• Imaging Science in Dentistry
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• v.51 no.2
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• pp.137-148
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• 2021

Purpose: This study aimed to assess the computer monitors used for analysis and interpretation of digital radiographs within the clinics of the Oral Health Centre of Western Australia. Materials and Methods: In total, 135 computer monitors(3 brands, 6 models) were assessed by analysing the same radiographic image of a combined 13-step aluminium step wedge and the Artinis CDDent 1.0^® (Artinis Medical Systems B.V.^®, Elst, the Netherlands) test object. The number of steps and cylindrical objects observed on each monitor was recorded along with the monitor's make, model, position relative to the researcher's eye level, and proximity to the nearest window. The number of window panels blocked by blinds, the outside weather conditions, and the number of ceiling lights over the surgical suite/cubicle were also recorded. MedCalc^® version 19.2.1 (MedCalc Software Ltd^®, Ostend, Belgium, https://www.medcalc.org; 2020) was used for statistical analyses(Kruskal-Wallis test and stepwise regression analysis). The level of significance was set at P<0.05. Results: Stepwise regression analysis showed that only the monitor brand and proximity of the monitor to a window had a significant impact on the monitor's performance (P<0.05). The Kruskal-Wallis test showed significant differences (P<0.05) in monitor performance for all variables investigated, except for the weather and the clinic in which the monitors were placed. Conclusion: The vast performance variation present between computer monitors implies the need for a review of monitor selection, calibration, and viewing conditions.

• Imaging Science in Dentistry
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• v.51 no.2
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• pp.167-174
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• 2021

Purpose: Sex determination can be done by morphological analysis of different parts of the body. The mastoid region, with its anatomical location at the skull base, is ideal for sex identification. Statistical shape analysis provides a simultaneous comparison of geometric information on different shapes in terms of size and shape features. This study aimed to investigate the geometric morphometry of the inter-mastoid triangle as a tool for sex determination in the Iranian population. Materials and Methods: The coordinates of 5 landmarks on the mastoid process on the 80 cone-beam computed tomographic images(from individuals aged 17-70 years, 52.5% female) were registered and digitalized. The Cartesian x-y coordinates were acquired for all landmarks, and the shape information was extracted from the principal component scores of generalized Procrustes fit. The t-test was used to compare centroid size. Cross-validated discriminant analysis was used for sex determination. The significance level for all tests was set at 0.05. Results: There was a significant difference in the mastoid size and shape between males and females(P<0.05). The first 2 components of the Procrustes shape coordinates explained 91.3% of the shape variation between the sexes. The accuracy of the discriminant model for sex determination was 88.8%. Conclusion: The application of morphometric geometric techniques will significantly impact forensic studies by providing a comprehensive analysis of differences in biological forms. The results demonstrated that statistical shape analysis can be used as a powerful tool for sex determination based on a morphometric analysis of the inter-mastoid triangle.

• Imaging Science in Dentistry
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• v.51 no.1
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• pp.17-25
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• 2021

Purpose: This study was performed to investigate mandibular canal displacement in patients with ameloblastoma using a 3-dimensional mirrored-model analysis. Materials and Methods: The sample consisted of computed tomographic scans of patients with ameloblastoma (n=10) and healthy controls (n=20). The amount of mandibular canal asymmetry was recorded as a continuous variable, while the buccolingual (yaw) and supero-inferior (pitch) directions of displacement were classified as categorical variables. The t-test for independent samples and the Fisher exact test were used to compare groups in terms of differences between sides and the presence of asymmetric inclinations, respectively (P<0.05). Results: The length of the mandibular canal was similar on both sides in both groups. The ameloblastoma group presented more lateral (2.40±4.16 mm) and inferior (-1.97±1.92 mm) positions of the mental foramen, and a more buccal (1.09±2.75 mm) position of the middle canal point on the lesion side. Displacement of the mandibular canal tended to be found in the anterior region in patients with ameloblastoma, occurring toward the buccal and inferior directions in 60% and 70% of ameloblastoma patients, respectively. Conclusion: Mandibular canal displacement due to ameloblastoma could be detected by this superimposed mirrored method, and displacement was more prevalent toward the inferior and buccal directions. This displacement affected the mental foramen position, but did not lead to a change in the length of the mandibular canal. The control group presented no mandibular canal displacement.

