Assessing the reliability of CBCT-based AI-generated STL files in diagnosing osseous changes of the mandibular condyle: a comparative study with ground truth diagnosis.

OBJECTIVES: This study aims to evaluate the reliability of AI-generated STL files in diagnosing osseous changes of the mandibular condyle and compare them to a ground truth (GT) diagnosis made by six radiologists. METHODS: A total of 432 retrospective CBCT images from four universities were evaluated by six dentomaxillofacial radiologists who identified osseous changes such as flattening, erosion, osteophyte formation, bifid condyle formation, and osteosclerosis. All images were evaluated by each radiologist blindly and recorded on a spreadsheet. All evaluations were compared and for the disagreements, a consensus meeting was held online to create a uniform GT diagnosis spreadsheet. A web-based dental AI software was used to generate STL files of the CBCT images, which were then evaluated by two dentomaxillofacial radiologists. The new observer, GT, was compared to this new STL file evaluation, and the interclass correlation (ICC) value was calculated for each pathology. RESULTS: Out of the 864 condyles assessed, the ground truth diagnosis identified 372 cases of flattening, 185 cases of erosion, 70 cases of osteophyte formation, 117 cases of osteosclerosis, and 15 cases of bifid condyle formation. The ICC values for flattening, erosion, osteophyte formation, osteosclerosis, and bifid condyle formation were 1.000, 0.782, 1.000, 0.000, and 1.000, respectively, when comparing diagnoses made using STL files with the ground truth. CONCLUSIONS: AI-generated STL files are reliable in diagnosing bifid condyle formation, osteophyte formation, and flattening of the condyle. However, the diagnosis of osteosclerosis using AI-generated STL files is not reliable, and the accuracy of diagnosis is affected by the erosion grade.

A deep learning algorithm for classification of oral lichen planus lesions from photographic images: A retrospective study.

INTRODUCTION: Deep learning methods have recently been applied for the processing of medical images, and they have shown promise in a variety of applications. This study aimed to develop a deep learning approach for identifying oral lichen planus lesions using photographic images. MATERIAL AND METHODS: Anonymous retrospective photographic images of buccal mucosa with 65 healthy and 72 oral lichen planus lesions were identified using the CranioCatch program (CranioCatch, Eskisehir, Turkey). All images were re-checked and verified by Oral Medicine and Maxillofacial Radiology experts. This data set was divided into training (n = 51; n = 58), verification (n = 7; n = 7), and test (n = 7; n = 7) sets for healthy mucosa and mucosa with the oral lichen planus lesion, respectively. In the study, an artificial intelligence model was developed using Google Inception V3 architecture implemented with Tensorflow, which is a deep learning approach. RESULTS: AI deep learning model provided the classification of all test images for both healthy and diseased mucosa with a 100% success rate. CONCLUSION: In the healthcare business, AI offers a wide range of uses and applications. The increased effort increased complexity of the job, and probable doctor fatigue may jeopardize diagnostic abilities and results. Artificial intelligence (AI) components in imaging equipment would lessen this effort and increase efficiency. They can also detect oral lesions and have access to more data than their human counterparts. Our preliminary findings show that deep learning has the potential to handle this significant challenge.

A Deep Learning Approach for Masseter Muscle Segmentation on Ultrasonography.

Aim: Deep learning algorithms have lately been used for medical image processing, and they have showed promise in a range of applications. The purpose of this study was to develop and test computer-based diagnostic tools for evaluating masseter muscle segmentation on ultrasonography images. Materials and methods: A total of 388 anonymous adult masseter muscle retrospective ultrasonographic images were evaluated. The masseter muscle was labeled on ultrasonography images using the polygonal type labeling method with the CranioCatch labeling program (CranioCatch, Eskisehir, Turkey). All images were re-checked and verified by Oral and Maxillofacial Radiology experts. This data set was divided into training (n = 312), verification (n = 38) and test (n = 38) sets. In the study, an artificial intelligence model was developed using PyTorch U-Net architecture, which is a deep learning approach. Results: In our study, the artificial intelligence deep learning model known as U-net provided the detection and segmentation of all test images, and when the success rate in the estimation of the images was evaluated, the F1, sensitivity and precision results of the model were 1.0, 1.0 and 1.0, respectively. Conclusion: Artificial intelligence shows promise in automatic segmentation of masseter muscle on ultrasonography images. This strategy can aid surgeons, radiologists, and other medical practitioners in reducing diagnostic time.

Assessment of Knowledge Level and Awareness About Human Papillomavirus Among Dental Students.

