.75</td><td>-1.09 ± 0.40</td><td>-1.54 ± 0.24</td></tr><tr><td>Number of supporting teeth</td><td>3.84 ± 2.88</td><td>4.82 ± 1.88</td><td>-0.11 ± 1.60</td><td>-0.34 ± 1.25</td></tr><tr><td>Presence or absence of Windows</td><td>4.72 ± 3.18</td><td>3.93 ± 1.38</td><td>-0.20 ± 1.56</td><td>-0.25 ± 1.31</td></tr></tbody></table>

using isopropyl alcohol before a final ultrasonic isopropyl alcohol bath (Form wash; Formlabs) for 20 minutes. Final polymerization was performed for 60 minutes using a temperature and light-controlled environment (Form cure; Formlabs).

## Scan Protocol and Angular Deviation (α)

The fitting of each test guide on the study model was visually checked using windows⁶ and both the study model and the test guide were scanned using a Trios 4 IOS (3Shape, Copenhagen, Denmark) by an experimented operator according to the manufacturer's recommendations. All digital impressions were exported as STL files into the Geomagic Design X 2017.1.1 metrology software (3D systems, Rock Hill, SC, USA). The impressions were aligned on the master model using the "reference alignment method" with all segmented surfaces except for the ones of the test guide¹⁸. The angular deviation α of the endodontic preparation was assessed by comparing the axis of the test guide tube on each impression and the ideal guide following a previous protocol³,¹⁶ (Fig. 1D).

## Expert Scoring

The test guides were then submitted to a panel of 10 experts. The panel included 7 educators from the Department of Restorative Dentistry and Endodontics and 3 private Endodontists, all had already used endodontic guides in the clinic and/or conducted research on this subject. Each test guide was presented successively to the expert in a random order, determined using Excel's random function (Microsoft Corporation, Redmond, WA, USA). The experts were orally instructed to evaluate the accuracy of each test guide using a Likert scale, giving a score from -2 to +2 (-2 = strongly inaccurate; -1 = inaccurate; +1 = accurate; +2 = strongly accurate) for each positioning (Fig. 1E).

## Correlation and Overall Agreement

The correlation between the angular deviation (α) and the score given by each practitioner (expert score) was calculated using Pearson's correlation coefficient. In addition, the overall agreement between the angular deviation (less than or greater than 2°,⁴) and the value of the expert score (positive or negative) was calculated, that is the occurrence of an angular deviation less than 2° and a positive score or an angular deviation greater than 2° and a negative score.

## Data Analysis

Data were evaluated for normality using the Kolmogorov-Smirnov test. The mean and standard deviation (SD) were calculated for deviations predicted by the IOS and assessed by expert scoring for each level (-1 or +1, each n = 8) of guide thickness, internal offset, number of supporting teeth, and presence or absence of windows. The influence of the 4 factors on the outcome, either angular deviation (α) or expert score, was assessed using the design of experiments approach, and the results were visualized using a Pareto diagram. The overall agreement was calculated per guide and per expert.

## RESULTS

The overall mean angular deviation α was 4.32° (SD = 2.40°). A greater guide thickness, internal offset, and number of supporting teeth increased the angular deviation, whereas presence of windows decreased the angular deviation (Table 2). The factors that influenced the most the angular deviation α was the guide thickness (41.4%) and the internal offset (27.0%, Fig. 2A). The highest interaction between factors was between the internal offset and the number of supporting teeth and represented 13.4% of the influence (Supplemental Fig. S1). The guide combining a greater guide thickness, a larger internal offset, a decreased number of supporting teeth, and

no window had the highest angular deviation (Supplemental Table S1).

The overall mean expert score was -0.29 (SD = 1.39). The internal offset decreased the expert score (Table 2). The factor that influenced the most the expert score was the internal offset (90.5%, Fig. 2B). The highest interaction between factors for the expert score was between the internal offset and the number of supporting teeth and represented 8.0% of the influence (Supplemental Fig. S2). The guide combining a greater guide thickness, a larger internal offset, an increased number of supporting teeth, and windows had the lowest expert score (Supplemental Table S2).

