Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning

A vision transformer-convolutional neural network framework for decision-transparent dual-energy X-ray absorptiometry recommendations using chest low-dose CT

OBJECTIVE

This study introduces an ensemble framework that integrates Vision Transformer (ViT) and Convolutional Neural Networks (CNN) models to leverage their complementary strengths, generating visualized and decision-transparent recommendations for dual-energy X-ray absorptiometry (DXA) scans from chest low-dose computed tomography (LDCT).

METHODS

The framework was developed using data from 321 individuals and validated with an independent test cohort of 186 individuals. It addresses two classification tasks: (1) distinguishing normal from abnormal bone mineral density (BMD) and (2) differentiating osteoporosis from non-osteoporosis. Three field-of-view (FOV) settings-fitFOV (entire vertebra), halfFOV (vertebral body only), and largeFOV (fitFOV + 20 %)-were analyzed to assess their impact on model performance. Model predictions were weighted and combined to enhance classification accuracy, and visualizations were generated to improve decision transparency. DXA scans were recommended for individuals classified as having abnormal BMD or osteoporosis.

RESULTS

The ensemble framework significantly outperformed individual models in both classification tasks (McNemar test, p < 0.001). In the development cohort, it achieved 91.6 % accuracy for task 1 with largeFOV (area under the receiver operating characteristic curve [AUROC]: 0.97) and 86.0 % accuracy for task 2 with fitFOV (AUROC: 0.94). In the test cohort, it demonstrated 86.6 % accuracy for task 1 (AUROC: 0.93) and 76.9 % accuracy for task 2 (AUROC: 0.99). DXA recommendation accuracy was 91.6 % and 87.1 % in the development and test cohorts, respectively, with notably high accuracy for osteoporosis detection (98.7 % and 100 %).

CONCLUSIONS

This combined ViT-CNN framework effectively assesses bone status from LDCT images, particularly when utilizing fitFOV and largeFOV settings. By visualizing classification confidence and vertebral abnormalities, the proposed framework enhances decision transparency and supports clinicians in making informed DXA recommendations following opportunistic osteoporosis screening.

Total radius BMD correlates with the hip and lumbar spine BMD among post-menopausal patients with fragility wrist fracture in a machine learning model

Osteoporosis screening should be systematic in the group of over 50-year-old females with a radius fracture. We tested a phantom combined with machine learning model and studied osteoporosis-related variables. This machine learning model for screening osteoporosis using plain radiographs requires further investigation in larger cohorts to assess its potential as a replacement for DXA measurements in settings where DXA is not available.

PURPOSE

The main purpose of this study was to improve osteoporosis screening, especially in post-menopausal patients with fragility wrist fractures. The secondary objective was to increase understanding of the connection between osteoporosis and aging, as well as other risk factors.

METHODS

We collected data on 83 females > 50 years old with a distal radius fracture treated at Oulu University Hospital in 2019-2020. The data included basic patient information, WHO FRAX tool, blood tests, X-ray imaging of the fractured wrist, and DXA scanning of the non-fractured forearm, both hips, and the lumbar spine. Machine learning was used in combination with a custom phantom.

RESULTS

Eighty-five percent of the study population had osteopenia or osteoporosis. Only 28.4% of patients had increased bone resorption activity measured by ICTP values. Total radius BMD correlated with other osteoporosis-related variables (age r =  - 0.494, BMI r = 0.273, FRAX osteoporotic fracture risk r =  - 0.419, FRAX hip fracture risk r =  - 0.433, hip BMD r = 0.435, and lumbar spine BMD r = 0.645), but the ultra distal (UD) radius BMD did not. Our custom phantom combined with a machine learning model showed potential for screening osteoporosis, with the class-wise accuracies for "Osteoporotic vs. osteopenic & normal bone" of 76% and 75%, respectively.

CONCLUSION

We suggest osteoporosis screening for all females over 50 years old with wrist fractures. We found that the total radius BMD correlates with the central BMD. Due to the limited sample size in the phantom and machine learning parts of the study, further research is needed to make a clinically useful tool for screening osteoporosis.

Enhancing osteoporosis risk prediction using machine learning: A holistic approach integrating biomarkers and clinical data

Osteoporosis (OP) affects approximately 18 % of the global population, with osteoporosis-associated fractures impacting up to 37 million people annually. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its limitations, including restricted availability and radiation exposure, highlight the need for alternative screening methods. We developed a machine learning model to predict OP risk using routinely collected clinical data, deliberately excluding DXA measurements to ensure broad accessibility. Using data from NHANES cycles 2007-2014, we analyzed 7924 participants aged 50 years and older, identifying 1636 OP cases (20.6 %) and 6288 normal cases (79.4 %) through comprehensive criteria incorporating both WHO densitometric standards (T-scores ≤ -2.5) and anthropometric risk factors. We implemented a stacking ensemble model combining four specialized classifiers (Gradient Boosting, Random Forest, XGBoost, and LightGBM) with a logistic regression meta-classifier. The model achieved 93 % accuracy, an AUC of 0.94, and demonstrated robust performance through cross-validation (mean score: 0.929 ± 0.030). feature importance analysis revealed age (6.04 %), arm muscle circumference (5.61 %), and body weight (5.30 %) as the most influential predictors, followed by gender (3.28 %), BMI (2.71 %), and calcium intake (2.42 %). Additional significant predictors included folate (2.28 %), height (2.23 %), hand grip strength (2.21 %), and alkaline phosphatase (2.16 %). These biologically plausible relationships align with established clinical knowledge of OP risk factors. The model's strong performance metrics and reliance on readily available clinical data suggest its potential as a practical screening tool, particularly in settings with limited DXA access. All code and implementation details are openly available on GitHub, facilitating integration into existing healthcare systems. This approach offers a promising pathway for enhancing early OP detection and risk assessment across diverse healthcare settings.

A Neural Network Model for Intelligent Classification of Distal Radius Fractures Using Statistical Shape Model Extraction Features

OBJECTIVE

Distal radius fractures account for 12%-17% of all fractures, with accurate classification being crucial for proper treatment planning. Studies have shown that in emergency settings, the misdiagnosis rate of hand/wrist fractures can reach up to 29%, particularly among non-specialist physicians due to a high workload and limited experience. While existing AI methods can detect fractures, they typically require large training datasets and are limited to fracture detection without type classification. Therefore, there is an urgent need for an efficient and accurate method that can both detect and classify different types of distal radius fractures. To develop and validate an intelligent classifier for distal radius fractures by combining a statistical shape model (SSM) with a neural network (NN) based on CT imaging data.

METHODS

From August 2022 to May 2023, a total of 80 CT scans were collected, including 43 normal radial bones and 37 distal radius fractures (17 Colles', 12 Barton's, and 8 Smith's fractures). We established the distal radius SSM by combining mean values with PCA (Principal Component Analysis) features and proposed six morphological indicators across four groups. The intelligent classifier (SSM + NN) was trained using SSM features as input data and different fracture types as output data. Four-fold cross-validations were performed to verify the classifier's robustness. The SSMs for both normal and fractured distal radius were successfully established based on CT data. Analysis of variance revealed significant differences in all six morphological indicators among groups (p < 0.001). The intelligent classifier achieved optimal performance when using the first 15 PCA-extracted features, with a cumulative variance contribution rate exceeding 75%. The classifier demonstrated excellent discrimination capability with a mean area under the curve (AUC) of 0.95 in four-fold cross-validation, and achieved an overall classification accuracy of 97.5% in the test set. The optimal prediction threshold range was determined to be 0.2-0.4.

RESULTS

The SSMs for both normal and fractured distal radius were successfully established based on CT data. Analysis of variance revealed significant differences in all six morphological indicators among groups (p < 0.001). The intelligent classifier achieved optimal performance when using the first 15 PCA-extracted features, with a cumulative variance contribution rate exceeding 75%. The classifier demonstrated excellent discrimination capability with a mean AUC of 0.95 in four-fold cross-validation and achieved an overall classification accuracy of 97.5% in the test set. The optimal prediction threshold range was determined to be 0.2-0.4.

CONCLUSION

The CT-based SSM + NN intelligent classifier demonstrated excellent performance in identifying and classifying different types of distal radius fractures. This novel approach provides an efficient, accurate, and automated tool for clinical fracture diagnosis, which could potentially improve diagnostic efficiency and treatment planning in orthopedic practice.

A prediction model of pediatric bone density from plain spine radiographs using deep learning

Osteoporosis, a bone disease characterized by decreased bone mineral density (BMD) resulting in decreased mechanical strength and an increased fracture risk, remains poorly understood in children. Herein, we developed/validated a deep learning-based model to predict pediatric BMD using plain spine radiographs. Using a two-stage model, Yolov8 was applied for vertebral body detection to predict BMD values using a regression model based on ResNet-18, from which a low-BMD group was classified based on Z-scores of predicted BMD. Patients aged 10-20-years who underwent dual-energy X-ray absorptiometry and radiography within 6 months at our hospital were enrolled. Ultimately, 601 patients (mean age, 14 years 4 months [SD 2 years]; 276 males) were included. The model achieved robust performance in detecting vertebral bodies (average precision [AP] 50 = 0.97, AP [50:95] = 0.68) and predicting BMD, with significant correlation (r = 0.72), showing consistency across different vertebral segments and agreement (intraclass correlation coefficient: 0.64). Moreover, it successfully classified low-BMD groups (area under the receiver operating characteristic curve = 0.85) with high sensitivity (0.76) and specificity (0.87). This deep-learning approach shows promise for BMD prediction and classification, with potential to enhance early detection and streamline bone health management in high-risk pediatric populations.

