Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures

Abstract

Background: Osteoporosis is a socio-economic problem of modern aging societies. Bone fractures and the related treatments generate the highest costs. The occurrence of osteoporotic fractures is a cause of chronic disability, many complications, reduced quality of life, and often premature death. Aim of the study: The aim of the study was to determine which of the patient’s potential risk factors pertaining to various diseases and lifestyle have an essential impact on the occurrence of low-energy fractures and the hierarchy of these factors. Methods: The study was retrospective. The documentation of 222 patients (206 women and 16 men) from an osteoporosis treatment clinic in Łódź, Poland was analyzed. Each patient was described by a vector consisting of 27 features, where each feature was a different risk factor. Using artificial neural networks, an attempt was made to create a model that, based on the available data, would be able to predict whether the patient would be exposed to low-energy fractures. We developed a neural network model that achieved the best result for the testing data. In addition, we used other methods to solve the classification problem, i.e., correctly dividing patients into two groups: those with fractures and those without fractures. These methods were logistic regression, k-nearest neighbors and SVM. Results: The obtained results gave us the opportunity to assess the effectiveness of various methods and the importance of the features describing patients. Using logistic regression and the recursive elimination of features, a ranking of risk factors was obtained in which the most important were age, chronic kidney disease, neck T-score, and serum phosphate level. Then, we repeated the learning procedure of the neural network considering only these four most important features. The average mean squared error on the test set was about 27% for the best variant of the model. Conclusions: The comparison of the rankings with different numbers of patients shows that the applied method is very sensitive to changes in the considered data (adding new patients significantly changes the result). Further cohort studies with more patients and more advanced methods of machine learning may be needed to identify other significant risk factors and to develop a reliable fracture risk system. The obtained results may contribute to the improved identification patients at risk of low-energy fractures and early implementation of comprehensive treatment.

Determining the hierarchy of risk factors for low-energy fractures in patients of an Osteoporosis Treatment Clinic

“…Figures 1-4 and Tables 1-2 describe the basic characteristics of patients of the Osteoporosis Treatment Clinic whose data were taken into account. More information is given in Table 1 of [7], where the same group of patients is considered. Tables 1 and 2 compare the data on actual fractures, with the probabilities computed by the FRAX calculator (in 2 versions: using BMD and using BMI).…”

“…Another study, based on the same dataset as the present study, was described in [7]. Its aim was to examine the possibility of applying some basic methods of AI to predict whether a patient would have an osteoporotic fracture.…”

“…In a recently published paper [7], some methods of Artificial Intelligence (AI) were applied to the same data to find a method for predicting osteoporotic fractures and to determine the most important risk factors. The comparison of this research and [7] will be presented in the Discussion section below.…”

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

“…With the recent advent of artificial intelligence (AI) technology, machine learning and deep learning models have applications in osteoporosis detection and classification. These applications encompass osteoporosis risk prediction [83][84][85] and fracture risk assessment [86][87][88][89]. Notably, AI has been employed to streamline the complex processing of CT images and enhance automated segmentation [90][91][92], which has been utilized for BMD measurement and classification in multiple studies [93][94][95][96][97][98][99][100][101][102][103].…”