Mandibular cortical width measurement based on dental panoramic radiographs with computer-aided system

The paper presents a method of the determining a mandibular cortical width on dental panoramic radiographs. Cortical width of lower border of mandible may potentially be associated with recognition of osteoporosis in postmenopausal women. An algorithm to perform a semiautomatic cortical width measurement in a given region of interest was developed. The algorithm is based on separate extraction of lower and upper boundaries of cortical bone. Results of boundaries extraction performed on 34 panoramic radiographs of healthy and osteoporotic individuals are presented, together with automatic measurements of particular distances. They were compared with results of hand-made measurements done by two maxillofacial radiologists. Presented algorithm may potentially be useful for screening patients with osteoporosis

Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates

Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lack of discernible symptoms. Hence, screening for osteoporosis with widely used dental panoramic radiographs would be very cost-effective and useful. In this study, we investigate the use of deep learning to classify osteoporosis from dental panoramic radiographs. In addition, the effect of adding clinical covariate data to the radiographic images on the identification performance was assessed. For objective labeling, a dataset containing 778 images was collected from patients who underwent both skeletal-bone-mineral density measurement and dental panoramic radiography at a single general hospital between 2014 and 2020. Osteoporosis was assessed from the dental panoramic radiographs using convolutional neural network (CNN) models, including EfficientNet-b0, -b3, and -b7 and ResNet-18, -50, and -152. An ensemble model was also constructed with clinical covariates added to each CNN. The ensemble model exhibited improved performance on all metrics for all CNNs, especially accuracy and AUC. The results show that deep learning using CNN can accurately classify osteoporosis from dental panoramic radiographs. Furthermore, it was shown that the accuracy can be improved using an ensemble model with patient covariates

