Osteoporosis Classification Research Articles

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 17th, 2020, to February 1st, 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 MI-CLAIM checklist. The systematic search yielded 97 articles that fell into five 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 6th area, AI-driven clinical decision support, identified the studies from the five preceding areas which 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.

Femur bone volumetric estimation for osteoporosis classification based on deep learning with tuna jellyfish optimization using X-ray images

Osteoporosis is a disorder, that leads to fractures and fatal problems in bones. It is believed that more than 200 million individuals are affected globally. Furthermore, osteoporosis is caused by micro-architectural degeneration of bone tissues, which increases the risk of bone fragility and fractures. Moreover, the osteoporosis categorization is essential for the medical industry, which classifies the skeleton problems of individuals caused by ageing. This work presented the prediction of femur bone volume for osteoporosis classification. Moreover, the femur bone X-ray image is utilized for the classification. The preprocessing phase is employed to neglect the noise contained in input bone images through a non-local means filter. In the image segmentation process, the SegNet is utilized to isolate the specific portion. Moreover, the template search approach based on femoral geometric estimation is carried out and the feature extraction phase is essential for a significant feature extraction process. The proposed tuna jellyfish optimization based deep batch-normalized eLU AlexNet (DbneAlexNet) is utilized in the osteoporosis classification process. Furthermore, accuracy, Positive Predictive Value (PPV), Negative Predictive Value (NPV), True Positive Rate (TPR) and True Negative Rate (TNR) are the metrics to validate the model and the superior values 0.913, 0.906, 0.896, 0.923 and 0.932 are achieved.

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.

Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics.

Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis in patients is known as osteoporosis classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from bone images and discover intricate patterns that could indicate osteoporosis. DCNN biases must be initialized carefully, much like their weights. Biases that are initialized incorrectly might affect the network's learning dynamics and hinder the model's ability to converge to an ideal solution. In this research, Deep Convolutional Neural Networks (DCNNs) are used, which have several benefits over conventional ML techniques for image processing. One of the key benefits of DCNNs is the ability to automatically Feature Extraction (FE) from raw data. Feature learning is a time-consuming procedure in conventional ML algorithms. During the training phase of DCNNs, the network learns to recognize relevant characteristics straight from the data. The Squirrel Search Algorithm (SSA) makes use of a combination of Local Search (LS) and Random Search (RS) techniques that are inspired by the foraging habits of squirrels. The method made it possible to efficiently explore the search space to find prospective values while using promising areas to refine and improve the solutions. Effectively recognizing optimum or nearly optimal solutions depends on balancing exploration and exploitation. The weight in the DCNN is optimized with the help of SSA, which enhances the performance of the classification. The comparative analysis with state-of-the-art techniques shows that the proposed SSA-based DCNN is highly accurate, with 96.57% accuracy.

Optimizing Osteoporosis Classification through Femur Bone Volumetric Estimation with Deep Belief Network in X-ray Imaging

The accurate classification of osteoporosis is a crucial requirement in the medical field for identifying patients with skeletal disorders associated with aging. Achieving improved accuracy and reduced computational complexity in classification algorithms is essential. To address this, our research proposes a novel classification method utilizing the Hybrid Gradient Particle Swarm (HSG) Optimization-based Deep Belief Network, integrating the Particle Swarm Optimization (PSO) algorithm into the Gradient Descent (GD) algorithm. The osteoporosis classification process comprises five key steps: Preprocessing, Active Shape Model-based Segmentation, Geometric Estimation employing the proposed template search method, Feature Extraction for extracting medical and image-level features, and Osteoporosis Classification using the HSG-based Deep Belief Network. The proposed template search method efficiently and automatically updates the geometric points of the femur segment. Experimental validation using a real-time database demonstrates the effectiveness of the proposed method in terms of accuracy, sensitivity, and specificity. The results indicate an accuracy of 0.9724, affirming the efficacy of the proposed algorithm in making precise decisions regarding osteoporosis classification.

Optimization Driven Deep Convolutional Neural Network and Mean Shift Based Segmentation for Osteoporosis Classification in X-ray Images

Optimization Driven Deep Convolutional Neural Network and Mean Shift Based Segmentation for Osteoporosis Classification in X-ray Images

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

Despite being the gold standard for diagnosing osteoporosis, dual-energy X-ray absorptiometry (DXA) is an underutilized screening tool for osteoporosis. 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. This was a single-center, retrospective study. 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. 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). 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. TMU-JIRB N201909036.

