Imaging Science in Dentistry最新文献

Purpose: This study examined the influence of a metal artifact reduction (MAR) tool, sharpening filters, and their combination on the diagnosis of vertical root fracture (VRF) in teeth with metallic posts using cone-beam computed tomography (CBCT).

Materials and methods: Twenty single-rooted human premolars - 9 with VRF and 11 without - were individually placed in a human mandible. A metallic post composed of a cobalt-chromium alloy was inserted into the root canal of each tooth. CBCT scans were then acquired under the following parameters: 8 mA, a 5×5 cm field of view, a voxel size of 0.085 mm, 90 kVp, and with MAR either enabled or disabled. Five oral and maxillofacial radiologists independently evaluated the CBCT exams under each MAR mode and across 3 sharpening filter conditions: no filter, Sharpen 1×, and Sharpen 2×. The diagnostic performance was quantified by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. These metrics were compared using 2-way analysis of variance with a significance level of α=5%. Intra- and inter-examiner agreement were assessed using the weighted kappa test.

Results: Neither MAR nor the application of sharpening filters significantly impacted AUC or specificity (P>0.05). However, sensitivity increased when MAR was combined with Sharpen 1× and Sharpen 2× (P=0.015). The intra-examiner agreement ranged from fair to substantial (0.34-0.66), while the inter-examiner agreement ranged from fair to moderate (0.27-0.41).

Conclusion: MAR in conjunction with sharpening filters improved VRF detection; therefore, their combined use is recommended in cases of suspected VRF.

Purpose: Preoperative identification of the intraosseous posterior superior alveolar artery (PSAA) is critical when planning sinus surgery. This study was conducted to determine the distance between the cementoenamel junction and the PSAA, as well as to identify factors influencing the detection of the PSAA on cone-beam computed tomography (CBCT).

Materials and methods: In total, 254 CBCT scans of maxillary sinuses, acquired with 2 different scanners, were examined to identify the PSAA. The distance from the cementoenamel junction (CEJ) to the PSAA was recorded at each maxillary posterior tooth position. Binomial logistic regression and multiple linear regression were employed to evaluate the effects of scanner type, CBCT parameters, sex, and age on PSAA detection and CEJ-PSAA distance, respectively. P-values less than 0.05 were considered to indicate statistical significance.

Results: The mean CEJ-PSAA distances at the second molar, first molar, second premolar, and first premolar positions were 17.0±4.0 mm, 21.8±4.1 mm, 19.5±4.7 mm, and 19.9±4.9 mm for scanner 1, respectively, and 17.3±3.5 mm, 16.9±4.3 mm, 18.5±4.1 mm, and 18.4±4.3 mm for scanner 2. No independent variable significantly influenced PSAA detection. However, tooth position (b=-0.67, P<0.05) and scanner type (b=-1.3, P<0.05) were significant predictors of CEJ-PSAA distance.

Conclusion: CBCT-based estimates of CEJ-PSAA distance were comparable to those obtained in previous studies involving cadavers, CT, and CBCT. The type of CBCT scanner may slightly influence this measurement. No independent variable significantly impacted PSAA detection.

Purpose: This study was conducted to identify the typical sites and patterns of peri-implant bone defects on cone-beam computed tomography (CBCT) images, as well as to evaluate the detectability of the identified bone defects on panoramic images.

Materials and methods: The study population included 114 patients with a total of 367 implant fixtures. CBCT images were used to assess the presence or absence of bone defects around each implant fixture at the mesial, distal, buccal, and lingual sites. Based on the number of defect sites, the presentations of the peri-implant bone defects were categorized into 3 patterns: 1 site, 2 or 3 sites, and circumferential bone defects. Two observers independently evaluated the presence or absence of bone defects on panoramic images. The bone defect detection rate on these images was evaluated using receiver operating characteristic analysis.

Results: Of the 367 implants studied, 167 (45.5%) had at least 1 site with a confirmed bone defect. The most common type of defect was circumferential, affecting 107 of the 167 implants (64.1%). Implants were most frequently placed in the mandibular molar region. The prevalence of bone defects was greatest in the maxillary premolar and mandibular molar regions. The highest kappa value was associated with the mandibular premolar region.