• Imaging Science in Dentistry
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• v.51 no.1
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• pp.1-7
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• 2021

Purpose: The aim of this study was to assess artifacts generated in cone-beam computed tomography (CBCT) of 3 types of dental implants using 3 metal artifact reduction (MAR) algorithm conditions (pre-acquisition MAR, post-acquisition MAR, and no MAR), and 2 peak kilovoltage (kVp) settings. Materials and Methods: Titanium-zirconium, titanium, and zirconium alloy implants were placed in a dry mandible. CBCT images were acquired using 84 and 90 kVp and at normal resolution for all 3 MAR conditions. The images were analyzed using ImageJ software (National Institutes of Health, Bethesda, MD) to calculate the intensity of artifacts for each combination of material and settings. A 3-factor analysis of variance model with up to 3-way interactions was used to determine whether there was a statistically significant difference in the mean intensity of artifacts associated with each factor. Results: The analysis of all 3 MAR conditions showed that using no MAR resulted in substantially more severe artifacts than either of the 2 MAR algorithms for the 3 implant materials; however, there were no significant differences between pre- and post-acquisition MAR. The 90 kVp setting generated less intense artifacts on average than the 84 kVp setting. The titanium-zirconium alloy generated significantly less intense artifacts than zirconium. Titanium generated artifacts at an intermediate level relative to the other 2 implant materials, but was not statistically significantly different from either. Conclusion: This in vitro study suggests that artifacts can be minimized by using a titanium-zirconium alloy at the 90 kVp setting, with either MAR setting.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.171-179
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• 2022

Purpose: The aim of this study was to assess the accuracy of cone-beam computed tomographic (CBCT) images obtained using different voxel sizes in measuring trabecular bone microstructure in comparison to micro-CT. Materials and Methods: Twelve human skull bones containing posterior-mandibular alveolar bone regions were analyzed. CBCT images were obtained at voxel sizes of 0.075mm(high: HI) and 0.2mm(standard: Std), while microCT imaging used voxel sizes of 0.06 mm (HI) and 0.12 mm (Std). Analyses were performed using CTAn software with the standardized automatic global threshold method. Intraclass correlation coefficients were used to evaluate the consistency and agreement of paired measurements for bone volume (BV), percent bone volume (BV/TV), bone surface (BS), trabecular thickness (TbTh), trabecular separation (TbSp), trabecular number (TbN), trabecular pattern factor(TbPf), and structure model index (SMI). Results: When compared to micro-CT, CBCT images had higher BV, BV/TV, and TbTh values, while micro-CT images had lower BS, TbSp, TbN, TbPf, and SMI values (P<0.05). The BV, BV/BT, TbTh, and TbSp variables were higher with Std voxels, whereas the BS, TbPf, and SMI variables were higher with HI voxels for both imaging methods. For each imaging modality and voxel size evaluated, BV, BS, and TbTh were significantly different(P<0.05). TbN, TbPf, and SMI showed statistically significant differences between imaging methods(P<0.05). The consistency and absolute agreement between micro-CT and CBCT were excellent for all variables. Conclusion: This study demonstrated the potential of high-resolution CBCT imaging for quantitative bone morphometry assessment.

