Imaging Science in Dentistry最新文献

Purpose: This study aimed to assess the performance of 2-dimensional (2D) imaging with microscopy coils in delineating teeth and periodontal tissues compared with conventional 3-dimensional (3D) imaging on a 3 T magnetic resonance imaging (MRI) unit.

Materials and methods: Twelve healthy participants (4 men and 8 women; mean age: 25.6 years; range: 20-52 years) with no dental symptoms were included. The left mandibular first molars and surrounding periodontal tissues were examined using the following 2 sequences: 2D proton density-weighted (PDw) images and 3D enhanced T1 high-resolution isotropic volume excitation (eTHRIVE) images. Two-dimensional MRI images were taken using a 3 T MRI unit and a 47 mm microscopy coil, while 3D MRI imaging used a 3 T MRI unit and head-neck coil. Oral radiologists assessed dental and periodontal structures using a 4-point Likert scale. Inter- and intra-observer agreement was determined using the weighted kappa coefficient. The Wilcoxon signed-rank test was used to compare 2D-PDw and 3D-eTHRIVE images.

Results: Qualitative analysis showed significantly better visualization scores for 2D-PDw imaging than for 3D-eTHRIVE imaging (Wilcoxon signed-rank test). 2D-PDw images provided improved visibility of the tooth, root dental pulp, periodontal ligament, lamina dura, coronal dental pulp, gingiva, and nutrient tract. Inter-observer reliability ranged from moderate agreement to almost perfect agreement, and intra-observer agreement was in a similar range.

Conclusion: Two-dimensional-PDw images acquired using a 3 T MRI unit and microscopy coil effectively visualized nearly all aspects of teeth and periodontal tissues.

Purpose: Multiple myeloma (MM) is a rare cancer that is typically managed with bisphosphonates to slow bone resorption and prevent skeletal complications. This study aimed to identify imaging patterns in MM patients receiving bisphosphonate therapy.

Materials and methods: This systematic review included studies investigating maxillomandibular bone alterations based on imaging examinations in MM patients treated with bisphosphonates. The selected studies were qualitatively assessed using the Critical Appraisal Tools from SUMARI.

Results: Six studies, involving 669 MM patients, were included, with 447 receiving bisphosphonate treatment. The majority were treated with pamidronate, zoledronate, or a combination of both. Seventy patients developed medication-related osteonecrosis of the jaw (MRONJ), predominantly in the mandible, characterized by the presence of bony sequestrum, bone sclerosis, increased periodontal ligament space, osteolytic lesions, and osteomyelitis as observed in imaging analyses. For non-MRONJ lesions, the mandible also exhibited the highest frequency of asymptomatic bone alterations. These ranged from "punched-out" osteolytic lesions or "soap bubble" lesions to solitary bone lesions, areas of bone sclerosis, abnormalities of the hard palate, osteoporosis, non-healed alveoli, and cortical bone rupture.

Conclusion: MM patients treated with bisphosphonates display radiographic patterns of maxillomandibular bone lesions. These patterns aid in diagnosis and facilitate early and targeted treatment, thereby contributing to improved morbidity outcomes for these patients.

Purpose: The purpose of this study was to classify mandibular molar furcation involvement (FI) in periapical radiographs using a deep learning algorithm.

Materials and methods: Full mouth series taken at East Carolina University School of Dental Medicine from 2011-2023 were screened. Diagnostic-quality mandibular premolar and molar periapical radiographs with healthy or FI mandibular molars were included. The radiographs were cropped into individual molar images, annotated as " healthy" or " FI," and divided into training, validation, and testing datasets. The images were preprocessed by PyTorch transformations. ResNet-18, a convolutional neural network model, was refined using the PyTorch deep learning framework for the specific imaging classification task. CrossEntropyLoss and the AdamW optimizer were employed for loss function training and optimizing the learning rate, respectively. The images were loaded by PyTorch DataLoader for efficiency. The performance of ResNet-18 algorithm was evaluated with multiple metrics, including training and validation losses, confusion matrix, accuracy, sensitivity, specificity, the receiver operating characteristic (ROC) curve, and the area under the ROC curve.

Results: After adequate training, ResNet-18 classified healthy vs. FI molars in the testing set with an accuracy of 96.47%, indicating its suitability for image classification.

Conclusion: The deep learning algorithm developed in this study was shown to be promising for classifying mandibular molar FI. It could serve as a valuable supplemental tool for detecting and managing periodontal diseases.

Purpose: This study examined the influence of metal artifact reduction (MAR), the application of sharpening filters, and their combination on the diagnosis of horizontal root fracture (HRF) in teeth adjacent to a zirconia implant on cone-beam computed tomography (CBCT) examinations.

Materials and methods: Nineteen single-rooted teeth (9 with HRF and 10 without) were individually positioned in the right central incisor socket of a dry human maxilla. A zirconia implant was placed adjacent to each tooth. Imaging was performed using an OP300 Maxio CBCT (Instrumentarium, Tuusula, Finland) unit with the following settings: a current of 8 mA, both MAR modes (enabled and disabled), a 5×5 cm field of view, a voxel size of 0.085 mm, and a peak kilovoltage of 90 kVp. Four oral and maxillofacial radiologists independently evaluated the CBCT scans under both MAR conditions and across 3 levels of sharpening filter application (none, Sharpen 1×, and Sharpen 2×). Diagnostic metrics were calculated and compared using 2-way analysis of variance (α=5%). The weighted kappa test was used to assess intra- and inter-examiner reliability in the diagnosis of HRF.

