[논문]멀티 모달리티 데이터 활용을 통한 골다공증 단계 다중 분류 시스템 개발: 합성곱 신경망 기반의 딥러닝 적용

하태준; 김희상; 강성욱; 이두희; 김우진; 문기원; 최현수; 김정현; 김윤; 박소현; 박상원

doi:10.7742/jksr.2024.18.3.187

[국내논문] 멀티 모달리티 데이터 활용을 통한 골다공증 단계 다중 분류 시스템 개발: 합성곱 신경망 기반의 딥러닝 적용
Multi-classification of Osteoporosis Grading Stages Using Abdominal Computed Tomography with Clinical Variables : Application of Deep Learning with a Convolutional Neural Network 원문보기

한국방사선학회 논문지 = Journal of the Korean Society of Radiology, v.18 no.3, 2024년, pp.187 - 201

하태준 (더존비즈온 플랫폼사업부) , 김희상 ((주)포소드 기술연구소) , 강성욱 (강원대학교병원 차세대정보산업실) , 이두희 ((주)지오비전) , 김우진 (강원대학교병원 내과) , 문기원 (강원대학교병원 내과) , 최현수 (강원대학교 의과대학 내과학교실) , 김정현 (강원대학교 의과대학 비뇨의학과) , 김윤 ((주)지오비전) , 박소현 (서울아산병원 영상의학과) , 박상원 (강원대학교 의과대학 의료정보학과)

초록
AI-Helper

골다공증은 전 세계적으로 주요한 건강 문제임에도 불구하고, 골절 발생 전까지 쉽게 발견되지 않는 단점을 가지고 있습니다. 본 연구에서는 골다공증 조기 발견 능력 향상을 위해, 복부 컴퓨터 단층 촬영(Computed Tomography, CT) 영상을 활용하여 정상-골감소증-골다공증으로 구분되는 골다공증 단계를 체계적으로 분류할 수 있는 딥러닝(Deep learning, DL) 시스템을 개발하였습니다. 총 3,012개의 조영제 향상 복부 CT 영상과 개별 환자의 이중 에너지 X선 흡수 계측법(Dual-Energy X-ray Absorptiometry, DXA)으로 얻은 T-점수를 활용하여 딥러닝 모델 개발을 수행하였습니다. 모든 딥러닝 모델은 비정형 이미지 데이터, 정형 인구 통계 정보 및 비정형 영상 데이터와 정형 데이터를 동시에 활용하는 다중 모달 방법에 각각 모델 구현을 실현하였으며, 모든 환자들은 T-점수를 통해 정상, 골감소증 및 골다공증 그룹으로 분류되었습니다. 가장 높은 정확도를 갖는 모델 우수성은 비정형-정형 결합 데이터 모델이 가장 우수하였으며, 수신자 조작 특성 곡선 아래 면적이 0.94와 정확도가 0.80를 제시하였습니다. 구현된 딥러닝 모델은 그라디언트 가중치 클래스 활성화 매핑(Gradient-weighted Class Activation Mapping, Grad-CAM)을 통해 해석되어 이미지 내에서 임상적으로 관련된 특징을 강조했고, 대퇴 경부가 골다공증을 통해 골절 발생이 높은 위험 부위임을 밝혔습니다. 이 연구는 DL이 임상 데이터에서 골다공증 단계를 정확하게 식별할 수 있음을 보여주며, 조기에 골다공증을 탐지하고 적절한 치료로 골절 위험을 줄일 수 있는 복부 컴퓨터 단층 촬영 영상의 잠재력을 제시할 수 있습니다.

Abstract ▼ AI-Helper

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

Keyword

표/그림 (9)

그림 Fig. 1. Patient classification flowchart for modeling. Patients who scanned abdominal in CT in each group (normal, osteopenia, and osteoporosis) were classified. Within the training data, 20% was used as test data. Abbreviation: CT, computed tomography; DXA, dual-energy x-ray absorptiometry.
그림 Fig. 2. Femur images from abdominal coronal CT. The images were cropped to 250*250 at the bottom left and right depending on the inspection area.
그림 Fig. 3. The process for layer staking of deep learning model
그림 Fig. 4. The results presented by Grad-CAM for each grading stage. The images used and the extracted gradients were averaged to obtain the overall results. The averaged image and gradient were mixed and presented at opacity of 0.7 and 0.3, respectively.
표 Table 1. Patients characteristics
표 Table 2. Model performance
표 Table 3. Comparison results of this study with those of other studies
그림 Fig. 5. T-score distribution showing errors between the predicted and the correct results.
표 연구자 정보 이력