• Imaging Science in Dentistry
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• v.52 no.1
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• pp.61-66
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• 2022

Purpose: This study aimed to compare the therapeutic effects of corticosteroid irrigations and normal saline irrigations in the early inflammatory state of the salivary gland. Materials and Methods: Adult male Wistar rats were divided into experimental (n=6) and control (n=3) groups. Inflammation was induced in the experimental subjects on both sides of the submandibular gland with ligation. After 14 days, both sides of the glands were de-ligated and retroductal irrigation using saline (n=3) and a corticosteroid (n=3) was performed on the left sides only. The controls (n=3) were used to normalize the gland state for the effects of diet and aging. Magnetic resonance imaging was performed to confirm inflammation and post-irrigation gland recovery by measuring relative signal intensity (SI). The glands were excised for histological examination. Results: All experimental animals showed inflamed glands with increased SI and subsequent recovery of the gland with decreased SI to varying degrees. The SI of the controls showed no significant changes during the overall period. The mean SI change of the irrigated gland was higher than that of the non-irrigated side, without a significant difference. The corticosteroid-irrigated glands showed a greater change in SI than that of the saline-irrigated glands. Histology revealed that inflammation was not observed in most of the irrigated glands, while mild to moderate quantities inflammatory cells were found in non-irrigated glands. Conclusion: Corticosteroid irrigation mitigated the early stages of salivary gland inflammation more effectively than normal saline.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.133-140
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• 2022

Purpose: The aim of this review was to systematically analyze the available literature on the correlation between the gray values (GVs) of cone-beam computed tomography (CBCT) and the Hounsfield units (HUs) of computed tomography (CT) for assessing bone mineral density. Materials and Methods: A literature search was carried out in PubMed, Cochrane Library, Google Scholar, Scopus, and LILACS for studies published through September 2021. In vitro, in vivo, and animal studies that analyzed the correlations GVs of CBCT and HUs of CT were included in this review. The review was prepared according to the PRISMA checklist for systematic reviews, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A quantitative analysis was performed using a fixed-effects model. Results: The literature search identified a total of 5,955 studies, of which 14 studies were included for the qualitative analysis and 2 studies for the quantitative analysis. A positive correlation was observed between the GVs of CBCT and HUs of CT. Out of the 14 studies, 100% had low risks of bias for the domains of patient selection, index test, and reference standards, while 95% of studies had a low risk of bias for the domain of flow and timing. The fixed-effects meta-analysis performed for Pearson correlation coefficients between CBCT and CT showed a moderate positive correlation (r=0.669; 95% CI, 0.388 to 0.836; P<0.05). Conclusion: The available evidence showed a positive correlation between the GVs of CBCT and HUs of CT.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.219-224
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• 2022

Purpose: This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods: Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III(Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant(Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results: When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion: Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.141-153
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• 2022

Purpose: This study aimed to identify the prevalence and extension of the anterior loop (AL) of the mental nerve in different populations and according to different cone-beam computed tomography (CBCT) imaging settings. Materials and Methods: Medline/PubMed, Embase, Scopus, Web of Science, and ProQuest were searched. The main inclusion criterion was ALs evaluated in CBCT images. The quality of studies was assessed with the Joanna Briggs Institute risk of bias checklist. Subgroup analyses were conducted for sex, side, continent, voxel size, field of view, and type of CBCT-reconstruction images with a random-effects model. Results: Sixty-three studies with 13,743 participants (27,075 hemimandibles) were included. An AL was found in 40.6% (95% CI: 32.8%-48.9%, P<0.05) of participants and 36.0% (95% CI: 27.5%-45.5%, P<0.05) of hemimandibles, in 34.9% (95% CI: 25.1%-46.2%, P<0.05) of males and 34.5% (95% CI: 23.5%-47.4%, P<0.05) of females. The average length of ALs was 2.39 mm (95% CI: 2.07-2.70 mm, P<0.05). Their extension was 2.13 mm (95% CI: 1.54-2.73 mm, P<0.05) in males and 1.85 mm (95% CI: 1.35-2.36 mm, P<0.05) in females. Significant differences were observed regarding the prevalence and length of ALs among continents and for its measured length on different CBCT-reconstruction images, but not between other subgroups. Conclusion: AL was a relatively common finding. The voxel size and fields of view of CBCT devices were adequate for assessing AL; however, a 2-mm safety margin from anatomical structures(such as the AL) could be recommended to be considered when using CBCT imaging.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.