KONet: Toward a Weighted Ensemble Learning Model for Knee Osteoporosis Classification

KONet: Toward a Weighted Ensemble Learning Model for Knee Osteoporosis Classification

Quantitative CT Evaluation of Bone Mineral Density in the Thoracic Spine on 18F-Fluorocholine PET/CT Imaging in Patients With Primary Hyperparathyroidism

Introduction: Measurement of bone mineral density (BMD) with quantitative CT (QCT) carries several advantages over other densitometric techniques, including superior assessment of the spine. As most QCT studies evaluated the lumbar spine, measurements of the thoracic spine are limited. We performed QCT analysis of the thoracic spine in a cohort of patients with primary hyperparathyroidism. Materials and methods: This study was a retrospective QCT analysis of the thoracic spine on 18F-fluorocholine PET/CT scans in patients with primary hyperparathyroidism patients between March 2018 and December 2022. Correlations between QCT-derived BMD or Hounsfield units (HU) and demographic data, laboratory parameters, results from histopathological examination after parathyroidectomy and results of DXA imaging were analyzed, when available. Results: In 189 patients, mean QCT-derived BMD at the thoracic spine was 85.6 mg/cm3. Results from recent DXA were available in 122 patients. Mean thoracic QCT-derived BMD and HU were significantly correlated with DXA-derived BMD in lumbar spine, total hip and femoral neck and with the lowest T-score at DXA imaging. Only weak correlations were found with BMI or 18F-fluorocholine uptake, while no significant correlations were found with adenoma weight, PTH or calcium levels. Conclusion: Our study confirms correlation between QCT-derived BMD in the thoracic spine with age and DXA-derived BMD measurements within a population of patients with primary hyperparathyroidism. Establishment of reference BMD values for individual thoracic vertebrae, may allow direct osteoporosis classification on thoracic CT imaging.

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.

A study on whether deep learning models based on CT images for bone density classification and prediction can be used for opportunistic osteoporosis screening.

To explore the feasibility of using deep learning to establish a model for osteoporosis classification and bone density value prediction based on opportunistic CT scans and to verify its generalization and diagnostic ability using an independent test set. A total of 1219 cases of opportunistic CT scans were included in this study, with QCT results as the reference standard. The training set: test set: independent test set ratio was 703: 176: 340, and the independent test set data of 340 cases were from 3 different hospitals and 4 different CT scanners. The VB-Net structure automatic segmentation model was used to segment the trabecular bone, and DenseNet was used to establish a three-classification model and bone density value prediction regression model. The performance parameters of the models were calculated and evaluated. The ROC curves showed that the mean AUCs of the three-category classification model for categorizing cases into "normal," "osteopenia," and "osteoporosis" for the training set, test set, and independent test set were 0.999, 0.970, and 0.933, respectively. The F1 score, accuracy, precision, recall, precision, and specificity of the test set were 0.903, 0.909, 0.899, 0.908, and 0.956, respectively, and those of the independent test set were 0.798, 0.815, 0.792, 0.81, and 0.899, respectively. The MAEs of the bone density prediction regression model in the training set, test set, and independent test set were 3.15, 6.303, and 10.257, respectively, and the RMSEs were 4.127, 8.561, and 13.507, respectively. The R-squared values were 0.991, 0.962, and 0.878, respectively. The Pearson correlation coefficients were 0.996, 0.981, and 0.94, respectively, and the p values were all < 0.001. The predicted values and bone density values were highly positively correlated, and there was a significant linear relationship. Using deep learning neural networks to process opportunistic CT scan images of the body can accurately predict bone density values and perform bone density three-classification diagnosis, which can reduce the radiation risk, economic consumption, and time consumption brought by specialized bone density measurement, expand the scope of osteoporosis screening, and have broad application prospects.

Comparison of volumetric and areal bone mineral density in CT and scout scans using spectral detector technology