AI 본문요약
AI-Helper

제안 방법

Consequently, we developed models to evaluate the patients’ bone density risk utilizing their CT images or demographic/clinical variables, such as sex, age, height, weight, and body mass index (BMI).
The training image, rotation, zoom in/out, and translation (up, down, left, and right) methods were randomly changed from –10 to 10, and a vertical/horizontal flip method was applied
The proposed model was divided into segments designed to learn from both images and demographic information. Specifically, we constructed a model dedicated to processing CT images and demographic data while also conducting a comprehensive analysis by combining both datasets. In addition, we conducted a multi-modal data analysis by combining both sets of data.
First, our study results provide an opportunity to overcome the shortcomings of DXA and quickly respond to the potential risk of the disease using widely used and easily obtained CT images and demographic data. In particular, we utilized real-world clinical data to address the challenge of concurrently classifying individuals into the normal, osteopenia, and osteoporotic stages, leveraging observations from the femur region in abdominal CT. Previous studies have primarily focused on classifying individuals as either normal or having osteoporosis, with limited attention given to classifying osteopenia—a stage that falls between the two disease categories—as either normal or osteoporotic.

이론/모형

Therefore, in this study, we implemented a DL framework for the multi-classification of osteoporosis using abdominal CT. We also compared the performance of abdominal CT with demographic and clinical information acquired from BMD and further investigated the effect of combining the two-modality information for osteoporosis diagnosis performance.
To identify informative features extracted through the CNN, Grad-CAM was used (Fig. 4), allowing the visualization of the results during the analysis process.