The Application of Artificial Intelligence in Spine Surgery: A Scoping Review

BACKGROUND

A comprehensive review on the application of artificial intelligence (AI) within spine surgery as a specialty remains lacking.

METHODS

This scoping review was conducted upon PubMed and EMBASE databases according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Our analysis focused on publications from January 1, 2020, to March 31, 2024, with a specific focus on AI in the field of spine surgery. Review articles and articles predominantly concerning secondary validation of algorithms, medical physics, electronic devices, biomechanics, preclinical, and with a lack of clinical emphasis were excluded.

RESULTS

One hundred five studies were included after our inclusion/exclusion criteria were applied. Most studies (n = 100) were conducted through supervised learning upon prelabeled data sets. Overall, 38 studies used conventional machine learning methods upon predefined features, whereas 67 used deep learning methods, predominantly for medical image analyses. Only 25.7% of studies (27/105) collected data from more than 1,000 patients for model development and validation. Data originated from only a single center in 72 studies. The most common application was prognostication (38/105), followed by diagnosis (35/105), medical image processing (29/105), and surgical assistance (3/105).

CONCLUSION

The application of AI within the domain of spine surgery has significant potential to advance patient-specific diagnosis, management, and surgical execution.

Utility of osteoporosis screening based on estimation of bone mineral density using bidirectional chest radiographs with deep learning models

Introduction

Osteoporosis increases the risk of fragility fractures, especially of the lumbar spine and femur. As fractures affect life expectancy, it is crucial to detect the early stages of osteoporosis. Dual X-ray absorptiometry (DXA) is the gold standard for bone mineral density (BMD) measurement and the diagnosis of osteoporosis; however, its low screening usage is problematic. The accurate estimation of BMD using chest radiographs (CXR) could expand screening opportunities. This study aimed to indicate the clinical utility of osteoporosis screening using deep-learning-based estimation of BMD using bidirectional CXRs.

Methods

This study included 1,624 patients aged ≥ 20 years who underwent DXA and bidirectional (frontal and lateral) chest radiography at a medical facility. A dataset was created using BMD and bidirectional CXR images. Inception-ResNet-V2-based models were trained using three CXR input types (frontal, lateral, and bidirectional). We compared and evaluated the BMD estimation performances of the models with different input information.

Results

In the comparison of models, the model with bidirectional CXR showed the highest accuracy. The correlation coefficients between the model estimates and DXA measurements were 0.766 and 0.683 for the lumbar spine and femoral BMD, respectively. Osteoporosis detection based on bidirectional CXR showed higher sensitivity and specificity than the models with single-view CXR input, especially for osteoporosis based on T-score ≤ -2.5, with 92.8% sensitivity at 50.0% specificity.

Discussion

These results suggest that bidirectional CXR contributes to improved accuracy of BMD estimation and osteoporosis screening compared with single-view CXR. This study proposes a new approach for early detection of osteoporosis using a deep learning model with frontal and lateral CXR inputs. BMD estimation using bidirectional CXR showed improved detection performance for low bone mass and osteoporosis, and has the potential to be used as a clinical decision criterion. The proposed method shows potential for more appropriate screening decisions, suggesting its usefulness in clinical practice.

Osteoporosis Evaluation by Radiofrequency Echographic Multispectrometry (REMS) in Primary Healthcare

Background/Objectives: Radiofrequency echographic multispectrometry (REMS) technology has emerged as a promising alternative for osteoporosis diagnosis. This non-ionising, portable and accessible method enables early detection of osteoporosis in primary healthcare settings. The aim of this study was to assess the effectiveness of REMS in evaluating osteoporosis within primary healthcare. Methods: Bone mineral density was assessed in 86 participants trough 172 scans of the lumbar spine and femur, using REMS technology in two Portuguese primary healthcare units in Guarda. Results: In the lumbar spine evaluation, 51.2% of the participants had osteopenia and 31.4% osteoporosis; in the femur evaluation, 43.0% had osteopenia and 34.9% osteoporosis. The data indicated a significant prevalence of bone fragility. The bone mineral density estimated by radiofrequency echographic multispectrometry showed good agreement with the clinical diagnosis, suggesting that this technology is effective in the early detection of osteoporosis. Conclusions: Bone densitometry using REMS method, performed by a radiographer in primary healthcare settings, offers a viable and innovative alternative for the effective detection of osteoporosis and osteopenia.

An automatic deep learning-based bone mineral density measurement method using X-ray images of children

Background

Osteoporosis is a common bone disease characterized by low bone mineral density (BMD). Low BMD screening and early interventions during childhood can significantly decrease osteoporosis risk in adulthood. However, in clinical settings, the applicability of dual-energy X-ray absorptiometry (DXA), a technique to measure standard area BMD (aBMD), cannot adequately meet the diagnostic needs of the majority of the Chinese population. We aimed to achieve a comprehensive evaluation in clinical settings by taking a single X-ray image, which, in conjunction with the use of equivalent step phantoms, can assess bone age or injuries (such as sprains, fractures, or breaks) in the wrist while also measuring aBMD in the forearm, to further evaluate growth and development.

Methods

In the present study, we used routine X-ray images of the hand and forearm to measure aBMD with step phantom. First, based on the X-ray images, the regions of interest (ROIs) and step phantom used in clinical settings were automatically located and segmented; then, their average grayscale values were calculated. Second, after fitting the linear calibration relationship between the equivalent phantom thickness and grayscale value of the phantom, the effect of soft tissue on aBMD measurement was eliminated using a deep learning method. Finally, aBMD was measured.

Results

Our developed method was validated on 500 X-ray images taken at the clinic and compared with DXA-based aBMD measurements. Experiments revealed that the average correlation coefficient was 0.836.

Conclusions

The proposed method is an automatic method for measuring aBMD in children by utilizing X-ray images of hand and forearm. Furthermore, our findings suggest the effectiveness of the developed method, which provides a comparable performance to that of clinicians.

Osteoporosis screening using X-ray assessment and osteoporosis self-assessment tool for Asians in hip surgery patients

OBJECTIVES

As many patients with osteoporosis remain undiagnosed, we aimed to develop a simple method to efficiently screen for osteoporosis using a combination of anteroposterior hip X-ray assessment and the Osteoporosis Self-Assessment Tool for Asians (OSTA), which is calculated as (body weight - age) × 0.2.

METHODS

One hundred Japanese women (age: 73 ± 11 years, body weight: 54.4 ± 11.1 kg) who underwent hip surgery, anteroposterior hip X-ray, and DXA were included. Based on the DXA results of the total proximal femur, 35 cases were diagnosed with osteoporosis. Fifteen orthopaedic surgeons visually inspected the hip X-ray images and scored the suspicion of osteoporosis on a scale of 1-4 (1: very unlikely, 4: very suspicious), which is referred to as "pred-score." In addition, OSTA was calculated as a continuous variable (OSTA score). Osteoporosis was screened using the pred-score and OSTA score, and both scores were analyzed using the receiver operating characteristic curves.

RESULTS

The area under the curves (AUCs) of the pred-score and OSTA score were 0.626-0.875 and 0.817 across surgeons, respectively. When both scores were used, the AUC for screening osteoporosis ranged from 0.821 to 0.915 across surgeons. Significant improvement from AUCs calculated with the pred-score or OSTA score was found in 11 surgeons (73.3%).

CONCLUSION

The combination of X-ray assessment and OSTA can be used to screen for osteoporosis and has the potential to be used as a new simple screening tool in daily clinical practice.

Deep learning based screening model for hip diseases on plain radiographs

INTRODUCTION

The interpretation of plain hip radiographs can vary widely among physicians. This study aimed to develop and validate a deep learning-based screening model for distinguishing normal hips from severe hip diseases on plain radiographs.

METHODS

Electronic medical records and plain radiograph from 2004 to 2012 were used to construct two patient groups: the hip disease group (those who underwent total hip arthroplasty) and normal group. A total of 1,726 radiographs (500 normal hip radiographs and 1,226 radiographs with hip diseases, respectively) were included and were allocated for training (320 and 783), validation (80 and 196), and test (100 and 247) groups. Four different models were designed-raw image for both training and test set, preprocessed image for training but raw image for the test set, preprocessed images for both sets, and change of backbone algorithm from DenseNet to EfficientNet. The deep learning models were compared in terms of accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, and area under the receiver operating characteristic curve (AUROC).

