Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs

Screening tests are vital for detecting diseases, especially at early stages, where efforts can prevent further illness. For example, osteoporosis is a systemic skeletal disease characterized by low bone mass and microarchitectural deterioration of bone tissue, resulting in bone fragility and susceptibility to fracture. Dual-energy x-ray absorptiometry is commonly used to diagnose osteoporosis since it evaluates bone mineral density. It is the most standard method for diagnosing osteoporosis, but it is not immediately available and is commonly used for research due to the high capital cost. Further, dual-energy x-ray absorptiometry is not used for populational-based screening due to its suboptimal ability to predict hip fractures based on measurements. Therefore, it is recommended to adopt a case-finding strategy to identify individuals at risk who benefit from the dual-energy x-ray absorptiometry examination. Several indices have been developed to estimate bone quality in dental panoramic radiographs to identify individuals at risk of osteoporosis. In particular, the mandibular cortical width index. Studies suggest that dentists can measure the mandibular cortical width to identify individuals at risk and refer them for bone mineral density testing. However, this endeavor is time-consuming and inconsistent due to the bone's unclear borders and the challenge of determining the mental foramen's position, leading to varying measurements between clinicians. Therefore, the dentistry community is investigating how to automate this process effectively and accurately. In an attempt to address some of these problems, this thesis presents a method to assess the mandibular cortical width index automatically. Four different object detectors were analyzed to determine the mental foramen's position. EfficientDet showed the highest average precision (0.30). Therefore, it was combined with an iterative procedure to estimate mandibular cortical width. The results are promising

Computer-aided detection in musculoskeletal projection radiography: A systematic review

This is the author accepted manuscript. The final version is available from WB Saunders via the DOI in this record.Objectives To investigated the accuracy of computer-aided detection (CAD) software in musculoskeletal projection radiography via a systematic review. Key findings Following selection screening, eligible studies were assessed for bias, and had their study characteristics extracted resulting in 22 studies being included. Of these 22 three studies had tested their CAD software in a clinical setting; the first study investigated vertebral fractures, reporting a sensitivity score of 69.3% with CAD, compared to 59.8% sensitivity without CAD. The second study tested dental caries diagnosis producing a sensitivity score of 68.8% and specificity of 94.1% with CAD, compared to sensitivity of 39.3% and specificity of 96.7% without CAD. The third indicated osteoporotic cases based on CAD, resulting in 100% sensitivity and 81.3% specificity. Conclusion The current evidence reported shows a lack of development into the clinical testing phase; however the research does show future promise in the variation of different CAD systems

파노라마방사선영상의 하악 피질골 두께를 이용한 한국 여성의 골다공증 스크리닝 프로그램 개발과 유용성 평가

학위논문 (박사)-- 서울대학교 대학원 치의학대학원 치의과학과, 2017. 8. 허민석.Purpose The purpose of this study was to develop and evaluate a screening program for Korean osteoporotic women using mandibular cortical width (MCW), which has been used for bone mineral density (BMD) assessment on panoramic radiographs. Materials and Methods A computer-based program was developed in order to measure the MCW automatically on panoramic radiographs. To identify changes in the MCW value according to the head position, panoramic radiographs were taken with 5 different vertical angulations using a head phantom. Normality test was performed among the MCW values measured from the panoramic radiographs of 250 young women. After a normal distribution was confirmed, the threshold corresponding to −2.5 standard deviations (SDs) was determined. The determined value was applied to the panoramic radiographs of 70 female subjects with a known femur BMD value, and the sensitivity, specificity, and accuracy were calculated. Additionally, a cut-off value for screening was obtained from a receiver operating characteristic (ROC) based on the data from these 70 female subjects. Results There was no statistically significant difference in the MCW with a change of vertical angle of the head phantom (Kruskal-Wallis test, P=0.406). Analysis of the MCW in the panoramic radiographs of young females showed a normal distribution (P=0.074). The threshold value corresponding to −2.5 SD was 2.46 mm. When this value was applied to patients with a known femur BMD value, the sensitivity, specificity, and accuracy were 60.0%, 96.7%, and 91.4%, respectively. The area under the ROC curve was 0.947 (95% confidence interval 0.894-0.999, P=0.000). The cut-off value obtained from the ROC curve was 3.32 mm. Conclusion In conclusion, it is suggested that the developed computer-based screening program for osteoporosis may have a validity in Korean women. If the cut-off value would be obtained from a big-data study, it could be used to screen for osteoporosis in patients who have undergone panoramic radiographic imaging in dental clinics.I. Introduction II. Materials and Methods III. Result IV. Discussion V. Conclusion VI. References Abstract (Korean)Docto

Analysis of temporomandibular joint dysfunction in paediatric patients with unilateral crossbite using automatically generated finite element models

The evaluation of temporomandibular joint (TMJ) dysfunction using finite element models is a time consuming process that requires extensive technical knowledge. We combined a statistical active appearance model with automated modelling algorithms to biomechanically study the relationship between TMJ malformations and dysfunction in radiographs from 20 paediatric patients with unilateral crossbite. A fitting algorithm (fitting error < 4%) recognised the TMJ shape and adjusted the dimensions of each patient-specific 2D FE model, which was then used to compute 2 different joint movements. Significant functional differences were observed between the crossbite and non-cross bite sides, and the shape-function relation was verified

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion

Oral manifestations of menopause: an interprofessional intervention for dental hygiene and physician assistant students

Purpose: Interprofessional education (IPE) is a means of fostering integration and collaboration between health care professions. This study evaluated the effect of an IPE educational module on the oral manifestations of menopause. Methods: This mixed-method study used a convenience sample of dental hygiene (DH) and physician assistants (PA) students. Pre- and posttests collected quantitative data using a modified Readiness for Interprofessional Learning Survey (RIPLS), and a PI-designed knowledge of menopause test to determine students attitudes and learning. Students participated in a one-time workshop that included an educational lecture and a case study exercise using a pseudo-standardized patient. Students worked in preselected groups, representing both disciplines, to create a patient care plan addressing oral manifestations of menopause. Qualitative data was collected from student comments. Results: Study results indicate an increase in participants\u27 knowledge of oral manifestations of menopause. Additionally, results suggest improved attitudes toward interprofessional teamwork and collaboration, professional identity, roles and responsibilities and interprofessional communication. Finally, data shows facilitation of gained confidence in applying new skills related to oral manifestations of menopause. Conclusion: Implementation of an IPE intervention demonstrated correlation between an IPE experience and participants\u27 attitudes, learning, and confidence. Patients experiencing menopause are prone to oral manifestation. Therefore, preparing students to meet the needs of menopausal women may ultimately decrease oral discomfort and improve quality of life --Leaf x

Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others

Periodontal Disease

This book aims to provide readers with the latest updates and an informative overview of the most successful diagnostic aids for periodontal diseases. This book is divided into three sections. Section 1 discusses the periodontal disease pathogenesis and how the disease develops and the contributing factors in disease development. Section 2 includes three chapters that focus mainly on the most common and recent biomarkers that aid in diagnosis of periodontal diseases. Section 3 includes one chapter and discusses a non-surgical treatment modality that could provide definite improvement in the mild to moderate conditions in periodontal diseases