성능/효과

Therefore, in this study, we implemented a DL framework for the multi-classification of osteoporosis using abdominal CT. We also compared the performance of abdominal CT with demographic and clinical information acquired from BMD and further investigated the effect of combining the two-modality information for osteoporosis diagnosis performance.
3, the DL model architecture for extracting image features consists of six convolutional layers (16,32,64,128,256,512); kernel_size = 3, same padding, and Maxpool2D were applied to each layer to use the activation function of rectified linear unit (ReLU)
Because the images obtained all had different sizes depending on the characteristics of the patient, and many other parts besides the femur were included, it was necessary to obtain the regions of interest. All the images were cropped to 250 × 250 pixels to include the femoral neck and head.
All hyper-parameters were tuned during the validation phase. The validation set consisted of 20% of the training set. The proposed model was divided into segments designed to learn from both images and demographic information.
The adaptive moment estimation (Adam) a first-order gradient-based probability optimization algorithm optimizer was used with the mini-batch size was set to 32 and the epoch was set to 500. The learning rate was set to 0.001 with decay rate of 0.96 and adjusted to 70% if the performance did not improve after 30 epochs. Whereas, the model for only using demographic information consists of two fully connected layers.
The differences among the three groups for all variables were statistically significant (P < 0.001), as determined by ANOVA
Each modality is learned and transformed into a feature map, which is the key to the classification. The two generated feature maps were expressed by concatenation and classified into normal, osteopenia, and osteoporosis groups through two fully connected layers. Each layer uses a ReLU activation function and is configured to learn quickly using batch normalization.
The characteristics of all patients included in this study are presented in Table 1. The mean age of all patients was 58.1 years, with the average age of each group as follows: normal individuals = 36.6 years, individuals with osteopenia = 62.5 years, and individuals with osteoporosis = 76.5 years. Among all patients, 2,367 were females (78.
5 years. Among all patients, 2,367 were females (78.6%), of whom 777 (72.8%) were normal, 791 (85.5%) had osteopenia, and 799 (78.4%) had osteoporosis. Overall, the weight of the patients was decreased as the disease severity was increased; the mean weight of normal patients was 64.
4%) had osteoporosis. Overall, the weight of the patients was decreased as the disease severity was increased; the mean weight of normal patients was 64.2 kg. For patients with osteopenia, it was 59.
2 kg. For patients with osteopenia, it was 59.7 kg, and for patients with osteoporosis, it was 53.7 kg. The differences among the three groups for all variables were statistically significant (P < 0.
001), as determined by ANOVA. Post-hoc analysis of individual groups revealed statistically significant differences between all groups, except for BMI between the normal and osteopenia groups and sex between the normal and osteoporosis groups.
Table 2 suggests the model performance results. All metrics in the multi-modal data showed the highest performance, which was more significant compared with that of the model using only demographic information for grading stage classification. The area under the receiver operating characteristic curve (AUC) of the multi-modal model was the highest (0.
All metrics in the multi-modal data showed the highest performance, which was more significant compared with that of the model using only demographic information for grading stage classification. The area under the receiver operating characteristic curve (AUC) of the multi-modal model was the highest (0.94) with an accuracy (ACC) of 0.80, indicting a 10% better performance than that of the model using only demographic information. The model using demographic information only showed the worst performance with an AUC of 0.
In addition, there was a larger difference in sensitivity than in specificity between the two models. The model using a multi-modal dataset had a sensitivity and specificity of 0.80 and 0.90, respectively, whereas the model using only demographic data had a sensitivity and specificity of 0.69 and 0.84, respectively. Similar results were obtained for precision and recall.
To identify informative features extracted through the CNN, Grad-CAM was used (Fig. 4), allowing the visualization of the results during the analysis process. Fig.
Based on the Grad-Cam results, distinctive regions extracted from CT scans were associated with osteoporosis and fracture induced by osteoporosis, such as the greater trochanter and femoral neck regions.
The regions that contributed significantly to the model results are shown in green; notably, the femoral neck was observed in all stages.
In this study, the multi-classification of osteoporosis into different stages (normal, osteopenia, and osteoporosis) was conducted through DL with abdominal CT, which is already widely performed in clinical practice. Using the developed multi-modal DL model, CT images captured for diverse medical purposes can be used to screen latent patients at risk of osteoporosis without additional costs and radiation exposure.
Using the developed multi-modal DL model, CT images captured for diverse medical purposes can be used to screen latent patients at risk of osteoporosis without additional costs and radiation exposure. Our model, implemented for osteoporosis classification, demonstrated superior performance by incorporating both imaging and demographic/clinical information from individual patients. The model could provide an interpretable DL method to enhance clinician decision support systems (CDSS).
Importantly, our study offers several novelties for the multi-classification of osteoporosis into three stages based on the femur region on abdominal CT images. First, our study results provide an opportunity to overcome the shortcomings of DXA and quickly respond to the potential risk of the disease using widely used and easily obtained CT images and demographic data. In particular, we utilized real-world clinical data to address the challenge of concurrently classifying individuals into the normal, osteopenia, and osteoporotic stages, leveraging observations from the femur region in abdominal CT.
However, this study has several limitations. First, the performance was guaranteed only for contrast-enhanced abdominal CT data. Although CT is a common imaging modality, it seldom provides BMD information in the clinic owing to technical difficulties.
Nevertheless, abdominal CT is generally performed for patients who do not have kidney function abnormalities or contrast agent side effects. Accordingly, we conducted this study using contrast-enhanced abdominal CT scans, which demonstrated high accuracy in osteoporosis classification. Second, there was a time gap in the data collected in this study.
Accordingly, we conducted this study using contrast-enhanced abdominal CT scans, which demonstrated high accuracy in osteoporosis classification. Second, there was a time gap in the data collected in this study. All patient data were obtained using a concomitant CT scan within 3 months before or after the DXA scan to collect as much data as possible.
Second, there was a time gap in the data collected in this study. All patient data were obtained using a concomitant CT scan within 3 months before or after the DXA scan to collect as much data as possible. Therefore, based on a single DXA examination, two or more CT images may be matched for the same patient.
Therefore, based on a single DXA examination, two or more CT images may be matched for the same patient. Third, data were collected from a single medical institution. It is difficult to prove this effect using data obtained from other institutions or CT equipment.
Furthermore, to reinforce the reliability of the internal validation results obtained in this study, it is crucial to carry out external validation of the model. Fourth, we confirmed that using clinical structured data and unstructured CT image data simultaneously improved the performance compared to using individual data independently. However, the results obtained using only images were not significantly different from those obtained using multi-modal data.
This finding suggests that the demographic data used in this study had only a minor effect. Although demographic information shows a small effect in improving the overall performance of the model, this can indicate the possibility of generalization of the model and its versatility in clinical practice environments by using only the initial screening information of subjects. Improved performance can be expected by additionally using clinical variables directly related to bone density, such as drugs and disease history; however, this may result in a trade-off for widely used in clinical practice.
Therefore, although it is a six-layer CNN based model, we implemented and suggested a model optimized for the data used in this study. Fifth, our study based on a specific population and imaging may not be broadly applicable to diverse global populations due to variations in demographic, genetic, and environmental factors. This indicates that to enhance the robustness of the model, it will be necessary to gather more data in the future and to take into accounts the characteristics of various population groups.
This indicates that to enhance the robustness of the model, it will be necessary to gather more data in the future and to take into accounts the characteristics of various population groups. Sixth, given that most osteoporosis occurs in the elderly population, the model was built by including images from patients in their 20s who did not have a DXA scan to ensure the correct classification of osteoporosis and osteopenia through classification from a normal group with high bone density. We added a group of patients who were judged to be normal to minimize bias in the results and to ensure the correct classification of osteopenia and osteoporosis.
In conclusion, we developed a DL model for the multi-classification of osteoporosis using real-world clinical data combining CT scanned images with variables. Additionally, we discussed important features selected based on Grad-CAM technology.
This implies that DL can be fully applied to medical data for the classification of osteoporosis. In addition, our results suggest that abdominal CT could be used as important data in osteoporosis screening and lead to appropriate treatment for the reduction of osteoporotic fractures.
80, indicting a 10% better performance than that of the model using only demographic information. The model using demographic information only showed the worst performance with an AUC of 0.85 and an ACC of 0.68. In addition, there was a larger difference in sensitivity than in specificity between the two models.