RESULTS

The mean age of the patients was 54.0 ± 14.8 years in the hip disease group and 49.8 ± 14.9 years in the normal group. The final model showed highest performance in both the internal test set (accuracy 0.96, sensitivity 0.96, specificity 0.97, PPV 0.99, NPV 0.99, F1-score 0.97, and AUROC 0.99) and the external validation set (accuracy 0.94, sensitivity 0.93, specificity 0.96, PPV 0.95, NPV 0.93, F1-score 0.94, and AUROC 0.98). In the gradcam image, while the first model depended on unrelated marks of radiograph, the second and third model mainly focused on the femur shaft and sciatic notch, respectively.

CONCLUSION

The deep learning-based model showed high accuracy and reliability in screening hip diseases on plain radiographs, potentially aiding physicians in more accurately diagnosing hip conditions.

Deep learning-based multimodal integration of imaging and clinical data for predicting surgical approach in percutaneous transforaminal endoscopic discectomy

BACKGROUND

For cases of multilevel lumbar disc herniation (LDH), selecting the surgical approach for Percutaneous Transforaminal Endoscopic Discectomy (PTED) presents significant challenges and heavily relies on the physician's judgment. This study aims to develop a deep learning (DL)-based multimodal model that provides objective and referenceable support by comprehensively analyzing imaging and clinical data to assist physicians.

METHODS

This retrospective study collected imaging and clinical data from patients with multilevel LDH. Each segmental MR scan was concurrently fed into a multi-input ResNet 50 model to predict the target segment. The target segment scan was then input to a custom model to predict the PTED approach direction. Clinical data, including the patient's lower limb sensory and motor functions, were used as feature variables in a machine learning (ML) model for prediction. Bayesian optimization was employed to determine the optimal weights for the fusion of the two models.

RESULT

The predictive performance of the multimodal model significantly outperformed the DL and ML models. For PTED target segment prediction, the multimodal model achieved an accuracy of 93.8%, while the DL and ML models achieved accuracies of 87.7% and 87.0%, respectively. Regarding the PTED approach direction, the multimodal model had an accuracy of 89.3%, significantly higher than the DL model's 87.8% and the ML model's 87.6%.

CONCLUSION

The multimodal model demonstrated excellent performance in predicting PTED target segments and approach directions. Its predictive performance surpassed that of the individual DL and ML models.

Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis

BACKGROUND

Osteoporosis is one of the most common metabolic diseases that is characterized by a decrease in bone density and a loss of the quality of the bone structure. The use of deep learning in the prediction of osteoporosis can provide a non-invasive, cost-effective, and efficient approach. The aim of this study is to investigate the diagnostic accuracy of deep learning in the prediction of osteoporosis.

METHODS

This is a systematic review and meta-analysis study that was conducted on the diagnostic accuracy of deep learning algorithms for predicting osteoporosis. A literature search was performed in electronic databases including PubMed, Elsevier, and Google Scholar to identify relevant articles until December 1, 2023. Articles were searched in databases by combining related terms such as "deep learning", "convolutional neural network", and "osteoporosis". We conducted title, abstract, and full-text screening based on inclusion/exclusion criteria. Various metrics, such as sensitivity, specificity, and area under the curve (AUC), were used to assess the diagnostic performance of deep learning models.

RESULTS

Out of the 181 articles initially identified, 10 studies were included in the analysis. All studies used a convolutional neural network (CNN) as the deep learning model. Three studies investigated multiple deep learning models. Eight studies used various architectures of CNN, such as ResNet, VGG, and EfficientNet. The pooled sensitivity and specificity were 0.86 (95% CI, 0.82-0.89) and 0.89 (95% CI, 0.85-0.91), respectively. The bivariate approach's pooled SROC curve produced an AUC of 0.94 (95% CI 0.91-0.95). The Diagnostic Odds Ratio (DOR) for the deep learning models was 49.09 (95% CI, 28.74-83.84). Deeks' funnel plot asymmetry test (P = 0.4) suggested no potential publication bias.

CONCLUSIONS

Deep learning has an acceptable performance for the diagnosis of osteoporosis, even better than other ML algorithms. However, further research is needed to validate the findings of this study in clinical trials.

HarDNet-based deep learning model for osteoporosis screening and bone mineral density inference from hand radiographs

PURPOSE

Osteoporosis, affecting over 200 million individuals, often remains unrecognized and untreated, increasing the risk of fractures in older adults. Osteoporosis is typically diagnosed with bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA). This study aims to develop DeepDXA-Hand, a deep learning model using the efficient CNN-based deep learning architecture, for opportunistic osteoporosis screening from hand radiographs.

METHODS

DeepDXA-Hand utilizes a CNN-based, HarDNet, approach to predict BMD non-invasively. A total of 10,351 hand radiographs and DXA pairs were used for model training and validation. The model's interpretability was enhanced using GradCAM for hotspot analysis to determine the model's attention areas.

RESULTS

The predicted and ground truth BMD were significantly correlated with a correlation coefficient of 0.745. For binary classification of osteoporosis, DeepDXA-Hand demonstrated a sensitivity of 0.73, specificity of 0.83, and accuracy of 0.80, indicating its clinical potential. The model mainly focused on the carpal bones, such as the capitate, trapezoid, hamate, triquetrum, and the head of the second metacarpal bone, suggesting these areas provide radiological features for inferring BMD.

CONCLUSION

DeepDXA-Hand shows potential for the early detection of osteoporosis with high sensitivity and specificity. Further studies should explore its utility in predicting fracture risks.

MINI ABSTRACT

Osteoporosis affects millions and often goes undetected and untreated. DeepDXA-Hand, a HarDNet-based deep learning model, predicted bone mineral density with a correlation of 0.745 and classified osteoporosis with 0.80 accuracy. This model enhances early detection and has significant clinical potential as osteoporosis opportunistic screening tool.

A generic causality-informed neural network (CINN) methodology for quantitative risk analytics and decision support

In this paper, we develop a generic framework for systemically encoding causal knowledge manifested in the form of hierarchical causality structure and qualitative (or quantitative) causal relationships into neural networks to facilitate sound risk analytics and decision support via causally-aware intervention reasoning. The proposed methodology for establishing causality-informed neural network (CINN) follows a four-step procedure. In the first step, we explicate how causal knowledge in the form of directed acyclic graph (DAG) can be discovered from observation data or elicited from domain experts. Next, we categorize nodes in the constructed DAG representing causal relationships among observed variables into several groups (e.g., root nodes, intermediate nodes, and leaf nodes), and align the architecture of CINN with causal relationships specified in the DAG while preserving the orientation of each existing causal relationship. In addition to a dedicated architecture design, CINN also gets embodied in the design of loss function, where both intermediate and leaf nodes are treated as target outputs to be predicted by CINN. In the third step, we propose to incorporate domain knowledge on stable causal relationships into CINN, and the injected constraints on causal relationships act as guardrails to prevent unexpected behaviors of CINN. Finally, the trained CINN is exploited to perform intervention reasoning with emphasis on estimating the effect that policies and actions can have on the system behavior, thus facilitating risk-informed decision making through comprehensive "what-if" analysis. Two case studies are used to demonstrate the substantial benefits enabled by CINN in risk analytics and decision support.

Development and reporting of artificial intelligence in osteoporosis management

An abundance of medical data and enhanced computational power have led to a surge in artificial intelligence (AI) applications. Published studies involving AI in bone and osteoporosis research have increased exponentially, raising the need for transparent model development and reporting strategies. This review offers a comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements. A systematic search in the PubMed database, from December 17, 2020 to February 1, 2023 was conducted to identify AI articles that relate to osteoporosis. The quality assessment of the studies relied on the systematic evaluation of 12 quality items derived from the minimum information about clinical artificial intelligence modeling checklist. The systematic search yielded 97 articles that fell into 5 areas; bone properties assessment (11 articles), osteoporosis classification (26 articles), fracture detection/classification (25 articles), risk prediction (24 articles), and bone segmentation (11 articles). The average quality score for each study area was 8.9 (range: 7-11) for bone properties assessment, 7.8 (range: 5-11) for osteoporosis classification, 8.4 (range: 7-11) for fracture detection, 7.6 (range: 4-11) for risk prediction, and 9.0 (range: 6-11) for bone segmentation. A sixth area, AI-driven clinical decision support, identified the studies from the 5 preceding areas that aimed to improve clinician efficiency, diagnostic accuracy, and patient outcomes through AI-driven models and opportunistic screening by automating or assisting with specific clinical tasks in complex scenarios. The current work highlights disparities in study quality and a lack of standardized reporting practices. Despite these limitations, a wide range of models and examination strategies have shown promising outcomes to aid in the earlier diagnosis and improve clinical decision-making. Through careful consideration of sources of bias in model performance assessment, the field can build confidence in AI-based approaches, ultimately leading to improved clinical workflows and patient outcomes.