치과용 파노라마 방사선 사진에서 골다공증 선별을 위한 심층 합성곱 신경망(deep CNN)의 전이학습 전략

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의학과, 2020. 8. 최진욱.Osteoporosis is a metabolic bone disease characterized by low bone mass and disruption in bone micro-architecture. Clinical diagnostic methods for osteoporosis are expensive and therefore have limited availability in population. Recent studies have shown that Dental Panoramic Radiographs (DPRs) can provide the bone density change clues in bone structure analysis. This study aims to evaluate the discriminating performance of deep convolutional neural networks (CNNs), employed with various transfer learning strategies, on the classification of specific features of osteoporosis in DPRs. For objective labeling, we collected a dataset containing 680 images from different patients who underwent both skeletal bone mineral density and digital panoramic radiographic examinations at the Korea University Ansan Hospital between 2009 and 2018. In order to select the backbone convolutional neural network which is the basis for applying the transfer learning, we conducted preliminary experiments on the three convolutional neural networks, VGG-16, Resnet50, and Xception networks, which were frequently used in image classification. Since VGG-16 showed the best AUC value in the classification experiment conducted without transfer learning, the transfer learning using the fine-tunning technique was tested using VGG-16 as the backbone network. In order to find the optimal fine-tuning degree in the VGG-16 network, a total of six fine-tuning applied transfer learning groups were set according to the number of fine-tuning blocks in the VGG-16 with five blocks as follows: A group that does not perform fine-tuning at all (VGG-16-TF0), a group that fine-tunes the last 1 block (VGG-16-TF1), a group that fine-tuning the last 2 blocks (VGG-16-TF2), a group that fine-tuning the last 3 blocks (VGG-16-TF3), a group that fine-tuning the last 4 blocks (VGG-16-TF4), and a group that performs fine-tuning all 5 blocks (VGG-16-SCR).The best performing model (VGG-16-TF2) achieved an overall area under the receiver operating characteristic of 0.858. In this study, transfer learning and optimal fine-tuning improved the performance of a deep CNN for screening osteoporosis in DPR images. In addition, using the gradient-weighted class activation mapping technique, a visual interpretation of the best performing deep CNN model indicated that the model relied on image features in the lower left and right border of the mandibular. This result suggests that deep learning-based assessment of DPR images could be useful and reliable in the automated screening of osteoporosis patients.골다공증은 골밀도가 낮고 골 미세 구조의 붕괴가 특징 인 대사성 골 질환입니다. 그러나, 골다공증에 대한 임상 진단 방법중에 하나인 DXA 검사는 대형의 검사용 엑스레이 장비가 별도로 필요하고 검사비용이 높아, 해당 검사의 이용성에 제한성이 있습니다. 최근 연구에 따르면 치과 파노라마 방사선 사진 (DPR) 또한 골 밀도 변화를 예측 할 수 있다고 연구되었습니다. 이에 본 연구는 DPR에서 골다공증에 의한 골 밀도 변화에 따른 엑스레이 영상 특이성 분류에 다양한 전이 학습전략을 적용한 심층 합성곱 신경망 (CNN)의 분류 성능을 평가하는 것에 목표로 두었습니다. 합습 및 검증용 데이터의 객관적인 라벨링을 위해 2009년부터 2018년까지 고려 대학교 안산 병원에서 골밀도 검사와 디지털 파노라마 방사선 촬영을 6개월 이내에 동시에 시행한 환자들로부터 680개의 데이터 세트를 수집했습니다. 전이 학습 전 기본이 되는 합성곱 신경망을 선택하기 위해 이미지 분류에 자주 사용되는 3개의 합성곱 신경망 인 VGG-16, Resnet-50 및 Xception 네트워크에 대해 전이학습이 없는 상태로 사전 분류성능 평가를 수행했습니다. VGG-16은 전이 학습 없이 수행 된 분류 성능 평가에서 다른 2개의 네트워크에 비해 높은 AUC 값을 보여 주었기에, 해당 네트워크를 백본(back-bone) 네트워크로 사용하여 전이학습 효과를 비교 분석하였습니다. 백본 네트워크에서 최적의 fine-tuning 정도를 찾기 위해 VGG-16에 fine-tuning이 적용 가능한 블록 수에 따라 총 6 개의 fine-tuning 적용 전이 학습 그룹이 다음과 같이 설정 하였습니다. fine-tuning을 전혀 하지 않는 그룹 (VGG16-TR0), 마지막 1 블록을 fine-tuning 하는 그룹 (VGG-16-TF1), 마지막 2 블록을 fine-tuning 하는 그룹 (VGG-16-TF2), 마지막 3 개 블록을 fine-tuning하는 그룹 (VGG-16-TF3), 마지막 4 개 블록을 fine-tuning하는 그룹 (VGG-16-TF4) 및 5 개 블록 모두를 fine-tuning하는 그룹 (VGG16-TR5). 실험 결과 최고 성능 모델 은 VGG-16-TF2 였으며, 분류 성능 값의 하나인 AUC 값이 0.858를 달성했습니다. 본 연구를 통하여 학습용 데이터 수에 제한이 있더라도, 전이 학습 및 fine-tuning을 통하여 DPR 이미지를 이용한 골다공증 스크리닝 성능의 개선이 가능함을 보여주었습니다. 또한 gradiant-CAM 기법을 이용하여 성능이 가장 우수한 CNN 모델의 시각적 해석을 통하여, DPR 이미지 상에서 적절한 골다공증의 분류성능은 하악골의 왼쪽 및 오른쪽 하연 경계에있는 이미지에 의존한다는 것을 확인 할 수 있었습니다. 본 결과는 DPR 이미지의 딥 러닝 기반 평가가 골다공증 환자의 자동 선별에 유용하고 신뢰할 수 있음을 시사 하였습니다.Chapter 1. Introduction 1 Chapter 2. Materials and Methods 8 2.1 Dataset Collection 8 2.2 Image Preprocessing 9 2.3 Cross validation 11 2.4 Back-bone Convolutional Neural Networks 14 2.5 Evaluation 16 2.6 Visualizing Model Decisions 18 Chapter 3. Results 19 3.1 Clinical and Demographic Characteristics 19 3.2 Back-bone Convolutional Neural Networks 20 3.3 Fine-Tuning of Transferred deep CNN 22 3.4 Evaluation 27 3.5 Visualizing Model Decisions 30 Chapter 4. Discussion 33 Chapter 5. Conclusion 42 References 42 Abstract 51Docto