후속연구

We excluded patients with foreign bodies resulting from femoral surgery, those with implanted artificial joints, or those lacking coronal CT phases from our study. Furthermore, as the majority of patients who underwent both DXA and CT within the study period exhibited abnormal bone density, we decided to include additional data from patients in their 20s who had exclusively undergone contrast-enhanced abdominal CT under identical conditions. A qualified radiologist assessed these patients, excluded those who did not have a fracture or chronic disease, and included those with normal bone density.
In particular, we used the results of the DXA scan, which is used as a diagnostic standard for osteoporosis in clinical practice, are used as labels for each disease group based on review by a radiologist, suggesting a high possibility of clinical application.
In general, using only CT images for diagnosing or screening osteoporosis has definite clinical limitations. Specifically, for the purpose of diagnosis for osteoporosis, DXA testing, which has the potential to become an international diagnostic standard, is necessary. However, given that contrast-enhanced abdominal CT is predominantly used unless there is impaired kidney function, our osteoporosis assessment was conducted using contrast-enhanced abdominal CT images.
However, given that contrast-enhanced abdominal CT is predominantly used unless there is impaired kidney function, our osteoporosis assessment was conducted using contrast-enhanced abdominal CT images. We anticipate that future studies involving multiple centers will be essential to validate and extend the findings of our research, which was based on CT images obtained from a single institution. In the future, we plan to collect more data from several machines and hospitals to reduce bias and increase robustness.
We anticipate that future studies involving multiple centers will be essential to validate and extend the findings of our research, which was based on CT images obtained from a single institution. In the future, we plan to collect more data from several machines and hospitals to reduce bias and increase robustness. Furthermore, to reinforce the reliability of the internal validation results obtained in this study, it is crucial to carry out external validation of the model.
Although we included a group of patients with normal bone density based on radiologist findings and diagnosis, the lack of DXA may be a limitation of this study. Further research may be needed to improve the model to be more robust by obtaining DXA results from a population with normal bone density.