A bidirectional framework for fracture simulation and deformation-based restoration prediction in pelvic fracture surgical planning

Pelvic fracture is a severe trauma with life-threatening implications. Surgical reduction is essential for restoring the anatomical structure and functional integrity of the pelvis, requiring accurate preoperative planning. However, the complexity of pelvic fractures and limited data availability necessitate labor-intensive manual corrections in a clinical setting. We describe in this paper a novel bidirectional framework for automatic pelvic fracture surgical planning based on fracture simulation and structure restoration. Our fracture simulation method accounts for patient-specific pelvic structures, bone density information, and the randomness of fractures, enabling the generation of various types of fracture cases from healthy pelvises. Based on these features and on adversarial learning, we develop a novel structure restoration network to predict the deformation mapping in CT images before and after a fracture for the precise structural reconstruction of any fracture. Furthermore, a self-supervised strategy based on pelvic anatomical symmetry priors is developed to optimize the details of the restored pelvic structure. Finally, the restored pelvis is used as a template to generate a surgical reduction plan in which the fragments are repositioned in an efficient jigsaw puzzle registration manner. Extensive experiments on simulated and clinical datasets, including scans with metal artifacts, show that our method achieves good accuracy and robustness: a mean SSIM of 90.7% for restorations, with translational errors of 2.88 mm and rotational errors of 3.18°for reductions in real datasets. Our method takes 52.9 s to complete the surgical planning in the phantom study, representing a significant acceleration compared to standard clinical workflows. Our method may facilitate effective surgical planning for pelvic fractures tailored to individual patients in clinical settings.

A few-shot learning framework for the diagnosis of osteopenia and osteoporosis using knee X-ray images

OBJECTIVE

We developed a few-shot learning (FSL) framework for the diagnosis of osteopenia and osteoporosis in knee X-ray images.

METHODS

Computer vision models containing deep convolutional neural networks were fine-tuned to enable generalization from natural images (ImageNet) to chest X-ray images (normal vs. pneumonia, base images). Then, a series of automated machine learning classifiers based on the Euclidean distances of base images were developed to make predictions for novel images (normal vs. osteopenia vs. osteoporosis). The performance of the FSL framework was compared with that of junior and senior radiologists. In addition, the gradient-weighted class activation mapping algorithm was used for visual interpretation.

RESULTS

In Cohort #1, the mean accuracy (0.728) and sensitivity (0.774) of the FSL models were higher than those of the radiologists (0.512 and 0.448). A diagnostic pipeline of FSL model (first)-radiologists (second) achieved better performance (0.653 accuracy, 0.582 sensitivity, and 0.816 specificity) than radiologists alone. In Cohort #2, the diagnostic pipeline also showed improved performance.

CONCLUSIONS

The FSL framework yielded practical performance with respect to the diagnosis of osteopenia and osteoporosis in comparison with radiologists. This retrospective study supports the use of promising FSL methods in computer-aided diagnosis tasks involving limited samples.

Developing a Computer Vision Model to Automate Quantitative Measurement of Hip-Knee-Ankle Angle in Total Hip and Knee Arthroplasty Patients

BACKGROUND

Increasing deformity of the lower extremities, as measured by the hip-knee-ankle angle (HKAA), is associated with poor patient outcomes after total hip and knee arthroplasty (THA, TKA). Automated calculation of HKAA is imperative to reduce the burden on orthopaedic surgeons. We proposed a detection-based deep learning (DL) model to calculate HKAA in THA and TKA patients and assessed the agreement between DL-derived HKAAs and manual measurement.

METHODS

We retrospectively identified 1,379 long-leg radiographs (LLRs) from patients scheduled for THA or TKA within an academic medical center. There were 1,221 LLRs used to develop the model (randomly split into 70% training, 20% validation, and 10% held-out test sets); 158 LLRs were considered "difficult," as the femoral head was difficult to distinguish from surrounding tissue. There were 2 raters who annotated the HKAA of both lower extremities, and inter-rater reliability was calculated to compare the DL-derived HKAAs with manual measurement within the test set.

RESULTS

The DL model achieved a mean average precision of 0.985 on the test set. The average HKAA of the operative leg was 173.05 ± 4.54°; the nonoperative leg was 175.55 ± 3.56°. The inter-rater reliability between manual and DL-derived HKAA measurements on the operative leg and nonoperative leg indicated excellent reliability (intraclass correlation (2,k) = 0.987 [0.96, 0.99], intraclass correlation (2, k) = 0.987 [0.98, 0.99, respectively]). The standard error of measurement for the DL-derived HKAA for the operative and nonoperative legs was 0.515° and 0.403°, respectively.

CONCLUSIONS

A detection-based DL algorithm can calculate the HKAA in LLRs and is comparable to that calculated by manual measurement. The algorithm can detect the bilateral femoral head, knee, and ankle joints with high precision, even in patients where the femoral head is difficult to visualize.

Proximal humeral bone density assessment and prediction analysis using machine learning techniques: An innovative approach in medical research

Background

Patients with fractures of the proximal humerus often local complications and failures attributed to osteoporosis. Currently, there is a lack of straight forward screening methods for assessing the extent of local osteoporosis in the proximal humerus. This study utilizes machine learning techniques to establish a diagnostic approach for evaluating local osteoporosis by analyzing the patient's demographic data, bone density, and X-ray ratio of the proximal humerus.

Methods

A cohort comprising a total of 102 hospitalized patients admitted during the period spanning from 2021 to 2023 underwent random selection procedures. Resulting in exclusion of 5 patients while enrolling 97 patients for analysis encompassing patient demographics, shoulder joint anteroposterior radiographs, and bone density information. Using the modified Tingart index methodology involving multiple measurements denoted as M1 through M4 obtained from humeral shafts. Within this cohort comprised 76 females (78.4 %) and 21 males (21.6 %), with an average age of 73.0 years (range: 43-98 years). There were 25 cases with normal bone density, 35 with osteopenia, and 37 with osteoporosis. Machine learning techniques were used to randomly divide the 97 cases into training (n = 59) and validation (n = 38) sets with a ratio of 6:4 using stratified random sampling. A decision tree model was built in the training set, and significant diagnostic indicators were selected, with the performance of the decision tree evaluated using the validation set. Multinomial logistic regression methods were used to verify the strength of the relationship between the selected indicators and osteoporosis.

Results

The decision tree identified significant diagnostic indicators as the humeral shaft medullary cavity ratio M2/M4, age, and gender. M2/M4 ≥ 1.13 can be used as an important screening criterion; M2/M4 < 1.13 was predicted as local osteoporosis; M2/M4 ≥ 1.13 and age ≥83 years were also predicted as osteoporosis. M2/M4 ≥ 1.13 and age <64 years or males aged between 64 and 83 years were predicted as the normal population; M2/M4 ≥ 1.13 and females aged between 64 and 83 years were predicted as having osteopenia. The decision tree's accuracy in the training set was 0.7627 (95 % CI (0.6341, 0.8638)), and its accuracy in the test set was 0.7895 (95 % CI (0.6268, 0.9045)). Multinomial logistic regression results showed that humeral shaft medullary cavity ratios M2/M4, age, and gender in X-ray images were significantly associated with the occurrence of osteoporosis.

Conclusion

Utilizing X-ray data of the proximal humerus in conjunction with demographic information such as gender and age enable the prediction of localized osteoporosis, facilitating physicians' rapid comprehension of osteoporosis in patients and optimization of clinical treatment plans.

Level of evidence

Level IV retrospective case study.

Automatic measurement of anatomical parameters of the lumbar vertebral body and the intervertebral disc on radiographs by deep learning

Background

Lumbar spine disorders are one of the common causes of low back pain (LBP). Objective and reliable measurement of anatomical parameters of the lumbar spine is essential in the clinical diagnosis and evaluation of lumbar disorders. However, manual measurements are time-consuming and laborious, with poor consistency and repeatability. Here, we aim to develop and evaluate an automatic measurement model for measuring the anatomical parameters of the vertebral body and intervertebral disc based on lateral lumbar radiographs and deep learning (DL).

Methods

A model based on DL was developed with a dataset consisting of 1,318 lateral lumbar radiographs for the prediction of anatomical parameters, including vertebral body heights (VBH), intervertebral disc heights (IDH), and intervertebral disc angles (IDA). The mean of the values obtained by 3 radiologists was used as a reference standard. Statistical analysis was performed in terms of standard deviation (SD), mean absolute error (MAE), Percentage of correct keypoints (PCK), intraclass correlation coefficient (ICC), regression analysis, and Bland-Altman plot to evaluate the performance of the model compared with the reference standard.

Results

The percentage of intra-observer landmark distance within the 3 mm threshold was 96%. The percentage of inter-observer landmark distance within the 3 mm threshold was 94% (R1 and R2), 92% (R1 and R3), and 93% (R2 and R3), respectively. The PCK of the model within the 3 mm distance threshold was 94-99%. The model-predicted values were 30.22±3.01 mm, 10.40±3.91 mm, and 10.63°±4.74° for VBH, IDH, and IDA, respectively. There were good correlation and consistency in anatomical parameters of the lumbar vertebral body and disc between the model and the reference standard in most cases (R2=0.89-0.95, ICC =0.93-0.98, MAE =0.61-1.15, and SD =0.89-1.64).

Conclusions

The newly proposed model based on a DL algorithm can accurately measure various anatomical parameters on lateral lumbar radiographs. This could provide an accurate and efficient measurement tool for the quantitative evaluation of spinal disorders.

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study

BACKGROUND

The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT).

OBJECTIVE

To construct a novel predicting model based on bone turn-over markers and CT to identify patients who were more inclined to suffer spine refracture.