Conclusion: The typical bone defect pattern observed was a circumferential defect surrounding the implant. The detection rate was generally higher in the molar region than in the anterior region. However, the capacity to detect partial bone defects using panoramic imaging was determined to be poor.

Purpose: This study was conducted to investigate the safety of dental imaging in pregnant women with respect to fetal health.

Materials and methods: Searches were conducted of the PubMed, Scopus, and Web of Science databases in May 2023. The inclusion criteria encompassed cross-sectional and longitudinal studies that focused on the analysis of diagnostic dental imaging in pregnant women, as well as studies utilizing phantoms to simulate imaging examinations. The exclusion criteria consisted of reviews, letters to the editor, book chapters, and abstracts from scientific conferences and seminars.

Results: A total of 3,913 articles were identified. Based on a review of the titles and abstracts, 3,892 articles were excluded, leaving 21 articles remaining for full-text review. Of these, 18 were excluded, and 4 additional articles were included as cross-references. Ultimately, 7 articles underwent quantitative-qualitative analysis. Three retrospective studies were focused on pregnant women who underwent dental imaging procedures. The remaining 4 studies utilized female phantoms to simulate imaging examinations and represent the radiation doses absorbed by the uterus or thyroid.

Conclusion: Few dental radiology studies have been conducted to determine the safe radiation threshold for pregnant women. Additionally, the reviewed articles did not provide numbers of dental examinations, by type, corresponding to this dose. Dental imaging examinations of pregnant women should not be restricted if clinically indicated. Ultimately, practitioners must be able to justify the examination and should adhere to the "as low as diagnostically acceptable, being indication-oriented and patient-specific" (ALADAIP) principle of radioprotection.

Purpose: This study evaluated the impact of artifacts generated by metal crowns on the detection of proximal caries lesions in teeth at various distances using cone-beam computed tomography (CBCT). Additionally, the diagnostic impacts of tube current and metal artifact reduction (MAR) were investigated.

Materials and methods: Thirty teeth were arranged within 10 phantoms, each containing 1 first premolar, 1 second premolar, and 1 second molar. A sound first molar (for the control group) or a tooth with a metal crown was placed. Of the 60 proximal surfaces evaluated, 15 were sound and 45 exhibited enamel caries. CBCT scans were acquired using an OP300 Maxio unit (Instrumentarium, Tuusula, Finland), while varying the tube current (4, 8, or 12.5 mA) and enabling or disabling MAR. Five observers assessed mesial and distal surfaces using a 5-point scale. Multi-way analysis of variance was employed for data comparison, with P<0.05 indicating statistical significance.

Results: The area under the curve (AUC) varied from 0.40 to 0.60 (sensitivity: 0.28-0.45, specificity: 0.44-0.80). The diagnostic accuracy was not significantly affected by the presence of a metal crown, milliamperage, or MAR (P>0.05). However, the overall AUC and specificity were significantly lower for surfaces near a crown (P<0.05).

Conclusion: CBCT-based caries detection was not influenced by the presence of a metal crown, variations in milliamperage, or MAR activation. However, the diagnostic accuracy was low and was further diminished for surfaces near a crown. Consequently, CBCT is not recommended for the detection of incipient caries lesions.

Chronic nonbacterial osteomyelitis (CNO) is histologically characterized by nonspecific osteitis. This inflammatory disorder, which lacks an infectious origin, typically presents with chronic pain and swelling at the affected site that can persist for months or even years. However, it is rare for CNO to affect the mandible. A 10-year-old girl presented with a primary complaint of pain in her left mandible. She had no significant medical or dental history. On examination, swelling was visible on the left buccal side, and imaging revealed radiolucent bone deterioration within the left mandible. This case report presents the radiological changes observed over a 12-year follow-up period. Variations in radiopacity, radiolucency, and periosteal reactions were noted periodically. This case highlights the radiological characteristics and findings that are crucial for the diagnosis of CNO, a condition for which no clear diagnostic criteria are currently available.