참고문헌 (36)

T. Sozen, L. Ozisik, N. C. Basaran, "An overview？and management of osteoporosis", European Journal？of Rheumatology, Vol. 4, No. 1, pp. 46-56, 2017.？http://dx.doi.org/10.5152/EURJRHEUM.2016.048

상세보기
H. Abbouchie, N. Raju, A. Lamanna, C. Chiang, N.？Kutaiba, "Screening for osteoporosis using L1？vertebral density on abdominal CT in an Australian？population", Clinical Radiology, Vol. 77, No. 7, pp.？e540-e548, 2022.？http://dx.doi.org/10.1016/j.crad.2022.04.002

상세보기
F. Cosman, S. J. de Beur, M. S. LeBoff, E. M.？Lewiecki, B. Tanner, S. Randall, R. Lindsay,？"Clinician's Guide to Prevention and Treatment of？Osteoporosis", Osteoporosis International, Vol. 25,？No. 10, pp. 2359-2381, 2014.？http://dx.doi.org/10.1007/s00198-014-2794-2

상세보기
NIH Consensus Development Panel on Osteoporosis？Prevention, Diagnosis, and Therapy, "Osteoporosis？prevention, diagnosis, and therapy", The Journal of？the American Medical Association, Vol. 285, No. 6,？pp. 785-795, 2001.？http://dx.doi.org/10.1001/jama.285.6.785

상세보기
M. T. Loffler, A. Jacob, A. Scharr, N. Sollmann, E.？Burian, M. El Husseini, et al., "Automatic？opportunistic osteoporosis screening in routine CT:？improved prediction of patients with prevalent？vertebral fractures compared to DXA", European？Radiology, Vol. 31, No. 8, pp. 6069-6077, 2021.？http://dx.doi.org/10.1007/s00330-020-07655-2

상세보기
J. S. Yu, N. G. Krishna, M. G. Fox, D. G.？Blankenbaker, M. A. Frick, S. T. Jawetz, et al.,？"ACR Appropriateness Criteria® Osteoporosis and？Bone Mineral Density: 2022 Update", Journal of the？American College of Radiology, Vol. 19, No. 11, pp.？417-S432, 2022.？http://dx.doi.org/10.1016/j.jacr.2022.09.007

상세보기
S. H. Ahn, S. M. Park, S. Y. Park, J. I. Yoo, H. S. Jung, J. H. Nho, et al., "Osteoporosis and？Osteoporotic Fracture Fact Sheet in Korea", Journal？of Bone Metabolism, Vol. 27, No. 4, pp. 281-290,？2020. http://dx.doi.org/10.11005/jbm.2020.27.4.281

상세보기
H. P. Dimai, "Use of dual-energy X-ray？absorptiometry (DXA) for diagnosis and fracture risk？assessment; WHO-criteria, T- and Z-score, and？reference databases", Bone, Vol. 104, pp. 39-43,？2017. http://dx.doi.org/10.1016/j.bone.2016.12.016

상세보기
A. B. King, D. M. Fiorentino, "Medicare payment？cuts for osteoporosis testing reduced use despite tests'？benefit in reducing fractures", Health Affairs？(Millwood), Vol. 30, No. 12, pp. 2362-2370, 2011.？http://dx.doi.org/10.1377/hlthaff.2011.0233

상세보기
M. Pazianas, B. Abrahamsen, Y. Wang, R. G.？Russell, "Incidence of fractures of the femur,？including subtrochanteric, up to 8 years since？initiation of oral bisphosphonate therapy: a？register-based cohort study using the US MarketScan？claims databases", Osteoporosis International, Vol.？23, No. 12, pp. 2873-2884, 2012.？http://dx.doi.org/10.1007/s00198-012-1952-7

상세보기
A. L. Amarnath, P. Franks, J. A. Robbins, G. Xing,？J. J. Fenton, "Underuse and Overuse of Osteoporosis？Screening in a Regional Health System: a？Retrospective Cohort Study", Journal of General？Internal Medicine, Vol. 30, No. 12, pp. 1733-1740,？2015. http://dx.doi.org/10.1007/s11606-015-3349-8

상세보기
J. R. Curtis, L. Carbone, H. Cheng, B. Hayes, A.？Laster, R. Matthews, et al., "Longitudinal trends in？use of bone mass measurement among older？Americans, 1999-2005", Journal of Bone and？Mineral Research, Vol. 23, No. 7, pp. 1061-1067,？2008. http://dx.doi.org/10.1359/jbmr.080232