METHODS

CT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U-net model was adopted to automatically segment ROI. Three-dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121-3D model to model the cropped region and simultaneously build a T-NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models.

RESULTS

The performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer-Lemeshow goodness-of-fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p-value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly.

CONCLUSION

T_P1NT was an independent risk factor of refracture. Our 3D-FULL model showed better performance in predicting high-risk population of spine refracture than other models and junior doctors do. This model can be applicable to real-world translation due to its automatic segmentation and detection.

Association between fatty acids intake and bone mineral density in adolescents aged 12-19: NHANES 2011-2018

Background

The relationship between the intake of dietary fatty acids (FA) and bone mineral density (BMD) has been the subject of prior investigations. However, the outcomes of these studies remain contentious. The objective of this research is to examine the link between dietary FA consumption among adolescents and BMD.

Methods

This study utilized high-quality data from the National Health and Nutrition Examination Survey database, spanning 2011 to 2018, to explore the association between dietary fatty acids and bone health indicators in adolescents, including BMD and bone mineral content (BMC). Analyses were performed using weighted multivariate linear regression models, incorporating detailed subgroup analysis.

Results

The study included 3440 participants. Analysis demonstrated that intake of saturated fatty acids (SFA) was positively correlated with total BMD, left arm BMD, total BMC, and left arm BMC. Monounsaturated fatty acid (MUFA) intake was positively correlated with BMC across most body parts, though it showed no correlation with BMD. Intake of polyunsaturated fatty acids (PUFA) was significantly inversely correlated with both BMD and BMC in most body parts. Additionally, subgroup analysis indicated that variables such as sex, age, standing height, and race significantly influenced the correlation between FA intake and BMD.

Conclusions

Our study indicates that dietary intake of SFA may benefit to BMD in adolescents, in contrast to PUFA and MUFA. Therefore, we recommend that adolescents maintain a balanced intake of SFA to promote optimal bone mass development while preserving metabolic health.

Age-period-cohort analysis of epidemiological trends in pelvic fracture in China from 1992 to 2021 and forecasts for 2046

Background

This study explored the epidemiological trends in pelvic fracture (PF) in China from 1992 to 2021, analyze their relationships with age-period-cohort (APC) factors, and predict the trends of PF from 2022 to 2046.

Methods

Incidence and years lived with disabilities (YLDs) of PF among sexes in China from 1992 to 2021 were obtained through the 2021 Global Burden of Disease (GBD) database. Trends in the incidence and YLDs were described, and a joinpoint regression model was used. The APC model was used to explore the effects of age, period, and cohort on the incidence and YLDs. Nordpred forecasted the incidence and YLDs in China from 2022 to 2046.

Results

In 2021, there were an estimated 0.63 million incidence cases and 0.33 million of YLDs, respectively. The number and age-standardized rate (ASR) of incidence and YLDs were both gradually increased. The average annual percent change (AAPC) in incidence and YLDs for men were 0.26% and -0.17%, respectively. For women, the AAPC values for incidence and YLDs were -0.03% and -0.57% (p < 0. 001), respectively. The relative risk (RR) of PF increases with age, with the lowest risk in those aged 10-14 years for incidence and aged 1-4 for YLDs and the highest risk in those aged >95 years for incidence and aged 90-94 years for YLDs. The period effect showed a totally increase in the risk across the general, male, and female populations. Cohort effects indicated a totally significant decline for both incidence and YLDs. The predicted incidence and YLDs of PF in China from 2022 to 2046 showed an initial rise, followed by a decline, with 2029 and 2034 being the turning point for incidence and YLDs, respectively.

Conclusion

The characteristics of pelvic fracture incidence and YLDs in China are complex. Thus, primary prevention measures must be strengthened. Raising awareness about osteoporosis prevention, enhancing public health education, and promoting good dietary and hygiene habits are appropriate preventive measures for PF in China.

A radiograph-based deep learning model improves radiologists' performance for classification of histological types of primary bone tumors: A multicenter study

PURPOSE

To develop a deep learning (DL) model for classifying histological types of primary bone tumors (PBTs) using radiographs and evaluate its clinical utility in assisting radiologists.

METHODS

This retrospective study included 878 patients with pathologically confirmed PBTs from two centers (638, 77, 80, and 83 for the training, validation, internal test, and external test sets, respectively). We classified PBTs into five categories by histological types: chondrogenic tumors, osteogenic tumors, osteoclastic giant cell-rich tumors, other mesenchymal tumors of bone, or other histological types of PBTs. A DL model combining radiographs and clinical features based on the EfficientNet-B3 was developed for five-category classification. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate model performance. The clinical utility of the model was evaluated in an observer study with four radiologists.

RESULTS

The combined model achieved a macro average AUC of 0.904/0.873, with an accuracy of 67.5 %/68.7 %, a macro average sensitivity of 66.9 %/57.2 %, and a macro average specificity of 92.1 %/91.6 % on the internal/external test set, respectively. Model-assisted analysis improved accuracy, interpretation time, and confidence for junior (50.6 % vs. 72.3 %, 53.07[s] vs. 18.55[s] and 3.10 vs. 3.73 on a 5-point Likert scale [P < 0.05 for each], respectively) and senior radiologists (68.7 % vs. 75.3 %, 32.50[s] vs. 21.42[s] and 4.19 vs. 4.37 [P < 0.05 for each], respectively).

CONCLUSION

The combined DL model effectively classified histological types of PBTs and assisted radiologists in achieving better classification results than their independent visual assessment.

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks

Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bladder (KUB) radiographs are inexpensive and frequently ordered in clinical practice. Thus, it is a potential screening tool for osteoporosis. In this study, we explored the possibility of predicting the bone mineral density (BMD) and classifying high-risk patient groups using KUB radiographs. We proposed DeepDXA-KUB, a deep learning model that predicts the BMD values of the left hip and lumbar vertebrae from an input KUB image. The datasets were obtained from Taiwanese medical centers between 2006 and 2019, using 8913 pairs of KUB radiographs and DXA examinations performed within 6 months. The images were randomly divided into training and validation sets in a 4:1 ratio. To evaluate the model's performance, we computed a confusion matrix and evaluated the sensitivity, specificity, accuracy, precision, positive predictive value, negative predictive value, F1 score, and area under the receiver operating curve (AUROC). Moderate correlations were observed between the predicted and DXA-measured BMD values, with a correlation coefficient of 0.858 for the lumbar vertebrae and 0.87 for the left hip. The model demonstrated an osteoporosis detection accuracy, sensitivity, and specificity of 84.7 %, 81.6 %, and 86.6 % for the lumbar vertebrae and 84.2 %, 91.2 %, and 81 % for the left hip, respectively. The AUROC was 0.939 for the lumbar vertebrae and 0.947 for the left hip, indicating a satisfactory performance in osteoporosis screening. The present study is the first to develop a deep learning model based on KUB radiographs to predict lumbar spine and femoral BMD. Our model demonstrated a promising correlation between the predicted and DXA-measured BMD in both the lumbar vertebrae and hip, showing great potential for the opportunistic screening of osteoporosis.

Prediction of Low Bone Mass for Japanese Female Athletes Using Machine Learning

Attainment of optimal bone mineral density (BMD) in female athletes is a very important aspect of ensuring their well-being throughout their lives, and monitoring of BMD is essential to avoid fractures and bone-related injuries. Several tools for assessing bone health already exist, but either lack the practicality required for continuous monitoring or are not suited for the young female athlete demographic. Because of this, this study has the main objective of developing a binary classifier to discriminate between normal and low bone mass individuals among younger female athletes using features extracted from a questionnaire. The dataset consisted of data from 213 female athletes. Five different models were compared: logistic regression, decision tree, random forest, multi-layer perceptron and XGBoost. Validation was performed via cross-validation and feature importance was assessed via permutation importance. XGBoost showed the most balanced results in terms of sensitivity and specificity, achieving values of 0.93 and 0.62 respectively. It also obtained an AUC of 0.73 and an accuracy of 0.68. It was observed that the duration of the current period of amenorrhea, as well as the impact of the sport, showed the highest relevance, which is consistent with previous literature. Other features such as thinness level, number of training days in a week and age at menarche also showed high importance. The models demonstrated promising results in identifying low bone mass subjects from normal ones, indicating that features based on questionnaires can be an important source for evaluating low BMD in female athletes.

Towards the diagnosis of osteoporosis - contributions from coincidental diagnostic imaging findings in chiropractors' practice

BACKGROUND

Osteoporosis is significantly associated with fractures and burdens the health of especially older people. Osteoporotic fractures cause pain, disability, and increased mortality. Early diagnosis of osteoporosis allows earlier initiation of treatment, thereby reducing the risk of osteoporotic fractures. Chiropractors encounter potential osteoporotic patients daily, and perform radiological evaluation of these and other patients, including evaluation of X-rays done for other purposes than osteoporosis. Therefore, chiropractors may identify vertebral fractures, vertebral deformity or osteopenia not otherwise suspected or recorded.

METHODS

This study examines procedures available to the chiropractor to describe conventional X-rays with the focus of osteoporosis related findings. We review the indications for radiological examination in chiropractic practice, and in the realm of osteoporosis we describe radiological methods available for examination of conventional radiographs, and the necessity of inter-disciplinary communication.