Epidemiological studies have shown that the worldwide trend of human papillomavirus (HPV)-induced oral cancer has increased. Dentistry students need comprehensive information about HPV to provide accurate advice to their patients. The aim of this study is to evaluate 3rd, 4th, and 5th grade students' knowledge and awareness about HPV. A questionnaire consisting of 16 questions was applied to 318 students (100 3rd grade, 119 4th grade, and 99 5th grades), who were studying at XXX. In this survey, students' knowledge level and awareness were examined. In our study, 99 (31.0%) of the participants were male and 219 (68.9%) were female. There was a statistically significant difference between the classes in terms of participation rates in the statement "Some types of HPV cause oral cancer (p, 0.000; p < 0.05). The rate of participation of third grade (72%) students in this proposition was significantly lower than 4th grade (89.9%) and 5th grade (84.8%) students (p1, 0.000; p2, 0.000; p < 0.05). There was no statistically significant difference between 4th and 5th grade students (p > 0.05). Overall, advanced students showed better knowledge, attitudes, and perceptions regarding human papillomavirus-related oral cancer. Comprehensive training and motivation for improving dentistry students' awareness against HPV-induced oral cancer will also improve knowledge and attitudes of the dental students.

Assessing Oral Cancer Awareness Among Dental Students.

The aim of this study was to assess oral cancer awareness among undergraduate dental students in Marmara University Faculty of Dentistry. A validated questionnaire which tested oral cancer awareness was given to third- and fifth-year students of the dental faculty of Marmara University. A total of 198 students participated in this survey. Knowledge of oral cancer risk factors and diagnosis procedures, dentistry student's attitude towards oral cancers, management practice regarding oral cancer, and oral cancer information sources were assessed using 25 questions. The data were analyzed with IBM SPSS Statistics 22.0 program. Among 198 participant dentistry students, there were 99 (50%) third-grade and 99 (50%) fifth-grade students. The largest number of the third- and last-grade students identified tobacco (98%) and alcohol usage (87.4%), prior oral cancer lesions (94.9%), viral infections (91.9%), UV exposure (94.4%), betel quid chewing (84.8%), older age (62.1%), and low consumption of fruit and vegetables (85.4%). Both groups showed higher scores in indicating squamous cell carcinoma as the most common form of oral cancer (p < 0.05); yet, third-grade students performed significantly higher scores in indicating erythroplakia and leukoplakia for most likely to be precancerous (p = 0.001; p < 0.05). This study highlighted the importance of improved educational methods for dentistry on oral cancer detection and prevention.

Assessment of Masseter Muscle Appearance and Thickness in Edentulous and Dentate Patients by Ultrasonography.

OBJECTIVES: The purpose of the present study was to examine ultrasonographic appearances of Masseter Muscle (MM) in dentate and edentulous patients without Temporomandibular Disorder (TMD). MATERIALS AND METHODS: The thickness of the MM in 25 dentate (mean age: 30,68 ± 10,49) and 24 edentulous (mean age: 61,46 ± 9,71) patients, who visited routine dental examination, was measured at rest and at maximum contraction bilaterally. Examinations were performed using an Aloka Prosound α6 (Hitachi Aloka Medical Systems, Tokyo, Japan) equipped with an 8 MHz-wide bandwidth linear active matrix transducer (ranging from 1 to 15 MHz). The visibility and width of the internal echogenic bands of the MM were also assessed and the muscle appearance was classified as I of III types. Type I, characterized by the clear visibility of the fine bands; Type II, thickening echogenicity of the bands; Type III, disappearance or reduction in a number of the bands. RESULTS: MM thickness at rest and contraction in the dentate group were significantly higher than the edentulous group (p <0.05). Type I was the most common echogenic type in both dentate (right:16 (64%), left; 15 (60%)) and edentulous patients (right; 22 (91.7%), left; 18 (75%)). In a dentate group, type II was significantly higher than the edentulous group in both the right and left sides (p <0.05; p <0.01, respectively). Age and gender seemed to have no significant effect on the echogenic type (p ˃0.05). CONCLUSION: There were significant differences in the thickness at rest and contraction between the dentate and edentulous groups. It was clarified that ultrasonographic features of the MM in dentate and edentulous patients were different.

Awareness assessment in Turkish subpopulation with chronic oral mucosal diseases.

OBJECTIVES: The aim of this study was to evaluate the awareness of group Turkish patients with chronic oral mucosal diseases by chronic oral mucosal diseases questionnaires (COMDQ). MATERIALS AND METHODS: Eighty patients with chronic oral mucosal diseases were participated in the study. A detailed medical history of each patient was taken, and all the COMDQ questions, which were translated from English version, were filled out. The data were analyzed with the IBM Statistical Package for Social Sciences Statistics 22.0. RESULTS: The mean ages of patients were 48.91 ± 13.36 years. Of the total 80 cases of chronic oral mucosal diseases identified 52 (65%) were female and 28 (35%) male. The standardized mean scores for COMDQ were 1.72 ± 1.11 for "pain and functional limitation," 1.09 ± 0.94 for "medication and treatment," 2.31 ± 1.06 for "social and emotional," and 2.27 ± 0.83 for "patient support," respectively. CONCLUSIONS: The results of this study indicate that the Turkish version of the COMDQ has the profitable psychometric peculiarity and comfortable to patients with chronic oral mucosal diseases in Turkey.