BACKGROUND:With the increasing degree of population aging in China,the incidence of osteoporosis is rising annually.This growing demand for screening and diagnosis poses significant challenges to the healthcare system,increasing the time costs,financial burdens,and radiation exposure risks for patients.OBJECTIVE:To develop a novel interpretable prediction method based on traditional CT examination data and demographic data,aiming to reduce the number of patient examinations and enable multiple screenings from one examination.METHODS:A two-stage interpretable framework for osteoporosis prediction was designed.In the first stage,a human-computer collaborative method was used for annotating CT images,with an innovative vertebra 7-point CT value measurement technique.Patient's sex and age were used as key demographic features to enrich the model's input.In the second stage,the LightGBM model was enhanced by SHapley Additive exPlanations for quantitative analysis of feature importance,improving the interpretability of predictions and increasing clinical trust.Systematic experiments validated the effectiveness of the framework and the stability of the optimal feature set through the comparative analysis of different feature combinations with six machine learning models.To further assess the generalization ability of the model,the model was further tested on an external dataset.RESULTS AND CONCLUSION:The experiment compared six machine learning models suitable for medical applications,and the results showed that LightGBM model achieved an F1 score of 0.902 2 and an area under the curve of 0.938 7,outperforming the other models.In terms of interpretability,the clinical application credibility and operability of the model was increased by ranking and visualizing the contribution of input features to the results.Additionally,this study realized a prototype system,and testing results indicated that the system is user-friendly,capable of quickly processing data to provide prediction results,with visualized outcomes demonstrating good interpretability.This system effectively assists doctors in clinical decision-making and provides robust support for the screening and preliminary diagnosis of osteoporosis.

BACKGROUND:With the increasing degree of population aging in China,the incidence of osteoporosis is rising annually.This growing demand for screening and diagnosis poses significant challenges to the healthcare system,increasing the time costs,financial burdens,and radiation exposure risks for patients.OBJECTIVE:To develop a novel interpretable prediction method based on traditional CT examination data and demographic data,aiming to reduce the number of patient examinations and enable multiple screenings from one examination.METHODS:A two-stage interpretable framework for osteoporosis prediction was designed.In the first stage,a human-computer collaborative method was used for annotating CT images,with an innovative vertebra 7-point CT value measurement technique.Patient's sex and age were used as key demographic features to enrich the model's input.In the second stage,the LightGBM model was enhanced by SHapley Additive exPlanations for quantitative analysis of feature importance,improving the interpretability of predictions and increasing clinical trust.Systematic experiments validated the effectiveness of the framework and the stability of the optimal feature set through the comparative analysis of different feature combinations with six machine learning models.To further assess the generalization ability of the model,the model was further tested on an external dataset.RESULTS AND CONCLUSION:The experiment compared six machine learning models suitable for medical applications,and the results showed that LightGBM model achieved an F1 score of 0.902 2 and an area under the curve of 0.938 7,outperforming the other models.In terms of interpretability,the clinical application credibility and operability of the model was increased by ranking and visualizing the contribution of input features to the results.Additionally,this study realized a prototype system,and testing results indicated that the system is user-friendly,capable of quickly processing data to provide prediction results,with visualized outcomes demonstrating good interpretability.This system effectively assists doctors in clinical decision-making and provides robust support for the screening and preliminary diagnosis of osteoporosis.