RESULTS

National guidelines are available regarding referral for X-rays in chiropractic practice. Standardized protocols ensure image acquisition of the highest quality in the chiropractors' radiological department. Conventional X-ray examination is not indicated on clinical suspicion of osteoporosis alone, as bone mineral density testing is the diagnostic test. Radiological assessment of all available X-rays of patients above the age of 50 years should include evaluation of the bone quality, and hip and vertebral fracture assessment. The Singh index, Genant Semi-Quantitative tool (GSQ), and Algorithm-Based Qualitative method (ABQ) should be used consistently during interpretation. Referral for additional imaging and evaluation should be prompt and systematic when needed.

CONCLUSIONS

This article presents an overview of evidence-based radiological procedures for the purpose of promoting early diagnosis of osteoporosis. We present recommendations to the clinicians where we propose an opportunistic evaluation of X-rays, done for any reason, which include systematic evaluation of bone quality, presence of hip and vertebral fractures, and vertebral deformation of all patients above the age of 50 years. Detailed referral to healthcare professionals for further diagnostic evaluation is performed when needed. Consistent, high-quality radiological procedures in chiropractic practices could feasibly contribute to the timely diagnosis of osteoporosis, ultimately minimizing the impact of osteoporosis-related complications on patients' health.

Clinical Validation of a Deep Learning-Based Software for Lumbar Bone Mineral Density and T-Score Prediction from Chest X-ray Images

Screening for osteoporosis is crucial for early detection and prevention, yet it faces challenges due to the low accuracy of calcaneal quantitative ultrasound (QUS) and limited access to dual-energy X-ray absorptiometry (DXA) scans. Recent advances in AI offer a promising solution through opportunistic screening using existing medical images. This study aims to utilize deep learning techniques to develop a model that analyzes chest X-ray (CXR) images for osteoporosis screening. This study included the AI model development stage and the clinical validation stage. In the AI model development stage, the combined dataset of 5122 paired CXR images and DXA reports from the patients aged 20 to 98 years at a medical center was collected. The images were enhanced and filtered for hardware retention such as pedicle screws, bone cement, artificial intervertebral discs or severe deformity in target level of T12 and L1. The dataset was then separated into training, validating, and testing datasets for model training and performance validation. In the clinical validation stage, we collected 440 paired CXR images and DXA reports from both the TCVGH and Joy Clinic, including 304 pared data from TCVGH and 136 paired data from Joy Clinic. The pre-clinical test yielded an area under the curve (AUC) of 0.940, while the clinical validation showed an AUC of 0.946. Pearson's correlation coefficient was 0.88. The model demonstrated an overall accuracy, sensitivity, and specificity of 89.0%, 88.7%, and 89.4%, respectively. This study proposes an AI model for opportunistic osteoporosis screening through CXR, demonstrating good performance and suggesting its potential for broad adoption in preliminary screening among high-risk populations.

Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review

Osteoporosis is a complex endocrine disease characterized by a decline in bone mass and microstructural integrity. It constitutes a major global health problem. Recent progress in the field of artificial intelligence (AI) has opened new avenues for the effective diagnosis of osteoporosis via radiographs. This review investigates the application of AI classification of osteoporosis in radiographs. A comprehensive exploration of electronic repositories (ClinicalTrials.gov, Web of Science, PubMed, MEDLINE) was carried out in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement (PRISMA). A collection of 31 articles was extracted from these repositories and their significant outcomes were consolidated and outlined. This encompassed insights into anatomical regions, the specific machine learning methods employed, the effectiveness in predicting BMD, and categorizing osteoporosis. Through analyzing the respective studies, we evaluated the effectiveness and limitations of AI osteoporosis classification in radiographs. The pooled reported accuracy, sensitivity, and specificity of osteoporosis classification ranges from 66.1% to 97.9%, 67.4% to 100.0%, and 60.0% to 97.5% respectively. This review underscores the potential of AI osteoporosis classification and offers valuable insights for future research endeavors, which should focus on addressing the challenges in technical and clinical integration to facilitate practical implementation of this technology.

Automated osteoporosis classification and T-score prediction using hip radiographs via deep learning algorithm

Background

Despite being the gold standard for diagnosing osteoporosis, dual-energy X-ray absorptiometry (DXA) is an underutilized screening tool for osteoporosis.

Objectives

This study proposed and validated a controllable feature layer of a convolutional neural network (CNN) model with a preprocessing image algorithm to classify osteoporosis and predict T-score on the proximal hip region via simple hip radiographs.

Design

This was a single-center, retrospective study.

Methods

An image dataset of 3460 unilateral hip images from 1730 patients (age ⩾50 years) was retrospectively collected with matched DXA assessment for T-score for the targeted proximal hip regions to train (2473 unilateral hip images from 1430 patients) and test (497 unilateral hip images from 300 patients) the proposed CNN model. All images were processed with a fully automated CNN model, X1AI-Osteo.

Results

The proposed screening tool illustrated a better performance (sensitivity: 97.2%; specificity: 95.6%; positive predictive value: 95.7%; negative predictive value: 97.1%; area under the curve: 0.96) than the open-sourced CNN models in predicting osteoporosis. Moreover, when combining variables, including age, body mass index, and sex as features in the training metric, there was high consistency in the T-score on the targeted hip regions between the proposed CNN model and the DXA (r = 0.996, p < 0.001).

Conclusion

The proposed CNN model may identify osteoporosis and predict T-scores on the targeted hip regions from simple hip radiographs with high accuracy, highlighting the future application for population-based opportunistic osteoporosis screening with low cost and high adaptability for a broader population at risk.

Trial registration

TMU-JIRB N201909036.

Deep learning in the radiologic diagnosis of osteoporosis: a literature review

OBJECTIVE

Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis.

METHODS

We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis.

RESULTS

A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7).

CONCLUSIONS

Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.

Performance of Progressive Generations of GPT on an Exam Designed for Certifying Physicians as Certified Clinical Densitometrists

BACKGROUND

Artificial intelligence (AI) large language models (LLMs) such as ChatGPT have demonstrated the ability to pass standardized exams. These models are not trained for a specific task, but instead trained to predict sequences of text from large corpora of documents sourced from the internet. It has been shown that even models trained on this general task can pass exams in a variety of domain-specific fields, including the United States Medical Licensing Examination. We asked if large language models would perform as well on a much narrower subdomain tests designed for medical specialists. Furthermore, we wanted to better understand how progressive generations of GPT (generative pre-trained transformer) models may be evolving in the completeness and sophistication of their responses even while generational training remains general. In this study, we evaluated the performance of two versions of GPT (GPT 3 and 4) on their ability to pass the certification exam given to physicians to work as osteoporosis specialists and become a certified clinical densitometrists. The CCD exam has a possible score range of 150 to 400. To pass, you need a score of 300.

METHODS

A 100-question multiple-choice practice exam was obtained from a 3rd party exam preparation website that mimics the accredited certification tests given by the ISCD (International Society for Clinical Densitometry). The exam was administered to two versions of GPT, the free version (GPT Playground) and ChatGPT+, which are based on GPT-3 and GPT-4, respectively (OpenAI, San Francisco, CA). The systems were prompted with the exam questions verbatim. If the response was purely textual and did not specify which of the multiple-choice answers to select, the authors matched the text to the closest answer. Each exam was graded and an estimated ISCD score was provided from the exam website. In addition, each response was evaluated by a rheumatologist CCD and ranked for accuracy using a 5-level scale. The two GPT versions were compared in terms of response accuracy and length.

RESULTS

The average response length was 11.6 ±19 words for GPT-3 and 50.0±43.6 words for GPT-4. GPT-3 answered 62 questions correctly resulting in a failing ISCD score of 289. However, GPT-4 answered 82 questions correctly with a passing score of 342. GPT-3 scored highest on the "Overview of Low Bone Mass and Osteoporosis" category (72 % correct) while GPT-4 scored well above 80 % accuracy on all categories except "Imaging Technology in Bone Health" (65 % correct). Regarding subjective accuracy, GPT-3 answered 23 questions with nonsensical or totally wrong responses while GPT-4 had no responses in that category.

CONCLUSION

If this had been an actual certification exam, GPT-4 would now have a CCD suffix to its name even after being trained using general internet knowledge. Clearly, more goes into physician training than can be captured in this exam. However, GPT algorithms may prove to be valuable physician aids in the diagnoses and monitoring of osteoporosis and other diseases.

Opportunistic Screening of Low Bone Mineral Density From Standard X-Rays

BACKGROUND

Osteoporosis, characterized by loss of bone mineral density (BMD), is underscreened. Osteoporosis and low bone mass are diagnosed by a BMD T-score ≤ -2.5, and between -1.0 and -2.5, respectively, at the femoral neck or lumbar vertebrae (L1-4), using dual energy x-ray absorptiometry (DXA). The ability to estimate BMD at those anatomic sites from standard radiographs would enable opportunistic screening of low BMD (T-score < -1) in individuals undergoing x-ray for any clinical indication.

