Application of Machine Learning to Identify Clinically Meaningful Risk Group for Osteoporosis in Individuals Under the Recommended Age for Dual-Energy X-Ray Absorptiometry

Prediction of osteoporosis in patients with rheumatoid arthritis using machine learning

Osteoporosis is a serious health concern in patients with rheumatoid arthritis (RA). Machine learning (ML) models have been increasingly incorporated into various clinical practices, including disease classification, risk prediction, and treatment response. However, only a few studies have focused on predicting osteoporosis using ML in patients with RA. We aimed to develop an ML model to predict osteoporosis using a representative Korean RA cohort database. The KORean Observational study Network for Arthritis (KORONA) database, established by the Clinical Research Center for RA in Korea, was used in this study. Among the 5077 patients registered in KORONA, 2374 patients were included in this study. Four representative ML algorithms were used for the prediction: logistic regression (LR), random forest, XGBoost (XGB), and LightGBM. The accuracy, F1 score, and area under the curve (AUC) of each model were measured. The LR model achieved the highest AUC value at 0.750, while the XGB model achieved the highest accuracy at 0.682. Body mass index, age, menopause, waist and hip circumferences, RA surgery, and monthly income were risk factors of osteoporosis. In conclusion, ML algorithms are a useful option for screening for osteoporosis in patients with RA.

Application of machine learning algorithms to predict osteoporosis in postmenopausal women with type 2 diabetes mellitus

PURPOSE

The screening and diagnosis of osteoporosis in patients with type 2 diabetes mellitus (T2DM) based on bone mineral density remains challenging because of the limited availability and accessibility of dual-energy X-ray absorptiometry. We aimed to develop and validate models to predict the risk of osteoporosis in postmenopausal women with T2DM based on machine learning (ML) algorithms.

METHODS

This retrospective study included 303 postmenopausal women with T2DM. To develop prediction models for osteoporosis, we applied nine ML algorithms combined with demographic, clinical, and laboratory data. The least absolute shrinkage and selection operator were used to perform feature selection. We used the bootstrap resampling technique for model training and validation. To test the performance of the models, we calculated indices including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve, and decision curve analysis. Furthermore, we conducted fivefold cross-validation for parameter optimization and model validation. Feature importance was assessed using the SHapley additive explanation (SHAP).

RESULTS

We identified 10 independent predictors as the most valuable features. An AUROC of 0.616-1.000 was observed for nine ML algorithms. The extreme gradient boosting (XGBoost) model exhibited the best performance, outperforming conventional risk assessment tools and registering 0.993 in the training set, 0.798 in the validation set, and 0.786 in the test set for fivefold cross-validation. Using SHAP, we found that the explanatory variables contributed to the model and their relationship with osteoporosis occurrence. Furthermore, we developed a user-friendly tool for calculating the risk of osteoporosis.

CONCLUSIONS

With the integration of demographic and clinical risk factors, ML algorithms can accurately predict osteoporosis. The XGBoost model showed ideal performance. With the incorporation of these models in the clinic, patients may benefit from early osteoporosis diagnosis and treatment.

A novel primary osteoporosis screening tool (POST) for adults aged 50 years and over

PURPOSE

This study aimed to develop and validate a simple primary osteoporosis screening tool (POST) based on adults aged 50 years and older.

METHODS

This study included participants aged ≥50 from the National Health and Nutrition Examination Survey. Osteoporosis was defined according to bone mineral density values. The POST was developed based on methods from previous studies. Moreover, we plotted the receiver operating characteristic curves to calculate the area under the curve (AUC) and determine the optimal cut-off value according to the weighted Youden index. In addition, we compared the performances in identifying individuals with osteoporosis between the POST and the Osteoporosis Self-assessment Tool (OST). Finally, we also assessed the performance of the POST in the Chinese population.

