Detection of osteoporosis in lumbar spine [L1-L4] trabecular bone: a review article

The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy where as the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. This analysis is on the basis of bone mineral density (BMD) standards obtained through a variety of scientific methods experimented from different skeletal regions. The detection of osteoporosis in lumbar spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. This paper focuses on the advanced technology in imaging systems and fracture probability analysis of osteoporosis detection. The various segmentation techniques are explored to examine osteoporosis in particular region of the image and further significant attributes are extracted using different methods to classify normal and abnormal (osteoporotic) bones. The limitations of the reviewed papers are more in feature dimensions, lesser accuracy and expensive imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and DEXA. To overcome these limitations it is suggested to have less feature dimensions, more accuracy and cost-effective imaging modality like X-ray. This is required to avoid bone fractures and to improve BMD with precision which further helps in the diagnosis of osteoporosis

Application of artificial intelligence in the dental field : A literature review

Purpose: The purpose of this study was to comprehensively review the literature regarding the application of artificial intelligence (AI) in the dental field, focusing on the evaluation criteria and architecture types. Study selection: Electronic databases (PubMed, Cochrane Library, Scopus) were searched. Full-text articles describing the clinical application of AI for the detection, diagnosis, and treatment of lesions and the AI method/architecture were included. Results: The primary search presented 422 studies from 1996 to 2019, and 58 studies were finally selected. Regarding the year of publication, the oldest study, which was reported in 1996, focused on “oral and maxillofacial surgery.” Machine-learning architectures were employed in the selected studies, while approximately half of them (29/58) employed neural networks. Regarding the evaluation criteria, eight studies compared the results obtained by AI with the diagnoses formulated by dentists, while several studies compared two or more architectures in terms of performance. The following parameters were employed for evaluating the AI performance: accuracy, sensitivity, specificity, mean absolute error, root mean squared error, and area under the receiver operating characteristic curve. Conclusion: Application of AI in the dental field has progressed; however, the criteria for evaluating the efficacy of AI have not been clarified. It is necessary to obtain better quality data for machine learning to achieve the effective diagnosis of lesions and suitable treatment planning

Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system

<p>Abstract</p> <p>Background</p> <p>Early diagnosis of osteoporosis can potentially decrease the risk of fractures and improve the quality of life. Detection of thin inferior cortices of the mandible on dental panoramic radiographs could be useful for identifying postmenopausal women with low bone mineral density (BMD) or osteoporosis. The aim of our study was to assess the diagnostic efficacy of using kernel-based support vector machine (SVM) learning regarding the cortical width of the mandible on dental panoramic radiographs to identify postmenopausal women with low BMD.</p> <p>Methods</p> <p>We employed our newly adopted SVM method for continuous measurement of the cortical width of the mandible on dental panoramic radiographs to identify women with low BMD or osteoporosis. The original X-ray image was enhanced, cortical boundaries were determined, distances among the upper and lower boundaries were evaluated and discrimination was performed by a radial basis function. We evaluated the diagnostic efficacy of this newly developed method for identifying women with low BMD (BMD T-score of -1.0 or less) at the lumbar spine and femoral neck in 100 postmenopausal women (≥50 years old) with no previous diagnosis of osteoporosis. Sixty women were used for system training, and 40 were used in testing.</p> <p>Results</p> <p>The sensitivity and specificity using RBF kernel-SVM method for identifying women with low BMD were 90.9% [95% confidence interval (CI), 85.3-96.5] and 83.8% (95% CI, 76.6-91.0), respectively at the lumbar spine and 90.0% (95% CI, 84.1-95.9) and 69.1% (95% CI, 60.1-78.6), respectively at the femoral neck. The sensitivity and specificity for identifying women with low BMD at either the lumbar spine or femoral neck were 90.6% (95% CI, 92.0-100) and 80.9% (95% CI, 71.0-86.9), respectively.</p> <p>Conclusion</p> <p>Our results suggest that the newly developed system with the SVM method would be useful for identifying postmenopausal women with low skeletal BMD.</p