Results: MAR tool activation, sharpening filter use, and their combination did not significantly impact the area under the receiver operating characteristic curve, sensitivity, or specificity of HRF diagnosis (P>0.05). Intra- and inter-examiner agreement ranged from fair to substantial.

Conclusion: The diagnosis of HRF in a tooth adjacent to a zirconia implant is not affected by the activation of MAR, the application of a sharpening filter, or the combination of these tools.

Ultrasonography is highly accurate for evaluating soft tissues. Given that minimally invasive aesthetic procedures are on the rise, complications have become more prevalent. Thus, ultrasonography holds promise for assisting in the diagnosis and management of complications arising from these interventions. This report highlights the importance of ultrasonography in the treatment of complications caused by hyaluronic acid injection. A patient visited a dental office 24 hours after hyaluronic acid application, presenting pain and bruising in the middle and inferior thirds of the face on the right side. To evaluate blood vessels, the surgeon used Doppler-mode ultrasonography, which enabled the precise application of hyaluronidase to reestablish blood perfusion and preserve adjacent structures. Therefore, to avoid severe outcomes, such as necrosis or even amaurosis, the use of ultrasonography is suggested, improving the precision and safety of these procedures.

[This corrects the article on p. 421 in vol. 52, PMID: 36605861.].

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures.

Materials and methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command.

Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images. The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913).

Conclusion: This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.

Purpose: Recent advancements in artificial intelligence (AI), particularly tools such as ChatGPT developed by OpenAI, a U.S.-based AI research organization, have transformed the healthcare and education sectors. This study investigated the effectiveness of ChatGPT in answering dentistry exam questions, demonstrating its potential to enhance professional practice and patient care.

Materials and methods: This study assessed the performance of ChatGPT 3.5 and 4 on U.S. dental exams - specifically, the Integrated National Board Dental Examination (INBDE), Dental Admission Test (DAT), and Advanced Dental Admission Test (ADAT) - excluding image-based questions. Using customized prompts, ChatGPT's answers were evaluated against official answer sheets.

Results: ChatGPT 3.5 and 4 were tested with 253 questions from the INBDE, ADAT, and DAT exams. For the INBDE, both versions achieved 80% accuracy in knowledge-based questions and 66-69% in case history questions. In ADAT, they scored 66-83% in knowledge-based and 76% in case history questions. ChatGPT 4 excelled on the DAT, with 94% accuracy in knowledge-based questions, 57% in mathematical analysis items, and 100% in comprehension questions, surpassing ChatGPT 3.5's rates of 83%, 31%, and 82%, respectively. The difference was significant for knowledge-based questions (P=0.009). Both versions showed similar patterns in incorrect responses.

Conclusion: Both ChatGPT 3.5 and 4 effectively handled knowledge-based, case history, and comprehension questions, with ChatGPT 4 being more reliable and surpassing the performance of 3.5. ChatGPT 4's perfect score in comprehension questions underscores its trainability in specific subjects. However, both versions exhibited weaker performance in mathematical analysis, suggesting this as an area for improvement.

Non-secretory multiple myeloma (NSMM) is a rare cancer of plasma cells characterized by the absence of detectable monoclonal M protein in the blood or urine. A 57-year-old woman presented with mandibular pain but without intraoral swelling. Imaging studies revealed multiple osteolytic lesions in her mandible and pronounced root resorption of the left mandibular second molar. Biopsy results showed atypical plasmacytoid cells positive for anti-kappa, CD138, MUM1, and CD79a antibodies, but negative for anti-lambda and CD20. These results were indicative of a malignant plasma cell neoplasm. No abnormalities were revealed by free light chain assay or by serum or urine protein electrophoresis, leading to a diagnosis of NSMM. The patient began chemotherapy in conjunction with bisphosphonate therapy and achieved remission following treatment. This case underscores the critical role of dentists in the early detection and prevention of NSMM complications, as the disease can initially present in the oral cavity.

Ameloblastic fibrodentinoma (AFD) is a rare benign odontogenic tumor that resembles an ameloblastic fibroma with dysplastic dentin. This report presents a rare case of mandibular AFD with imaging features in a young patient. Panoramic radiography and computed tomography revealed a well-defined lesion with internal septa and calcified foci, causing inferior displacement of the adjacent molars as well as buccolingual cortical thinning and expansion of the posterior mandible. The lesion was surgically removed via mass excision, and the involved tooth was extracted under general anesthesia. During the 5-year follow-up period, no evidence of recurrence was observed. Radiologic features of AFD typically reveal a moderately to well-defined mixed lesion with varying degrees of radiopacity, reflecting the extent of dentin formation. Radiologists should consider AFD in the differential diagnosis when encountering a multilocular lesion with little dense radiopacity, particularly if it is associated with delayed eruption, impaction, or absence of involved teeth, on radiographic images of young patients.