METHODS

Radiographs of the lumbar spine, thoracic spine, chest, pelvis, hand, and knee, with a paired DXA acquired within 1 year, were obtained from community imaging centers (62,023 x-ray-DXA pairs of patients). A software program called Rho was developed that uses x-ray, age, and sex as inputs, and outputs a score of 1 to 10 that corresponds with the likelihood of low BMD. The program's performance was assessed using receiver-operating characteristic analyses in three independent test sets, as follows: patients from community imaging centers (n = 3,729; 83% female); patients in the Canadian Multicentre Osteoporosis Study (n = 1,780; 71% female); and patients in the Osteoarthritis Initiative (n = 591; 50% female).

RESULTS

The areas under the receiver-operating characteristic curves were 0.89 (0.87-0.90), 0.87 (0.85-0.88), and 0.82 (0.79-0.85), respectively, and subset analyses showed similar results for each sex, body part, and race.

CONCLUSION

Rho can opportunistically screen patients at risk of low BMD (at femoral neck or L1-4) from radiographs of the lumbar spine, thoracic spine, chest, pelvis, hand, or knee.

External validation of a deep learning model for predicting bone mineral density on chest radiographs

We developed a new model for predicting bone mineral density on chest radiographs and externally validated it using images captured at facilities other than the development environment. The model performed well and showed potential for clinical use.

PURPOSE

In this study, we performed external validation (EV) of a developed deep learning model for predicting bone mineral density (BMD) of femoral neck on chest radiographs to verify the usefulness of this model in clinical practice.

METHODS

This study included patients who visited any of the collaborating facilities from 2010 to 2020 and underwent chest radiography and dual-energy X-ray absorptiometry (DXA) at the femoral neck in the year before and after their visit. A total of 50,114 chest radiographs were obtained, and BMD was measured using DXA. We developed the model with 47,150 images from 17 facilities and performed EV with 2914 images from three other facilities (EV dataset). We trained the deep learning model via ensemble learning based on chest radiographs, age, and sex to predict BMD using regression. The outcomes were the correlation of the predicted BMD and measured BMD with diagnoses of osteoporosis and osteopenia using the T-score estimated from the predicted BMD.

RESULTS

The mean BMD was 0.64±0.14 g/cm2 in the EV dataset. The BMD predicted by the model averaged 0.61±0.08 g/cm2, with a correlation coefficient of 0.68 (p<0.01) when compared with the BMD measured using DXA. The accuracy, sensitivity, and specificity of the model were 79.0%, 96.6%, and 34.1% for T-score < -1 and 79.7%, 77.1%, and 80.4% for T-score ≤ -2.5, respectively.

CONCLUSION

Our model, which was externally validated using data obtained at facilities other than the development environment, predicted BMD of femoral neck on chest radiographs. The model performed well and showed potential for clinical use.

Development and Validation of a Deep-Learning Model to Predict Total Hip Replacement on Radiographs: The Total Hip Replacement Prediction (THREP) Model

BACKGROUND

There are few methods for accurately assessing the risk of total hip arthroplasty (THA) in patients with osteoarthritis. A novel and reliable method that could play a substantial role in research and clinical routine should be investigated. The purpose of the present study was to develop a deep-learning model that can reliably predict the risk of THA with use of radiographic images and clinical symptom data.

METHODS

This retrospective, multicenter, case-control study assessed hip joints on weighted-bearing anteroposterior pelvic radiographs obtained from Osteoarthritis Initiative (OAI) participants. Participants who underwent THA were matched to controls according to age, sex, body mass index, and ethnicity. Cases and controls were uniformly split into training, validation, and testing data sets at proportions of 72% (n = 528), 14% (n = 104), and 14% (n = 104), respectively. Images and clinical symptom data were passed through a detection model and a deep convolutional neural network (DCNN) model to predict the probability of THA within 9 years as well as the most likely time period for THA (0 to 2 years, 3 to 5 years, 6 to 9 years). Model performance was assessed with use of the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity in the testing set.

RESULTS

A total of 736 participants were evaluated, including 184 cases and 552 controls. The prediction model achieved an overall accuracy, sensitivity, and specificity of 91.35%, 92.59% and 86.96%, respectively, with an AUC of 0.944, for THA within 9 years. The AUC of the DCNN model for assessing the most likely time period was 0.907 for 0 to 2 years, 0.916 for 3 to 5 years, and 0.841 for 6 to 9 years. Gradient-weighted class activation mapping closely corresponded to regions affecting the prediction of the DCNN model.

CONCLUSIONS

The proposed DCNN model is a reliable and valid method to predict the probability of THA-within limitations. It could assist clinicians in patient counseling and decision-making regarding the timing of the intervention. In the future, by increasing the size of the data set, enhancing the ethnic and socioeconomic diversity of the participants, and improving the follow-up rate, the quality of the conclusions can be further improved.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Assessing the utility of osteoporosis self-assessment tool for Asians in patients undergoing hip surgery

Objectives

Diagnosis and treatment of osteoporosis are instrumental in obtaining good outcomes of hip surgery. Measuring bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis. However, due to limited access to DXA, there is a need for a screening tool to identify patients at a higher risk of osteoporosis. We analyzed the potential utility of the Osteoporosis Self-assessment Tool for Asians (OSTA) as a screening tool for osteoporosis.

Methods

A total of 1378 female patients who underwent hip surgery at 8 institutions were analyzed. For each patient, the BMD of the proximal femoral region was measured by DXA (DXA-BMD), and the correlation with OSTA score (as a continuous variable) was assessed. Receiver operating characteristic (ROC) curve analysis was performed to assess the ability of OSTA score to predict osteoporosis. Lastly, the OSTA score was truncated to yield an integer (OSTA index) to clarify the percentage of patients with osteoporosis for each index.

Results

DXA-BMD showed a strong correlation with OSTA (r = 0.683; P < 0.001). On ROC curve analysis, the optimal OSTA score cut-off value of -5.4 was associated with 73.8% sensitivity and 80.9% specificity for diagnosis of osteoporosis (area under the curve: 0.842). A decrease in the OSTA index by 1 unit was associated with a 7.3% increase in the probability of osteoporosis.

Conclusions

OSTA is a potentially useful tool for screening osteoporosis in patients undergoing hip surgery. Our findings may help identify high-risk patients who require further investigation using DXA.

Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm

The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our model successfully predicted postoperative re-tear before the surgery using 62 preoperative variables with an accuracy of 96.93%, and achieved an accuracy of 79.55% on an independent external dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness. This research method and strategy can be applied to other medical fields, and the research findings can assist in making healthcare decisions.

Diagnostic Accuracy of Deep Learning for the Prediction of Osteoporosis Using Plain X-rays: A Systematic Review and Meta-Analysis

(1) Background: This meta-analysis assessed the diagnostic accuracy of deep learning model-based osteoporosis prediction using plain X-ray images. (2) Methods: We searched PubMed, Web of Science, SCOPUS, and Google Scholar from no set beginning date to 28 February 2023, for eligible studies that applied deep learning methods for diagnosing osteoporosis using X-ray images. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 criteria. The area under the receiver operating characteristic curve (AUROC) was used to quantify the predictive performance. Subgroup, meta-regression, and sensitivity analyses were performed to identify the potential sources of study heterogeneity. (3) Results: Six studies were included; the pooled AUROC, sensitivity, and specificity were 0.88 (95% confidence interval [CI] 0.85-0.91), 0.81 (95% CI 0.78-0.84), and 0.87 (95% CI 0.81-0.92), respectively, indicating good performance. Moderate heterogeneity was observed. Mega-regression and subgroup analyses were not performed due to the limited number of studies included. (4) Conclusion: Deep learning methods effectively extract bone density information from plain radiographs, highlighting their potential for opportunistic screening. Nevertheless, additional prospective multicenter studies involving diverse patient populations are required to confirm the applicability of this novel technique.

Association of dietary fat intake with skeletal muscle mass and muscle strength in adults aged 20-59: NHANES 2011-2014

Background

Sarcopenia, a progressive loss of skeletal muscle mass and strength, needs to initially prevent in the twenties. Meanwhile, there is a lack of research on the effects of fat consumption on skeletal muscle mass and strength in adults aged 20-59. We aimed to assess associations between dietary fat intake and skeletal muscle mass, as measured by appendicular lean mass adjusted for body mass index (ALMBMI), and muscle strength, as represented by handgrip strength adjusted for body mass index (GSMAXBMI), among adults aged 20-59.

Methods

Dietary fat intake per kilogram of actual body weight was assessed using two 24h recalls, while ALM and GSMAX were measured using DXA and a handgrip dynamometer, respectively. A weighted multiple linear regression model was employed to analyze the association between dietary fat intake and skeletal muscle mass, utilizing data from the National Health and Nutrition Examination Survey spanning from 2011 to 2014. To assess the non-linear relationship and saturation value between dietary fat intake and skeletal muscle mass, a smooth curve fitting approach and a saturation effect analysis model were utilized.