상세보기
S. J. Lee, P. A. Anderson, P. J. Pickhardt,？"Predicting Future Hip Fractures on Routine？Abdominal CT Using Opportunistic Osteoporosis？Screening Measures: A Matched Case-Control？Study", American Journal of Roentgenology, Vol.？209, No. 2, pp. 395-402, 2017.？http://dx.doi.org/10.2214/AJR.17.17820

상세보기
R. M. Summers, N. Baecher, J. Yao, J. Liu, P. J.？Pickhardt, J. R. Choi, et al., "Feasibility of？Simultaneous Computed Tomographic Colonography？and Fully Automated Bone Mineral Densitometry in？a Single Examination", Journal of Computed？Assisted Tomography, Vol. 35, No. 2, pp. 212-216,？2011.？http://dx.doi.org/10.1097/RCT.0b013e3182032537

상세보기
P. J. Pickhardt, L. J. Lee, A. M. del Rio, T.？Lauder, R. J. Bruce, R. M. Summers, et al.,？"Simultaneous screening for osteoporosis at CT？colonography: bone mineral density assessment using？MDCT attenuation techniques compared with the？DXA reference standard", Journal of Bone and？Mineral Research, Vol. 26, No. 9, pp. 2194-2203,？2011. http://dx.doi.org/10.1002/jbmr.428

상세보기
P. J. Pickhardt, B. D. Pooler, T. Lauder, A. M. del？Rio, R. J. Bruce, N. Binkley, "Opportunistic？screening for osteoporosis using abdominal computed？tomography scans obtained for other indications",？Annals of Internal Medicine, Vol. 158, No. 8, pp.？588-595, 2013.？http://dx.doi.org/10.7326/0003-4819-158-8-201304160-00003

상세보기
S. J. Lee, N. Binkley, M. G. Lubner, R. J. Bruce,？T. J. Ziemlewicz, P. J. Pickhardt, "Opportunistic？screening for osteoporosis using the sagittal？reconstruction from routine abdominal CT for？combined assessment of vertebral fractures and？density", Osteoporosis International, Vol. 27, No. 3,？pp. 1131-1136, 2016.？http://dx.doi.org/10.1007/s00198-015-3318-4

상세보기
M. P. Rosen, B. Siewert, D. Z. Sands, R.？Bromberg, J. Edlow, V. Raptopoulos, "Value of？abdominal CT in the emergency department for？patients with abdominal pain", European Radiology,？Vol. 13, No. 2, pp. 418-424, 2003.？http://dx.doi.org/10.1007/s00330-002-1715-5

상세보기
P. J. Pickhardt, T. Lauder, B. D. Pooler, A. Munoz？Del Rio, H. Rosas, R. J. Bruce, N. Binkley, "Effect？of IV contrast on lumbar trabecular attenuation at？routine abdominal CT: correlation with DXA and？implications for opportunistic osteoporosis screening",？Osteoporosis International, Vol. 27, No. 1, pp.？147-152, 2016.？https://doi.org/10.1007/s00198-015-3224-9

상세보기
B. Suh, H. Yu, H. Kim, S. Lee, S. Kong, J. W.？Kim, et al., "Interpretable Deep-Learning Approaches？for Osteoporosis Risk Screening and Individualized？Feature Analysis Using Large Population-Based？Data: Model Development and Performance？Evaluation", Journal of Medical Internet Research,？Vol. 25, e40179, 2023.？http://dx.doi.org/10.2196/40179

상세보기
Y. LeCun, Y. Bengio, G. Hinton, "Deep learning",？Nature, Vol. 521, No. 7553, pp. 436-444, 2015.？https://doi.org/10.1038/nature14539

상세보기
A. Tarekegn, F. Ricceri, G. Costa, E. Ferracin, M.？Giacobini, "Predictive Modeling for Frailty？Conditions in Elderly People: Machine Learning？Approaches", JMIR Medical Informatics, Vol. 8, No.？6, e16678, 2020. https://doi.org/10.2196/16678

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J. Smets, E. Shevroja, T. Hugle, W. D. Leslie, D.？Hans, "Machine Learning Solutions for？Osteoporosis-A Review", Journal of Bone and？Mineral Research, Vol. 36, No. 5, pp. 833-851,？2021. http://dx.doi.org/10.1002/jbmr.4292

