Objective: This study was conducted to assess the clinical usability of the zero-echo time (ZTE) technique of MRI for evaluating bone changes of the temporomandibular joint (TMJ) in comparison with CBCT. Methods: Twenty patients with TMJ disorder who underwent both CBCT and MRI were randomly selected. CBCT images were obtained with an Alphard 3030 device (Asahi Roentgen Ind., Co. Ltd, Kyoto, Japan). MRIs were obtained using a 3.0 T scanner (Pioneer; GE Healthcare, Waukesha, WI, USA) and a 21-channel head coil. An isotropic three-dimensional proton-density-weighted ZTE sequence was acquired. Two radiologists evaluated 40 joints of 20 patients for the presence of the following osseous changes: flattening, erosion, osteophyte and sclerosis of the condyle; and flattening, erosion and sclerosis of the articular fossa. CBCT and ZTE-MRI assessments were performed at a 2-month interval. The prevalence-adjusted and bias-adjusted κ statistic was used to analyse interexaminer and intraexaminer agreement and the agreement between ZTE-MRI and CBCT. Results: Intraexaminer and interexaminer agreement analyses of ZTE-MRI showed high reproducibility (κ>0.80), which was comparable to that of CBCT. Flattening, osteophyte and sclerosis of the condyle and all types of bone changes in the mandibular fossa showed nearly perfect agreement between CBCT and ZTE-MRI (κ = 0.80–0.90). Erosion of the condyle showed substantial agreement between both sets of images (κ = 0.65–0.70). Conclusions: It is suggested that ZTE-MRI provides clinically reliable images for bone assessment in TMJ disorder. MRI may become a beneficial diagnostic tool for patients with both TMJ disc and bone pathology, with advantages involving medical costs and radiation dose.

Objectives: This study evaluated the performance of four large language model (LLM)-based chatbots by comparing their test results with those of dental students on an oral and maxillofacial radiology examination. Methods: ChatGPT, ChatGPT Plus, Bard, and Bing Chat were tested on 52 questions from regular dental college examinations. These questions were categorized into three educational content areas: basic knowledge, imaging and equipment, and image interpretation. They were also classified as multiple-choice questions (MCQs) and short-answer questions (SAQs). The accuracy rates of the chatbots were compared with the performance of students, and further analysis was conducted based on the educational content and question type. Results: The students’ overall accuracy rate was 81.2%, while that of the chatbots varied: 50.0% for ChatGPT, 65.4% for ChatGPT Plus, 50.0% for Bard, and 63.5% for Bing Chat. ChatGPT Plus achieved a higher accuracy rate for basic knowledge than the students (93.8% vs. 78.7%). However, all chatbots performed poorly in image interpretation, with accuracy rates below 35.0%. All chatbots scored less than 60.0% on MCQs, but performed better on SAQs. Conclusions: The performance of chatbots in oral and maxillofacial radiology was unsatisfactory. Further training using specific, relevant data derived solely from reliable sources is required. Additionally, the validity of these chatbots’ responses must be meticulously verified.

Background: This study explored dental students’ and dentists’ perceptions and attitudes toward artificial intelligence (AI) and analyzed differences according to professional seniority. Methods: In September to November 2022, online surveys using Google Forms were conducted at 2 dental colleges and on 2 dental websites. The questionnaire consisted of general information (8 or 10 items) and participants’ perceptions, confidence, predictions, and perceived future prospects regarding AI (17 items). A multivariate logistic regression analysis was performed on 4 questions representing perceptions and attitudes toward AI to identify highly influential factors according to position, age, sex, residence, and self-reported knowledge level about AI of respondents. Participants were reclassified into 2 subgroups based on students’ years in school and 4 subgroups based on dentists’ years of experience. The chi-square test or Fisher’s exact test was used to determine differences between dental students and dentists and between subgroups for all 17 questions. Results: The study included 120 dental students and 96 dentists. Participants with high level of AI knowledge were more likely to be interested in AI compared to those with moderate or low level (adjusted OR 24.345, p < 0.001). Most dental students (60.8%) and dentists (67.7%) predicted that dental AI would complement human limitations. Dental students responded that they would actively use AI in almost all cases (40.8%), while dentists responded that they would use AI only when necessary (44.8%). Dentists with 11–20 years of experience were the most likely to disagree that AI could outperform skilled dentists (50.0%), and respondents with longer careers had higher response rates regarding the need for AI education in schools. Conclusions: Knowledge level about AI emerged as the factor influencing perceptions and attitudes toward AI, with both dental students and dentists showing similar views on recognizing the potential of AI as an auxiliary tool. However, students’ and dentists’ willingness to use AI differed. Although dentists differed in their confidence in the abilities of AI, all dentists recognized the need for education on AI. AI adoption is becoming a reality in dentistry, which requires proper awareness, proper use, and comprehensive AI education.

Kim HS, et al. Imaging Sci Dent. 2022 Mar;52:e14. https://doi.org/10.5624/isd.20210287

Objectives: This study aimed to develop a fully automated human identification method based on a convolutional neural network (CNN) with a large-scale dental panoramic radiograph (DPR) data set. Methods: In total, 2760 DPRs from 746 subjects who had 2–17 DPRs with various changes in image characteristics due to various dental treatments (tooth extraction, oral surgery, prosthetics, orthodontics, or tooth development) were collected. The test data set included the latest DPR of each subject (746 images) and the other DPRs (2014 images) were used for model training. A modified VGG16 model with two fully connected layers was applied for human identification. The proposed model was evaluated with rank-1, -3, and -5 accuracies, running time, and gradient-weighted class activation mapping (Grad-CAM)-applied images. Results: This model had rank-1, -3, and -5 accuracies of 82.84%, 89.14%, and 92.23%, respectively. All rank-1 accuracy values of the proposed model were above 80% regardless of changes in image characteristics. The average running time to train the proposed model was 60.9 s per epoch, and the prediction time for 746 test DPRs was short (3.2 s/image). The Grad-CAM technique verified that the model automatically identified humans by focusing on identifiable dental information. Conclusion: The proposed model showed good performance in fully automatic human identification despite differing image characteristics of DPRs acquired from the same patients. Our model is expected to assist in the fast and accurate identification by experts by comparing large amounts of images and proposing identification candidates at high speed.