Results

The study comprised a total of 5356 subjects. After adjusting for confounding factors, there was a positive association observed between dietary fat intake and ALMBMI as well as GSMAXBMI. The relationship between dietary fat intake and ALMBMI showed an inverted U-shaped curve, as did the association with GSMAXBMI. Turning points were observed at 1.88 g/kg/d for total fat intake and ALMBMI, as well as at 1.64 g/kg/d for total fat intake and GSMAXBMI. Furthermore, turning points were still evident when stratifying by gender, age, protein intake, and physical activity. The turning points were lower in individuals with low protein intake(<0.8 g/kg/d) and high levels of physical activity.

Conclusion

The moderate dietary fat intake can be beneficial for muscle mass and strength in adults aged 20-59 under specific conditions. Special attention should be directed toward the consumption of fats in individuals with low protein intake and those engaged in high levels of physical activity.

Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk

A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83-3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61-1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.

Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models

PURPOSE

Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. Deep-learning and specific models achieve highly accurate results using different imaging modalities. The primary purpose of this research was to develop unimodal and multimodal deep-learning-based diagnostic models to predict bone mineral loss of the lumbar vertebrae using magnetic resonance (MR) and computed tomography (CT) imaging.

METHODS

Patients who received both lumbar dual-energy X-ray absorptiometry (DEXA) and MRI (n = 120) or CT (n = 100) examinations were included in this study. Unimodal and multimodal convolutional neural networks (CNNs) with dual blocks were proposed to predict osteoporosis using lumbar vertebrae MR and CT examinations in separate and combined datasets. Bone mineral density values obtained by DEXA were used as reference data. The proposed models were compared with a CNN model and six benchmark pre-trained deep-learning models.

RESULTS

The proposed unimodal model obtained 96.54%, 98.84%, and 96.76% balanced accuracy for MRI, CT, and combined datasets, respectively, while the multimodal model achieved 98.90% balanced accuracy in 5-fold cross-validation experiments. Furthermore, the models obtained 95.68%-97.91% accuracy with a hold-out validation dataset. In addition, comparative experiments demonstrated that the proposed models yielded superior results by providing more effective feature extraction in dual blocks to predict osteoporosis.

CONCLUSION

This study demonstrated that osteoporosis was accurately predicted by the proposed models using both MR and CT images, and a multimodal approach improved the prediction of osteoporosis. With further research involving prospective studies with a larger number of patients, there may be an opportunity to implement these technologies into clinical practice.

Establish and validate the reliability of predictive models in bone mineral density by deep learning as examination tool for women

While FRAX with BMD could be more precise in estimating the fracture risk, DL-based models were validated to slightly reduce the number of under- and over-treated patients when no BMD measurements were available. The validated models could be used to screen for patients at a high risk of fracture and osteoporosis.

PURPOSE

Fracture risk assessment tool (FRAX) is useful in classifying the fracture risk level, and precise prediction can be achieved by estimating both clinical risk factors and bone mineral density (BMD) using dual X-ray absorptiometry (DXA). However, DXA is not frequently feasible because of its cost and accessibility. This study aimed to establish the reliability of deep learning (DL)-based alternative tools for screening patients at a high risk of fracture and osteoporosis.

METHODS

Participants were enrolled from the National Bone Health Screening Project of Taiwan in this cross-sectional study. First, DL-based models were built to predict the lowest T-score value in either the lumbar spine, total hip, or femoral neck and their respective BMD values. The Bland-Altman analysis was used to compare the agreement between the models and DXA. Second, the predictive model to classify patients with a high fracture risk was built according to the estimated BMD from the first step and the FRAX score without BMD. The performance of the model was compared with the classification based on FRAX with BMD.

RESULTS

Approximately 10,827 women (mean age, 65.4 ± 9.4 years) were enrolled. In the prediction of the lumbar spine BMD, total hip BMD, femoral neck BMD, and lowest T-score, the root-mean-square error (RMSE) was 0.099, 0.089, 0.076, and 0.68, respectively. The Bland-Altman analysis revealed a nonsignificant difference between the predictive models and DXA. The FRAX score with femoral neck BMD for major osteoporotic fracture risk was 9.7% ± 6.7%, whereas the risk for hip fracture was 3.3% ± 4.6%. Comparison between the classification of FRAX with and without BMD revealed the accuracy rate, positive predictive value (PPV), and negative predictive value (NPV) of 78.8%, 64.6%, and 89.9%, respectively. The area under the receiver operating characteristic curve (AUROC), accuracy rate, PPV, and NPV of the classification model were 0.913 (95% confidence interval: 0.904-0.922), 83.5%, 71.2%, and 92.2%, respectively.

CONCLUSION

While FRAX with BMD could be more precise in estimating the fracture risk, DL-based models were validated to slightly reduce the number of under- and over-treated patients when no BMD measurements were available. The validated models could be used to screen for patients at a high risk of fracture and osteoporosis.

Application of Artificial Intelligence at All Stages of Bone Tissue Engineering

The development of artificial intelligence (AI) has revolutionized medical care in recent years and plays a vital role in a number of areas, such as diagnostics and forecasting. In this review, we discuss the most promising areas of AI application to the field of bone tissue engineering and prosthetics, which can drastically benefit from AI-assisted optimization and patient personalization of implants and scaffolds in ways ranging from visualization and real-time monitoring to the implantation cases prediction, thereby leveraging the compromise between specific architecture decisions, material choice, and synthesis procedure. With the emphasized crucial role of accuracy and robustness of developed AI algorithms, especially in bone tissue engineering, it was shown that rigorous validation and testing, demanding large datasets and extensive clinical trials, are essential, and we discuss how through developing multidisciplinary cooperation among biology, chemistry with materials science, and AI, these challenges can be addressed.

Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images

Utilizing "You only look once" (YOLO) v4 AI offers valuable support in fracture detection and diagnostic decision-making. The purpose of this study was to help doctors to detect and diagnose fractures more accurately and intuitively, with fewer errors. The data accepted into the backbone are diversified through CSPDarkNet-53. Feature maps are extracted using Spatial Pyramid Pooling and a Path Aggregation Network in the neck part. The head part aggregates and generates the final output. All bounding boxes by the YOLO v4 are mapped onto the 3D reconstructed bone images after being resized to match the same region as shown in the 2D CT images. The YOLO v4-based AI model was evaluated through precision-recall (PR) curves and the intersection over union (IoU). Our proposed system facilitated an intuitive display of the fractured area through a distinctive red mask overlaid on the 3D reconstructed bone images. The high average precision values (>0.60) were reported as 0.71 and 0.81 from the PR curves of the tibia and elbow, respectively. The IoU values were calculated as 0.6327 (tibia) and 0.6638 (elbow). When utilized by orthopedic surgeons in real clinical scenarios, this AI-powered 3D diagnosis support system could enable a quick and accurate trauma diagnosis.

Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

OBJECTIVES

Early identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis.

METHODS

Relevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites.

RESULTS

Fifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively.

CONCLUSIONS

ML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models.

PROSPERO REGISTRATION NUMBER

CRD42022346896.

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. To frequently monitor bone health, low-cost, low-dose, and ubiquitously available diagnostic methods are highly anticipated. In this study, we aim to perform bone mineral density (BMD) estimation from a plain X-ray image for opportunistic screening, which is potentially useful for early diagnosis. Existing methods have used multi-stage approaches consisting of extraction of the region of interest and simple regression to estimate BMD, which require a large amount of training data. Therefore, we propose an efficient method that learns decomposition into projections of bone-segmented QCT for BMD estimation under limited datasets. The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3.27 to 3.79% for four measurements with different poses. Furthermore, we conducted extensive validation experiments, including multi-pose, uncalibrated-CT, and compression experiments toward actual application in routine clinical practice.

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

Targeting Kindlin-2 in adipocytes increases bone mass through inhibiting FAS/PPARγ/FABP4 signaling in mice

Osteoporosis (OP) is a systemic skeletal disease that primarily affects the elderly population, which greatly increases the risk of fractures. Here we report that Kindlin-2 expression in adipose tissue increases during aging and high-fat diet fed and is accompanied by decreased bone mass. Kindlin-2 specific deletion (K2KO) controlled by Adipoq-Cre mice or adipose tissue-targeting AAV (AAV-Rec2-CasRx-sgK2) significantly increases bone mass. Mechanistically, Kindlin-2 promotes peroxisome proliferator-activated receptor gamma (PPARγ) activation and downstream fatty acid binding protein 4 (FABP4) expression through stabilizing fatty acid synthase (FAS), and increased FABP4 inhibits insulin expression and decreases bone mass. Kindlin-2 inhibition results in accelerated FAS degradation, decreased PPARγ activation and FABP4 expression, and therefore increased insulin expression and bone mass. Interestingly, we find that FABP4 is increased while insulin is decreased in serum of OP patients. Increased FABP4 expression through PPARγ activation by rosiglitazone reverses the high bone mass phenotype of K2KO mice. Inhibition of FAS by C75 phenocopies the high bone mass phenotype of K2KO mice. Collectively, our study establishes a novel Kindlin-2/FAS/PPARγ/FABP4/insulin axis in adipose tissue modulating bone mass and strongly indicates that FAS and Kindlin-2 are new potential targets and C75 or AAV-Rec2-CasRx-sgK2 treatment are potential strategies for OP treatment.