There was no correlation between the angular deviation predicted by IOS and the expert score (for all experts; *P* > .05). From the point of view of the experts, the mean overall agreement was 60.6% (SD = 11.8%), ranging from 37.5% to 81.2% (Supplemental Fig. S3). From the point of view of the guide, the mean overall agreement was 60.6% (SD = 39.1%), ranging from 10% to 100%; the overall agreement was higher for guides with high internal offset compared to those with a low internal offset (Fig. 2C).

## DISCUSSION

The present study found that experts were able to detect defective positioning due to a high internal offset but not when this is caused by other factors.

The results of the present study confirm the ability of an IOS to evaluate the potential mispositioning of the printed guide¹⁶, and, notably, that guide thickness, internal offset, and number of supporting teeth increased the angular deviation. These factors are also associated with greater rigidity of the guide¹⁰,¹⁹, potentially leading to more friction during the positioning of the guide. The IOS could constitute a predictable way to avoid mistakes before any drilling in routine practice,
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** – Classification of influences in descending order for (A) the angular deviation (α) and (B) expert score, and (C) overall agreement between the angular deviation and the expert score for each test guide. *Black bars* indicate test guides with a high internal offset (*white bars* with low internal offset).</figcaption></figure>

but clinical trials are now required to confirm
the validity of the results presented herein.
Another potential direction for future research
could be the evaluation of an IOS to accurately
capture the axis of drilling by scanning the
prepared pathway in the coronal third, as IOS
has good *in vitro* accuracy for detecting root
canal entries in scanned access cavities<sup>20,21</sup> or
postpreparations up to 14 mm in depth<sup>22,23</sup>. It
should however be noted that this high
accuracy was achieved for post preparations
with diameters above 2.2 mm and that root
canal preparations often involve narrower
diameters. Beyond the limitations of IOS, an
alternative approach could be the use of
extraoral cameras to enable dynamic

navigation or robot-assisted endodontics²⁴⁻²⁶.
These systems allow real-time procedural
guidance with similar accuracy, even in narrow
root canal entries²⁷. Despite their potential,
such technologies still require additional
equipment, which remains costly and not yet
widely implemented in routine practice. The
present protocol could constitute a cost-
effective alternative for evaluating guide
positioning preoperatively, reducing the risk of
misalignment before any drilling occurs.

Regarding expert judgment, the
internal offset was the most influential factor
affecting expert scoring. This factor is
reported as being highly influential for the risk
of imprecise fitting²⁸ or angular deviation²⁹.

However, numerous other factors can
impact angular deviation, raising the
question of whether experts tend to focus on
a single parameter. In the present study,
experts had a limited capacity to discriminate
potential deviation when the internal offset
was low. This might be explained by the fact
that a low internal offset is frequently
associated with friction during the
positioning of the guide, resulting in good
stability. Consequently, experts might
struggle to visually detect a small change of
2° in the angular deviation, which might only
be detectable using an IOS. This limitation
might have clinical consequences as almost
half of the guides would have been
considered appropriate based on expert
scoring alone, despite having an angular
deviation over 2°, posing a potential risk of
root canal deviation.

It is particularly important to
acknowledge the limitations of the present
study, which are related to the procedures
employed. Of note, the angular deviation was
predicted by the IOS based solely on the
positioning of the guide, without performing
the actual drilling procedure. This could
however result in a higher angular deviation
than anticipated notably due to eccentric
forces accidentally applied during the drilling³⁰.
A greater guide sleeve length significantly
increases the risk of angular deviations and
potential root perforation¹⁶ and this effect is
further amplified by additional factors, such as
a larger guide diameter, bur diameter, and the
use of slow-speed drilling¹⁰,¹¹. While these
factors have been primarily investigated for the
procedure of implant surgery within bone
tissue¹⁰,¹¹, their specific impact and
interactions in dental tissue remain to be
precisely evaluated. Furthermore, various
factors that could complicate scanning were
not considered, such as guide translucency,
as well as clinical parameters such as fogging
or limited mouth opening. The scanning and
following positioning assessment may be even
more challenging for endodontic surgery
guides, as their design must precisely account
for bone anatomy³¹,³². A more general point
concerns the use of Computer-Aided Design
and Computer-Aided Manufacturing software
and was time-consuming, posing challenges
for routine clinical application; future research
should explore ways to facilitate the ergonomic

and automatic integration of this process into
dedicated software to enable simplified clinical
use and evaluation.