BackgroundTo determine whether denoised areal bone mineral density (BMD) measurements from scout scans in spectral detector computed tomography (CT) correlate with volumetric trabecular BMD for opportunistic osteoporosis screening.MethodsA 64-slice single-source dual-layer spectral CT scanner was used to acquire scout scan data of 228 lumbar vertebral bodies within 57 patients. Scout scans in anterior–posterior (AP) view were performed with a dose of < 0.06 mSv and spectrally decomposed into areal BMD (aBMD) values. A spectral dictionary denoising algorithm was applied to increase the signal-to-noise ratio (SNR). Volumetric trabecular bone mineral density (vBMD) was determined via material decomposition. A 3D convolutional network for image segmentation and labeling was applied for automated vBMD quantification. Projected maps were used to compare the classification accuracy of AP and lateral scout scans.ResultsThe denoising algorithm led to the minimization of anticorrelated noise in spectral maps and an SNR increase from 5.23 to 13.4 (p < 0.002). Correlation analysis between vBMD and measured AP aBMD, projected AP, and lateral aBMD showed a Pearson correlation coefficient of 0.68, 0.81, and 0.90, respectively. The sensitivity and specificity for the osteoporosis classification task were higher in lateral projection images than in AP crystallizing in an increased area under the curve value of 0.99 versus 0.90.ConclusionDenoised material-specific aBMD maps show a positive correlation to vBMD, enabling spectral scout scans as an opportunistic predictor for osteoporotic patients. This could be applied routinely as a screening tool in patients undergoing a CT examination.Relevance statementScout-based DEXA could be applied routinely as a screening tool in patients undergoing a CT examination.Key points• Spectral scout scans can be used as a dual-energy x-ray absorptiometry-like screening tool.• Spectral dictionary denoising on projection images increases the signal-to-noise ratio.• Positive correlation between volumetric and areal bone mineral density is observed.• Lateral projections increase osteoporosis classification accuracy compared to anterior-posterior projections.Graphical

End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images.

Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energyX-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.

Multi-View Computed Tomography Network for Osteoporosis Classification

Osteoporosis is a skeletal disease that is difficult to identify in advance of symptoms. Existing skeletal disease screening methods, such as dual-energy X-ray absorptiometry, are only used for specific purpose due to cost and safety reasons once symptoms develop. Early detection of osteopenia and osteoporosis using other modalities for relatively frequent examinations is helpful in terms of early treatment and cost. Recently, many studies have proposed deep learning-based osteoporosis diagnosis methods for various modalities and achieved outstanding results. However, these studies have limitations in clinical use because they require tedious processes, such as manually cropping a region of interest or diagnosing osteoporosis rather than osteopenia. In this study, we present a classification task for diagnosing osteopenia and osteoporosis using computed tomography (CT). Additionally, we propose a multi-view CT network (MVCTNet) that automatically classifies osteopenia and osteoporosis using two images from the original CT image. Unlike previous methods that use a single CT image as input, the MVCTNet captures various features from the images generated by our multi-view settings. The MVCTNet comprises two feature extractors and three task layers. Two feature extractors use the images as separate inputs and learn different features through dissimilarity loss. The target layers learn the target task through the features of the two feature extractors and then aggregate them. For the experiments, we use a dataset containing 2,883 patients’ CT images labeled as normal, osteopenia, and osteoporosis. Additionally, we observe that the proposed method improves the performance of all experiments based on the quantitative and qualitative evaluations.

Multifractal analysis for improved osteoporosis classification

Osteoporosis is characterized by decrease of bone mineral density and alterations of the bone microarchitecture, leading to bone fragility and an increased risk of fractures. Discrimination between osteoporotic and healthy subjects using X-ray images presents a major challenge for medical image analysis and classification. This work introduces a new approach based on multifractal texture analysis for the characterization of the trabecular bone microarchitecture to distinguish between Control Subjects (CS) and Osteoporotic Patients (OP). The proposed method enables a description of both local and global regularity and roughness of the trabecular bone on radiographic images. To this end, the multifractal spectrum is exploited to extract new texture attributes and reveal bone microarchitecture changes due to osteoporosis. To assess the proposed approach effectiveness, tests were carried out for the classification of two populations (CS and OP) using a logistic regression model. A classification rate of 98.01% was reached showing that the proposed approach presents a promising potential as a complementary tool for osteoporosis diagnosis.

Computer-Aided System of the Mandibular Cortical Bone Porosity Assessment on Digital Panoramic Radiographs.