Bone quality and quantity measurement techniques in dentistry

In dentistry, finding a reliable method for measurement mineral density of bone and bonestrength in jaw bones is not only beneficial for implant planning, but also benefical in earlydiagnosis of diseases which effects the mineral density of bone like osteoporosis. Dual-energyX-ray absorptiometry (DXA) method is considered as a gold standard for measurement of bonemineral density in medicine. DXA yields quantitative information on bone structure. In additionDXA, imaging methods which given detailed qualitative information have been developed.Finding a method, which can be also applied on jaw bones and will not damage to the patientand offering a correct information that might be contrubute in dentistry especially with respectto implant and periodontal operations.The aim of the present article, is to provide basic information to reader from past to presentabout main procedures on assessment of bone structure also including jaw bones and theadvantages and disadvantages of these methods

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

The Facial Skeleton in Patients with Osteoporosis: A Field for Disease Signs and Treatment Complications

Osteoporosis affects all bones, including those of the facial skeleton. To date the facial bones have not drawn much attention due to the minimal probability of morbid fractures. Hearing and dentition loss due to osteoporosis has been reported. New research findings suggest that radiologic examination of the facial skeleton can be a cost-effective adjunct to complement the early diagnosis and the follow up of osteoporosis patients. Bone-mass preservation treatments have been associated with osteomyelitis of the jawbones, a condition commonly described as osteonecrosis of the jaws (ONJ). The facial skeleton, where alimentary tract mucosa attaches directly to periosteum and teeth which lie in their sockets of alveolar bone, is an area unique for the early detection of osteoporosis but also for the prevention of treatment-associated complications. We review facial bone involvement in patients with osteoporosis and we present data that make the multidisciplinary approach of these patients more appealing for both practitioners and dentists. With regard to ONJ, a tabular summary with currently available evidence is provided to facilitate multidisciplinary practice coordination for the treatment of patients receiving bisphosphonates

Accuracy of Mandibular Panoramic Indices in the Assessment of Bone Mineral Density in Comparison with Dexa Scans among Post Menopause Women

BACKGROUND: Osteoporosis is one of the major health hazards in postmenopausal women in India, and approximately 46 billion women are with osteoporosis, mainly attributed to different levels of fracture. Vitamin D deficiency along with low calcium intake, early menopause, genetic predisposition, poor knowledge of bone health and lack of diagnostic facilities attributes to increased prevalence of osteoporosis. Diagnosis of osteoporosis is done by dual energy X- ray absorptiometry (DEXA), which is considered as a gold standard procedure by WHO. Data suggests that in India, there is approximately 0.26 DXA machine per million Indian populations. In recent years emphasis has been placed on detecting postmenopausal women with low bone mineral density using panoramic radiographs and predicting these radiographs as screening tools for osteoporosis. The present study was conducted to study the role of dentist as a potentially valuable resource to identify patients with asymptomatic low bone density and to guide the dentist in proper case selection of postmenopausal patients for implant placement. AIMS AND OBJECTIVES: To evaluate the accuracy of mandibular panoramic indices in assessing bone mineral density among postmenopausal women and compare with DXA scan. To evaluate the effectiveness of mandibular panoramic indices as a guide in case selection for implant placement in elderly patients. METHODOLOGY: The study consists of 20 patients. The patients included in this study were subjected to DEXA scan and digital OPG. Mandibular cortical index (MCI) and mental index (MI) were used to measure the quantity and quality of mandibular cortex in the Digital panoramic radiograph. All the data were entered in Microsoft excel sheets and Statistical analysis was done using SPSS software. RESULTS: The result showed that there was a significant accuracy of mandibular panoramic indices in assessing bone mineral density among postmenopausal women, compared with DXA scan with a statistically significant p valve of P < 0.001. Thus, mandibular panoramic indices can be used as a guide in case selection for implant placement in postmenopausal and elderly patients seeking dental rehabilitation. CONCLUSION: Mandibular panoramic indices are effective in identifying post-menopausal women with low bone mineral density and can be used as a valuable guide in case selection for implant placement in elderly patients