CONCLUSION

The present study found that experts had a
good capacity to discriminate positioning
errors when the internal offset was high but
had limited capacity in other cases, where the
IOS performed better. This new application of
IOS shows promising potential in clinical
practice to assist practitioners during the fitting
of guides.

DECLARATION OF GENERATIVE
AI AND AI-ASSISTED
TECHNOLOGIES IN THE
WRITING PROCESS

During the preparation of this work, the
authors used Le Chat (Mistral AI) to improve
language and readability, the authors reviewed
and edited the content as needed and take full
responsibility for the content of the publication.

CREDIT AUTHORSHIP
CONTRIBUTION STATEMENT

**Pauline Binvignat:** Data curation, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. **Marine Hénaut:** Conceptualization, Methodology, Project administration, Software, Supervision, Writing – review & editing. **Lieven Robberecht:** Conceptualization, Methodology, Project administration, Software, Supervision, Writing

- review & editing. **Davide Mancino:** Conceptualization, Methodology, Project administration, Software, Supervision, Writing – review & editing. **Naji Kharouf:** Conceptualization, Methodology, Project administration, Software, Supervision, Writing – review & editing. **Jean-Christophe Maurin:** Formal analysis, Investigation, Software, Writing – review & editing. **Cyril Villat:** Formal analysis, Investigation, Software, Writing – review & editing. **Maxime Ducret:** Conceptualization, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. **Raphael Richert:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing.

ACKNOWLEDGMENTS

The authors would like to thank Giuseppe
Carrieri and Faouzia Boussetta (both private
practitioners in endodontics) for their
evaluation of the guides. The authors would
like to thank Philip Robinson (PhD; Hospices
Civils de Lyon, France) for help in manuscript
preparation. The authors deny any conflicts of
interest related to this study.

SUPPLEMENTARY MATERIAL

Supplementary material associated with this
article can be found in the online version at
www.jendodon.com (https://doi.org/10.1016/
j.joen.2025.02.011)

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SUPPLEMENTARY MATERIAL

<table><caption>SUPPLEMENTAL TABLE S1 - Orthogonal Matrix for the Full Factorial Design of Experiment with the Influence of 4 Factors on the Expert Score and Angular Deviation (α)</caption><thead><tr><th>n°</th><th>Guide thickness</th><th>Internal offset</th><th>Number of supporting teeth</th><th>Presence/absence of windows</th><th>Expert score</th><th>Angular deviation (°)</th></tr></thead><tbody><tr><td>8</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>1.6</td><td>3.54</td></tr><tr><td>11</td><td>-1</td><td>-1</td><td>-1</td><td>+1</td><td>1.4</td><td>2.35</td></tr><tr><td>5</td><td>-1</td><td>-1</td><td>+1</td><td>-1</td><td>0.6</td><td>0.57</td></tr><tr><td>3</td><td>-1</td><td>-1</td><td>+1</td><td>+1</td><td>0.7</td><td>4.00</td></tr><tr><td>13</td><td>-1</td><td>+1</td><td>-1</td><td>-1</td><td>-1.6</td><td>2.36</td></tr><tr><td>16</td><td>-1</td><td>+1</td><td>-1</td><td>+1</td><td>-1.8</td><td>2.11</td></tr><tr><td>7</td><td>-1</td><td>+1</td><td>+1</td><td>-1</td><td>-1.3</td><td>5.42</td></tr><tr><td>9</td><td>-1</td><td>+1</td><td>+1</td><td>+1</td><td>-1.4</td><td>4.99</td></tr><tr><td>1</td><td>+1</td><td>-1</td><td>-1</td><td>-1</td><td>1.4</td><td>2.72</td></tr><tr><td>15</td><td>+1</td><td>-1</td><td>-1</td><td>+1</td><td>1.1</td><td>3.34</td></tr><tr><td>14</td><td>+1</td><td>-1</td><td>+1</td><td>-1</td><td>1.3</td><td>6.75</td></tr><tr><td>4</td><td>+1</td><td>-1</td><td>+1</td><td>+1</td><td>0.6</td><td>5.29</td></tr><tr><td>2</td><td>+1</td><td>+1</td><td>-1</td><td>-1</td><td>-1.7</td><td>10.82</td></tr><tr><td>12</td><td>+1</td><td>+1</td><td>-1</td><td>+1</td><td>-1.3</td><td>3.44</td></tr><tr><td>10</td><td>+1</td><td>+1</td><td>+1</td><td>-1</td><td>-1.9</td><td>5.56</td></tr><tr><td>6</td><td>+1</td><td>+1</td><td>+1</td><td>+1</td><td>-1.3</td><td>5.91</td></tr></tbody></table>