The loss of bone mineral density (BMD) in various sites of the body, including the mandible, is the main sign of osteoporosis. Thus, the computer-aided diagnosis (CAD) system was developed for bone density assessment and patients were classified into normal, osteopenia, and osteoporosis groups using a digital panoramic radiograph. Data of dental panoramic radiographs and corresponding BMD assessments from 123 postmenopausal women were collected. For the proposed CAD system test, regions of interest (ROI) that were located below the left and right mental foramen on dental panoramic radiographs were determined. The width and texture of the mandibular cortical bone in each ROI were used to classify the data into normal, osteopenia, and osteoporosis classes. The width of the mandibular cortical was measured using the polynomial fitting method. The texture feature of the cortical bone is obtained by calculating the average value of the grayscale intensity of cortical bone. The classification result was obtained by using a multiclass support vector machine. The experimental results using 10-fold cross-validation showed that the proposed system achieved an average accuracy of 86.50% for osteoporosis classification on dental panoramic radiographs. The average misclassification error and relative foreground area error of the segmentation process were 5.21 and 12.98%, respectively. From the analysis of the cortical width measurement process, highest average mandibular cortical width (MCW) was found in the normal patient category compared with the other classes. This research showed that the proposed computer-aided system can be used for osteoporosis and osteopenia assessment by measuring the MCW and texture on dental panoramic radiographs with the average system accuracy of 89.52%.

Transfer Learning Model Training Time Comparison for Osteoporosis Classification on Knee Radiograph of RGB and Grayscale Images

In terms of financial costs and human suffering, osteoporosis poses a serious public health burden. Reduced bone mass, degeneration of the microarchitecture of bone tissue, and an increased risk of fracture are its main skeletal symptoms. Osteoporosis is caused not just by low bone mineral density, but also by other factors such as age, weight, height, and lifestyle. Recent advancement in Artificial Intelligence (AI) has led to successful applications of expert systems that use Deep Learning techniques for osteoporosis diagnosis based on some modalities such as dental radiographs amongst others. This study uses a dataset of knee radiographs (i.e., knee-Xray images) to apply and compare the training time of two robust transfer learning model algorithms: GoogLeNet, VGG-16, and ResNet50 to classify osteoporosis. The dataset was split into two subcategories using python opencv library: Grayscale Images and Red Green Blue (RGB) images. From the scikit learn python analysis, the training time of the GoogLeNet model on grayscale images and RGB images was 42minutes and 50 minutes respectively. The VGG-16 model training time on grayscale images and RGB images was 37 minutes and 44 minutes respectively. In addition, to compare the diagnostic performance of the two models, several state-of-the-art neural networks metric was used.

Evaluation of Parameter Fine-Tuning with Transfer Learning for Osteoporosis Classification in Knee Radiograph

Osteoporosis is a bone disease that raises the risk of fracture due to the density of the bone mineral being low and the decline of the structure of bone tissue. Among other techniques, such as Dual-Energy X-ray Absorptiometry (DXA), 2D x-ray pictures of the bone can be used to detect osteoporosis. This study aims to evaluate deep convolutional neural networks (CNNs), applied with transfer learning techniques, to categorize specific osteoporosis features in knee radiographs. For objective labeling, we obtained a selection of patient knee x-ray images. The study makes use of the Visual Geometry Group Deep (VGG-16), and VGG-16 with fine-tuning. In this work, the deployed CNNs were assessed using state-of-the-art metrics such as accuracy, sensitivity, and specificity. The evaluation shows that fine-tuning enhanced the VGG-16 CNN's effectiveness for detecting osteoporosis in radiographs of the knee. The accuracy of the VGG-16 with parameter fine-tuning was 88% overall, while the accuracy of the VGG-16 without parameter fine-tuning was 80%.

Deep Transfer Learning and Majority Voting Approaches for Osteoporosis Classification

Deep Transfer Learning and Majority Voting Approaches for Osteoporosis Classification

Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis

Background and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the image-only models. This study aimed to statistically evaluate the osteoporosis identification ability of deep learning by combining hip radiographs with patient variables. Materials andMethods: We collected a dataset containing 1699 images from patients who underwent skeletal-bone-mineral density measurements and hip radiography at a general hospital from 2014 to 2021. Osteoporosis was assessed from hip radiographs using convolutional neural network (CNN) models (ResNet18, 34, 50, 101, and 152). We also investigated ensemble models with patient clinical variables added to each CNN. Accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC) were calculated as performance metrics. Furthermore, we statistically compared the accuracy of the image-only model with that of an ensemble model that included images plus patient factors, including effect size for each performance metric. Results: All metrics were improved in the ResNet34 ensemble model compared with the image-only model. The AUC score in the ensemble model was significantly improved compared with the image-only model (difference 0.004; 95% CI 0.002–0.0007; p = 0.0004, effect size: 0.871). Conclusions: This study revealed the additional effect of patient variables in identification of osteoporosis using deep CNNs with hip radiographs. Our results provided evidence that the patient variables had additive synergistic effects on the image in osteoporosis identification.