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N. Yamamoto, S. Sukegawa, A. Kitamura, R. Goto,？T. Noda, K. Nakano, et al., "Deep Learning for？Osteoporosis Classification Using Hip Radiographs？and Patient Clinical Covariates", Biomolecules, Vol.？10, No. 11, 1534, 2020.？http://dx.doi.org/10.3390/biom10111534

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M. Zeytinoglu, R. K. Jain, T. J. Vokes, "Vertebral？fracture assessment: Enhancing the diagnosis,？prevention, and treatment of osteoporosis", Bone,？Vol. 104, pp. 54-65, 2017.？http://dx.doi.org/10.1016/j.bone.2017.03.004

상세보기
J. A. Kanis, "Assessment of fracture risk and its？application to screening for postmenopausal？osteoporosis: synopsis of a WHO report",？Osteoporosis International, Vol. 4, No. 6, pp.？368-381, 1994. http://dx.doi.org/10.1007/BF01622200

상세보기
R. R. Selvaraju, M. Cogswell, A. Das, R.？Vedantam, D. Parikh, D. Batra, "Grad-cam: Visual？explanations from deep networks via gradient-based？localization", In Proceedings of the IEEE？international conference on computer vision, pp.？618-626,
G. Yang, H. Wang, Z. Wu, Y. Shi, Y. Zhao,？"Prediction of osteoporosis and osteopenia by routine？computed tomography of the lumbar spine in？different regions of interest", Journal of Orthopaedic？Surgery and Research, Vol. 17, No. 1, pp. 454,？2022. http://dx.doi.org/10.1186/s13018-022-03348-2

상세보기
Y. W. Kim, J. H. Kim, S. H. Yoon, J. H. Lee, C.？H. Lee, C. S. Shin, et al., "Vertebral bone？attenuation on low-dose chest CT: quantitative？volumetric analysis for bone fragility assessment",？Osteoporosis International, Vol. 28, No. 1, pp.？329-338, 2017.？http://dx.doi.org/10.1007/s00198-016-3724-2

상세보기
B. Zhang, K. Yu, Z. Ning, K. Wang, Y. Dong, X.？Liu, et al., "Deep learning of lumbar spine X-ray？for osteopenia and osteoporosis screening: A？multicenter retrospective cohort study", Bone, Vol.？140, 115561, 2020.？http://dx.doi.org/10.1016/j.bone.2020.115561

상세보기
J. Liu, J. Wang, W. Ruan, C. Lin, D. Chen,？"Diagnostic and Gradation Model of Osteoporosis？Based on Improved Deep U-Net Network", Journal？of Medical Systems, Vol. 44, No. 1, 15, 2019.
K. Yasaka, H. Akai, A. Kunimatsu, S. Kiryu, O.？Abe, "Prediction of bone mineral density from？computed tomography: application of deep learning？with a convolutional neural network", European？Radiology, Vol. 30, No. 6, pp. 3549-3557, 2020.？http://dx.doi.org/10.1007/s00330-020-06677-0

상세보기
Z. C. Lipton, "The Mythos of Model Interpretability:？In machine learning, the concept of interpretability？is both important and slippery", Queue, Vol. 16,？No. 3, pp. 31-57, 2018.？https://doi.org/10.1145/3236386.3241340

상세보기
G. Jones, T. Nguyen, P. Sambrook, P. J. Kelly, J.？A. Eisman, "Progressive loss of bone in the femoral？neck in elderly people: longitudinal findings from？the Dubbo osteoporosis epidemiology study", BMJ,？Vol. 309, No. 6956, pp. 691-695, 1994.？http://dx.doi.org/10.1136/bmj.309.6956.691

상세보기
N. C. Wright, A. C. Looker, K. G. Saag, J. R.？Curtis, E. S. Delzell, S. Randall, et al., "The recent？prevalence of osteoporosis and low bone mass in？the United States based on bone mineral density at？the femoral neck or lumbar spine", Journal of Bone？and Mineral Research, Vol. 29, No. 11, pp.？2520-2526, 2014. http://dx.doi.org/10.1002/jbmr.2269

상세보기
J. A. Kanis, "Diagnosis of osteoporosis and？assessment of fracture risk", Lancet, Vol. 359, No.？9321, pp. 1929-1936, 2002.？http://dx.doi.org/10.1016/S0140-6736(02)08761-5

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