Purpose: Pycnodysostosis (PYCD), an autosomal recessive syndrome, is characterized by an imbalance in bone remodeling that produces various clinical and radiographic craniofacial manifestations. This review represents a systematic examination of these manifestations, as well as oral features associated with PYCD.

Materials and methods: A systematic review was conducted across 8 databases from February to March 2023. The search strategy focused on studies reporting cases of PYCD that examined the clinical and radiographic craniofacial and oral characteristics associated with this syndrome.

Results: The review included 84 studies, encompassing a total of 179 cases of PYCD. More than half of the patients were female (55.3%), and the mean age was 14.7 years. Parental consanguinity was reported in 51.4% of the cases. The most common craniofacial clinical manifestation was a prominent nose, observed in 57.5% of cases. Radiographically, the most frequently reported craniofacial characteristics included the presence of an obtuse mandibular angle (84.3%) and frontal cranial bosses (82.1%). Clinical and radiographic examinations revealed oral alterations, with micrognathia present in 62.6% of patients and malocclusion in 59.2%. Among dental anomalies, tooth agenesis was the most commonly reported, affecting 15.6% of patients.

Conclusion: Understanding the clinical and radiographic craniofacial features of PYCD is crucial for dental professionals. This knowledge enables these clinicians to devise effective treatment plans and improve patient quality of life.

Purpose: The aims of this study were to create a deep learning model to distinguish between nasopalatine duct cysts (NDCs), radicular cysts, and no-lesions (normal) in the midline region of the anterior maxilla on panoramic radiographs and to compare its performance with that of dental residents.

Materials and methods: One hundred patients with a confirmed diagnosis of NDC (53 men, 47 women; average age, 44.6±16.5 years), 100 with radicular cysts (49 men, 51 women; average age, 47.5±16.4 years), and 100 with normal groups (56 men, 44 women; average age, 34.4±14.6 years) were enrolled in this study. Cases were randomly assigned to the training datasets (80%) and the test dataset (20%). Then, 20% of the training data were randomly assigned as validation data. A learning model was created using a customized DetectNet built in Digits version 5.0 (NVIDIA, Santa Clara, USA). The performance of the deep learning system was assessed and compared with that of two dental residents.

Results: The performance of the deep learning system was superior to that of the dental residents except for the recall of radicular cysts. The areas under the curve (AUCs) for NDCs and radicular cysts in the deep learning system were significantly higher than those of the dental residents. The results for the dental residents revealed a significant difference in AUC between NDCs and normal groups.

Conclusion: This study showed superior performance in detecting NDCs and radicular cysts and in distinguishing between these lesions and normal groups.

Purpose: Oral hygiene, maintained through plaque control, helps prevent periodontal disease and dental caries. This study was conducted to examine the accuracy of plaque detection with an intraoral scanner (IOS) compared to images captured with an optical camera.

Materials and methods: To examine the effect of color tone, artificial tooth resin samples were stained red, blue, and green, after which images were acquired with a digital single-lens reflex (DSLR) camera and an IOS device. Stained surface ratios were then determined and compared. Additionally, the deviation rate of the IOS relative to the DSLR camera was computed for each color. In the clinical study, following plaque staining with red disclosing solution, the staining was captured by the DSLR and IOS devices, and the stained area on each image was measured.

Results: The stained surface ratios did not differ significantly between DSLR and IOS images for any color group. Additionally, the deviation rate did not vary significantly across colors. In the clinical test, the stained plaque appeared slightly lighter in color, and the delineation of the stained areas less distinct, on the IOS compared to the DSLR images. However, the stained surface ratio was significantly higher in the IOS than in the DSLR group.

Conclusion: When employing IOS with dental plaque staining, the impact of color was minimal, suggesting that the traditional red stain remains suitable for plaque detection. IOS images appeared relatively blurred and enlarged relative to the true state of the teeth, due to inferior sharpness compared to camera images.

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs.

Materials and methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset.

Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%.

Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.