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

GABUNGAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX DAN GRAY LEVEL RUN LENGTH MATRIX PADA ANALISIS CITRA RADIOGRAFI DENTAL PANORAMIC UNTUK DETEKSI DINI OSTEOPOROSIS

ABSTRAKOsteoporosis merupakan salah satu masalah kesehatan utama. Osteoporosis dianggap sebagai penyakit metabolik yang umum, dan sering diabaikan. Penyakit ini kebanyakan menyerang wanita dewasa yang dapat menyebabkan  kekurusan dan kerapuhan tulang, dan memicu patah tulang. Osteoporosis didiagnosis dengan mengukur Densitas Mineral Tulang menggunakan DXA (dual energy X-ray absorptiometry). Perawatan dengan alat ini membutuhkan biaya yang mahal, dan alat ini tidak tersedia secara luas. Sampel penelitian ini mengambil 19 orang dengan kriteria inklusi perempuan telah menopause, dinyatakan sehat, tidak mengalami patah tulang dan tidak memiliki kelainan tulang sejak lahir. Sampel diukur nilai bone mineral density (BMD) atau derajat osteoporosis dengan menggunakan DXA. Kemudian dilakukan pemotretan radiografi untuk mendapatkan citra dental panoramic. Tahapan penelitian adalah: 1) melakukan pre-processing terhadap citra radiografi panoramic tulang mandibular; 2) menentukan nilai tekstur citra metode  gray level co-occurrence matrix 3) menentukan nilai tekstur citra metode  gray level run length matrix 4) mengkalisifikasikan menggunakan metode k means kluster. Hasil Klasifikasi dengan menggunakan k means Kluster menunjukkan ketepatan klasifikasi sebesar 89,47% Kata kunci: radiografi; citra tulang rahang; BMD; analisis tekstur. ABSTRACTOsteoporosis is one of the major health problems. Osteoporosis is considered a common metabolic disease, and is often overlooked. This disease mostly affects adult women which can cause thin and brittle bones, and trigger fractures. Osteoporosis is diagnosed by measuring Bone Mineral Density using DXA (dual energy X-ray absorptiometry). Treatment with this device is expensive, and it is not widely available. The sample of this study took 19 people with the inclusion criteria of women having menopause, declared healthy, had no fractures and had no bone abnormalities since birth. The sample was measured the value of bone mineral density (BMD) or the degree of osteoporosis using DXA. Then, radiography was taken to obtain a panoramic dental image. The stages of the research are: 1) pre-processing the panoramic radiographic image of the mandible; 2) determine the texture value of the image using the gray level co-occurrence matrix method 3) determine the texture value of the image using the gray level run length matrix method 4) classify it using the k means cluster method.Classification results using k means clusters show the classification accuracy of 89.47% Keywords:. Radiography; dental panoramic; BMD; texture analysi

A comparative study of salivary and serum calcium and alkaline phosphatase in patients with osteoporosis

Background: This study was undertaken to investigate the changes in salivary and serum calcium and alkaline phosphatase in osteoporosis patients. The objective was to compare the change in serum levels with those in saliva.Methods: The study was conducted in the department of biochemistry, National Institute of Medical Sciences and Hospital, Shobha Nagar, Jaipur, Rajasthan, India. Subjects were selected from department of orthopedics, National Institute of Medical Sciences and Hospital, Shobha Nagar, Jaipur, Rajasthan, India. At the same time one hundred adult osteoporosis patients confirmed by DEXA were taken. Calcium and alkaline phosphatase were measured in serum and saliva of each patient. The data obtained was statistically analyzed.Results: Serum calcium has strong positive correlation with salivary calcium (r=0.726) while serum ALP and salivary ALP had weak positive correlation (r =0.453).Conclusions: Saliva can be used to measure calcium level instead of serum as it is non-invasive, quick and easy method

Patella radiograph image texture: The correlation with lumbar spine bone mineral density values

Osteoporosis is a common metabolic disease that is frequently overlooked. This disease primarily affects adult women and causes bone thinness and fragility, which leads to fractures. DXA (Dual Energy X-ray Absorptiometry) is used to diagnose osteoporosis by measuring bone mineral density. These devices are expensive and not widely available for treatment. This study aimed to find a correlation between the texture value of an image of the patellar bone and the density of the lumbar spine, which can then be used to detect osteoporosis. This study's sample size was 19 subjects, and their bone mineral density (BMD) was measured using DXA. An X-ray was then taken to obtain an image of the genu bone. The stages of the research are as follows: 1) preparing the X-ray image of the bone; 2) determining the image texture value method of gray level co-occurrence matrix 3) investigating the relationship between texture values and BMD in the lumbar spine. The correlation test results revealed a statistically significant correlation between the texture value and the BMD of the lumbar spine for the correlation and variance characteristics (P less than 0.05). As a result, the value of the texture of the image of the patella bone can be used to detect osteoporosis

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

Computer aided detection of oral lesions on CT images

Oral lesions are important findings on computed tomography images. They are difficult to detect on CT images because of low contrast, arbitrary orientation of objects, complicated topology and lack of clear lines indicating lesions. In this thesis, a fully automatic method to detect oral lesions from dental CT images is proposed to identify (1) Closed boundary lesions and (2) Bone deformation lesions. Two algorithms were developed to recognize these two types of lesions, which cover most of the lesion types that can be found on CT images. The results were validated using a dataset of 52 patients. Using non training dataset, closed boundary lesion detection algorithm yielded 71% sensitivity with 0.31 false positives per patient. Moreover, bone deformation lesion detection algorithm achieved 100% sensitivity with 0.13 false positives per patient. Results suggest that, the proposed framework has the potential to be used in clinical context, and assist radiologists for better diagnosis. --Abstract, page iv

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

Análisis de los cambios óseos de los maxilares en niños con osteogénesis imperfecta (OI) en tratamiento con antirresortivos

Objetivo General: Evaluar en las radiografías panorámicas por medio del programa ImageJ los cambios observados en el hueso cortical y trabeculado óseo de los maxilares en niños con OI y diferentes grados de severidad que están recibiendo o han recibido protocolo de tratamiento con fármacos antirresortivos (bifosfonatos, denosumab). Objetivos Específicos: - Evaluar los cambios en el ancho cortical mandibular (MCW) en las radiografías panorámicas de los niños con densidad mineral ósea normal (DMO) y con OI. - Valorar los cambios a nivel del hueso trabecular mediante el índice de dimensión fractal (FD) en las radiografías panorámicas de los niños con DMO y con OI. - Comparar el MCW y la FD en las radiografías panorámicas de niños con diferentes tipos de OI y considerando el protocolo de tratamiento administrado (fármaco administrado, número de ciclos, edad de inicio del tratamiento), observando los posibles cambios que se produzcan en el hueso cortical y en el trabeculado óseo