RESULTS

Finally, a total of 6665 individuals were included in this study. The AUC values of the POST for identifying individuals with osteoporosis in the development cohort and the validation cohort were 0.81 (95% CI: 0.79-0.83) and 0.81 (95% CI: 0.77-0.84), respectively. Moreover, a POST-score ≥7 was determined as the threshold to identify individuals with osteoporosis, in which the sensitivity was greater than 90%. In addition, the POST showed significantly higher sensitivity than the OST. Finally, the POST showed an AUC of 0.75 (95% CI: 0.65-0.85) among 94 Chinese subjects aged ≥50 years old.

CONCLUSIONS

POST is a convenient and effective tool for osteoporosis screening among adults aged 50 years and over, which might provide new methodological support for future osteoporosis screening.

Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus-A Preliminary Study

Purpose

Diagnosing osteoporosis in T2DM based on bone mineral density (BMD) remains challenging. We sought to develop prediction models employing machine learning algorithms for use as screening instruments for osteoporosis in T2DM patients.

Patients and Methods

Data were collected from 433 participants and analyzed using nine categorical machine learning algorithms to select features based on demographic and clinical variables. Multiple classification models were compared using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, specificity, the average precision (AP), precision, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA) to determine the best model. In addition, 5-fold cross-validation was utilized to optimize the model, followed by an evaluation of feature significance using Shapley Additive exPlanations (SHAP). Using latent class analysis (LCA), distinct subpopulations were identified by constructing several discrete clusters.

Results

In this study, nine feature variables were identified to construct predictive models for osteoporosis in individuals with T2DM. The machine learning algorithms achieved an AP range of 0.444-1.000. The XGBoost model was selected as the final prediction model with an AUROC of 0.940 in the training set, 0.772 in the validation set for 5-fold cross-validation, and 0.872 in the test set. Using SHAP methodology, 25(OH)D was identified as the most important risk factor. Additionally, a 3-Class model was constructed using LCA, which categorized individuals into high, medium, and low-risk groups.

Conclusion

Our study developed a predictive model with high accuracy and clinical validity for predicting osteoporosis in type 2 diabetes patients. We also identified three subpopulations with varying osteoporosis risk using clustering. However, limited sample size warrants cautious interpretation of results, and validation in larger cohorts is needed.

Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation

BACKGROUND

Osteoporosis is one of the diseases that requires early screening and detection for its management. Common clinical tools and machine-learning (ML) models for screening osteoporosis have been developed, but they show limitations such as low accuracy. Moreover, these methods are confined to limited risk factors and lack individualized explanation.

OBJECTIVE

The aim of this study was to develop an interpretable deep-learning (DL) model for osteoporosis risk screening with clinical features. Clinical interpretation with individual explanations of feature contributions is provided using an explainable artificial intelligence (XAI) technique.

METHODS

We used two separate data sets: the National Health and Nutrition Examination Survey data sets from the United States (NHANES) and South Korea (KNHANES) with 8274 and 8680 respondents, respectively. The study population was classified according to the T-score of bone mineral density at the femoral neck or total femur. A DL model for osteoporosis diagnosis was trained on the data sets and significant risk factors were investigated with local interpretable model-agnostic explanations (LIME). The performance of the DL model was compared with that of ML models and conventional clinical tools. Additionally, contribution ranking of risk factors and individualized explanation of feature contribution were examined.

RESULTS

Our DL model showed area under the curve (AUC) values of 0.851 (95% CI 0.844-0.858) and 0.922 (95% CI 0.916-0.928) for the femoral neck and total femur bone mineral density, respectively, using the NHANES data set. The corresponding AUC values for the KNHANES data set were 0.827 (95% CI 0.821-0.833) and 0.912 (95% CI 0.898-0.927), respectively. Through the LIME method, significant features were induced, and each feature's integrated contribution and interpretation for individual risk were determined.

CONCLUSIONS

The developed DL model significantly outperforms conventional ML models and clinical tools. Our XAI model produces high-ranked features along with the integrated contributions of each feature, which facilitates the interpretation of individual risk. In summary, our interpretable model for osteoporosis risk screening outperformed state-of-the-art methods.