Assessing minipig compact jawbone quality at the microscale

Preclinical studies often require animal models for in vivo experiments. Particularly in dental research, pig species are extensively used due to their anatomical similarity to humans. However, there is a considerable knowledge gap on the multiscale morphological and mechanical properties of the miniature pigs’ jawbones, which is crucial for implant studies and a direct comparison to human tissue. In the present work, we demonstrate a multimodal framework to assess the jawbone quantity and quality for a minipig animal model that could be further extended to humans. Three minipig genotypes, commonly used in dental research, were examined: Yucatan, G ̈ottingen, and Sinclair. Three animals per genotype were tested. Cortical bone samples were extracted from the premolar region of the mandible, opposite to the teeth growth. Global morphological, compositional, and mechanical properties were assessed using micro-computed tomography (micro-CT) together with Raman spectroscopy and nano- indentation measurements, averaged over the sample area. Local mineral-mechanical relationships were investigated with the site-matched Raman spectroscopy and micropillar compression tests. For this, a novel femtosecond laser ablation protocol was developed, allowing high-throughput micropillar fabrication and testing without exposure to high vacuum. At the global averaged sample level, bone relative mineralization demonstrated a significant difference between the genotypes, which was not observed from the complementary micro-CT measurements. Moreover, bone hardness measured by nanoindentation showed a positive trend with the relative mineralization. For all genotypes, significant differences between the relative mineralization and elastic properties were more pronounced within the osteonal regions of cortical bone. Site-matched micropillar compression and Raman spectroscopy highlighted the differences between the genotypes’ yield stress and mineral to matrix ratios. The methods used at the global level (averaged over sample area) could be potentially correlated to the medical tools used to assess jawbone toughness and morphology in clinics. On the other hand, the local analysis methods can be applied to quantify compressive bone mechanical properties and their relationship to bone mineralization

Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015-2016

Objective To apply deep learning to a data set of dental panoramic radiographs to detect the mental foramen for automatic assessment of the mandibular cortical width. Methods Data from the seventh survey of the Tromsø Study (Tromsø7) were used. The data set contained 5197 randomly chosen dental panoramic radiographs. Four pretrained object detectors were tested. We randomly chose 80% of the data for training and 20% for testing. Models were trained using GeForce RTX 2080 Ti with 11 GB GPU memory (NVIDIA Corporation, Santa Clara, CA, USA). Python programming language version 3.7 was used for analysis. Results The EfficientDet-D0 model showed the highest average precision of 0.30. When the threshold to regard a prediction as correct (intersection over union) was set to 0.5, the average precision was 0.79. The RetinaNet model achieved the lowest average precision of 0.23, and the precision was 0.64 when the intersection over union was set to 0.5. The procedure to estimate mandibular cortical width showed acceptable results. Of 100 random images, the algorithm produced an output 93 times, 20 of which were not visually satisfactory. Conclusions EfficientDet-D0 effectively detected the mental foramen. Methods for estimating bone quality are important in radiology and require further development

General and local predictors of mandibular cortical bone morphology in adult females and males: the seventh survey of the Tromsø Study

Objectives To analyze factors predicting mandibular cortical width (MCW) and mandibular cortical index (MCI) in adult females and males. Material and methods Data on 427 females and 335 males aged 40–84 from The Tromsø study: Tromsø7 were used. T-score, age, menopausal status (for females), remaining teeth, and periodontal status were analyzed in linear and logistic regression analyses as predictors of MCW and MCI, respectively. Results T-score, age, and the number of remaining teeth significantly predicted MCW in females but not males. Standardized β coefficients were 0.286, −0.231, and 0.131, respectively. The linear regression model explained 24% of MCW variation in females. MCI in females was significantly predicted by T-score, age, and remaining teeth with the Wald values of 9.65, 6.17, and 5.83, respectively. The logistic regression model explained 16.3−23% of the variation in MCI in females. In males, T-score was the only significant predictor of the eroded cortex, and the logistic model explained only 4.3–5.8% of the variation in MCI. Conclusions The T-score demonstrated a stronger relationship with MCW and MCI than other factors in females, which supports the usefulness of those indices for osteoporosis screening. Conversely, the T-score exhibited no association with MCW and remained the only significant predictor of MCI in males, yet to a lesser extent than in females. Clinical relevance Understanding factors affecting mandibular cortical morphology is essential for further investigations of MCW and MCI usefulness for osteoporosis screening in females and males