The extreme levels are encoded as [-1; +1].

<table><caption>SUPPLEMENTAL TABLE S2 - Scoring for Each Expert using a Likert Scale and Mean Expert Score used as Outcome for the Design of Experiment</caption><thead><tr><th>n°</th><th>Expert 1</th><th>Expert 2</th><th>Expert 3</th><th>Expert 4</th><th>Expert 5</th><th>Expert 6</th><th>Expert 7</th><th>Expert 8</th><th>Expert 9</th><th>Expert 10</th><th>Mean</th></tr></thead><tbody><tr><td>1</td><td>1</td><td>0</td><td>2</td><td>2</td><td>2</td><td>1</td><td>1</td><td>2</td><td>2</td><td>1</td><td>1.4</td></tr><tr><td>2</td><td>-2</td><td>-2</td><td>-1</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>0</td><td>-2</td><td>-2</td><td>-1.7</td></tr><tr><td>3</td><td>1</td><td>0</td><td>1</td><td>-1</td><td>1</td><td>2</td><td>0</td><td>0</td><td>1</td><td>2</td><td>0.7</td></tr><tr><td>4</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>-1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0.6</td></tr><tr><td>5</td><td>0</td><td>0</td><td>-2</td><td>1</td><td>2</td><td>-1</td><td>1</td><td>1</td><td>2</td><td>2</td><td>0.6</td></tr><tr><td>6</td><td>-2</td><td>-2</td><td>2</td><td>-2</td><td>-1</td><td>-2</td><td>-2</td><td>-1</td><td>-1</td><td>-2</td><td>-1.3</td></tr><tr><td>7</td><td>-2</td><td>-1</td><td>-2</td><td>-2</td><td>0</td><td>-2</td><td>-2</td><td>0</td><td>-1</td><td>-1</td><td>-1.3</td></tr><tr><td>8</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>0</td><td>2</td><td>1</td><td>2</td><td>1</td><td>1.6</td></tr><tr><td>9</td><td>-1</td><td>-1</td><td>1</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-1</td><td>-1.4</td></tr><tr><td>10</td><td>-1</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-1.9</td></tr><tr><td>11</td><td>2</td><td>2</td><td>2</td><td>1</td><td>1</td><td>2</td><td>0</td><td>1</td><td>2</td><td>1</td><td>1.4</td></tr><tr><td>12</td><td>-1</td><td>-2</td><td>0</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>2</td><td>-2</td><td>-2</td><td>-1.3</td></tr><tr><td>13</td><td>-1</td><td>-2</td><td>0</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-1</td><td>-2</td><td>-2</td><td>-1.6</td></tr><tr><td>14</td><td>1</td><td>0</td><td>2</td><td>2</td><td>1</td><td>1</td><td>2</td><td>1</td><td>2</td><td>1</td><td>1.3</td></tr><tr><td>15</td><td>1</td><td>0</td><td>2</td><td>1</td><td>0</td><td>2</td><td>2</td><td>0</td><td>1</td><td>2</td><td>1.1</td></tr><tr><td>16</td><td>-2</td><td>-2</td><td>0</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-2</td><td>-1.8</td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>SUPPLEMENTAL FIGURE S1 – Classification of individual and combined factor influences in descending order as bars for the angular deviation (α).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>SUPPLEMENTAL FIGURE S2 – Classification of individual and combined factor influences in descending order as bars for the expert score.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>SUPPLEMENTAL FIGURE S3 – Overall agreement for each practitioner.</figcaption></figure>