Stochastic Assessment of Bone Fragility in Human Lumbar Spine

Osteoporotic fractures are a vital public health concern and create a great economic burden for our society. It is estimated that more than 2 million fractures occur in the United States at a cost of $17 billion each year. Deterioration of microarchitecture of trabecular bone is considered as a major contributor to bone fragility. Current clinical imaging modalities such as Dual-energy X-ray absorptiometry (DXA) are not able to describe bone microarchitecture due to their low resolution. The main objective of this study was to obtain the relationship between stochastic parameters calculated from bone mineral density (BMD) maps of DXA scans and the microarchitecture parameters measured from three dimensional (3D) images of human lumbar vertebrae acquired using a Micro-Computed Tomography (Micro-CT) scanner. Eighteen human lumbar vertebrae with intact posterior elements were scanned in the posterior-anterior projection using a DXA scanner. Stochastic parameters such as correlation length (L), sill variance (C) and nugget variance ( ) were calculated by fitting a theoretical model onto the experimental variogram of the BMD map of the human vertebrae. In addition, microarchitecture parameters such as bone volume fraction (BV/TV), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular number (Tb.N), connectivity density (Conn.Dn), and bone surface-to-volume ratio (BS/BV) were measured from 3D images of the same human lumbar vertebrae. Significant correlations were observed between stochastic predictors and microarchitecture parameters of trabecular bone. Specifically, the sill variance was positively correlated with the bone volume fraction, trabecular thickness, trabecular number, connectivity density and negatively correlated with the bone surface to volume ratio and trabecular separation. This study demonstrates that stochastic assessment of the inhomogeneity of bone mineral density from routine clinical DXA scans of human lumbar vertebrae may have the potential to serve as a valuable clinical tool in enhancing the prediction of risks for osteoporotic fractures in the spine. The main advantage of using DXA scans is that it would be cost effective, since most hospitals already have DXA machines and there would be no need for purchasing new equipment

Bone health assessment via digital wrist tomosynthesis in the mammography setting

Bone fractures attributable to osteoporosis are a significant problem. Though preventative treatment options are available for individuals who are at risk of a fracture, a substantial number of these individuals are not identified due to lack of adherence to bone screening recommendations. The issue is further complicated as standard diagnosis of osteoporosis is based on bone mineral density (BMD) derived from dual energy x-ray absorptiometry (DXA), which, while helpful in identifying many at risk, is limited in fully predicting risk of fracture. It is reasonable to expect that bone screening would become more prevalent and efficacious if offered in coordination with digital breast tomosynthesis (DBT) exams, provided that osteoporosis can be assessed using a DBT modality. Therefore, the objective of the current study was to explore the feasibility of using digital tomosynthesis imaging in a mammography setting. To this end, we measured density, cortical thickness and microstructural properties of the wrist bone, correlated these to reference measurements from microcomputed tomography and DXA, demonstrated the application in vivo in a small group of participants, and determined the repeatability of the measurements. We found that measurements from digital wrist tomosynthesis (DWT) imaging with a DBT scanner were highly repeatable ex vivo (error = 0.05%-9.62%) and in vivo (error = 0.06%-10.2%). In ex vivo trials, DWT derived BMDs were strongly correlated with reference measurements (R = 0.841-0.980), as were cortical thickness measured at lateral and medial cortices (R = 0.991 and R = 0.959, respectively) and the majority of microstructural measures (R = 0.736-0.991). The measurements were quick and tolerated by human patients with no discomfort, and appeared to be different between young and old participants in a preliminary comparison. In conclusion, DWT is feasible in a mammography setting, and informative on bone mass, cortical thickness, and microstructural qualities that are known to deteriorate in osteoporosis. To our knowledge, this study represents the first application of DBT for imaging bone. Future clinical studies are needed to further establish the efficacy for diagnosing osteoporosis and predicting risk of fragility fracture using DWT

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

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