Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database

Abstract

BackgroundIt is known that bone mineral density (BMD) predicts the fracture's risk only partially and the severity and number of vertebral fractures are predictive of subsequent osteoporotic fractures (OF). Spinal deformity index (SDI) integrates the severity and number of morphometric vertebral fractures. Nowadays, there is interest in developing algorithms that use traditional statistics for predicting OF. Some studies suggest their poor sensitivity. Artificial Neural Networks (ANNs) could represent an alternative. So far, no study investigated ANNs ability in predicting OF and SDI. The aim of the present study is to compare ANNs and Logistic Regression (LR) in recognising, on the basis of osteoporotic risk-factors and other clinical information, patients with SDI≥1 and SDI≥5 from those with SDI = 0.MethodologyWe compared ANNs prognostic performance with that of LR in identifying SDI≥1/SDI≥5 in 372 women with postmenopausal-osteoporosis (SDI≥1, n = 176; SDI = 0, n = 196; SDI≥5, n = 51), using 45 variables (44 clinical parameters plus BMD). ANNs were allowed to choose relevant input data automatically (TWIST-system-Semeion). Among 45 variables, 17 and 25 were selected by TWIST-system-Semeion, in SDI≥1 vs SDI = 0 (first) and SDI≥5 vs SDI = 0 (second) analysis. In the first analysis sensitivity of LR and ANNs was 35.8% and 72.5%, specificity 76.5% and 78.5% and accuracy 56.2% and 75.5%, respectively. In the second analysis, sensitivity of LR and ANNs was 37.3% and 74.8%, specificity 90.3% and 87.8%, and accuracy 63.8% and 81.3%, respectively.ConclusionsANNs showed a better performance in identifying both SDI≥1 and SDI≥5, with a higher sensitivity, suggesting its promising role in the development of algorithm for predicting OF.

Calcium supplementation in osteoporosis: useful or harmful?

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Ensemble artificial neural networks applied to predict the key risk factors of hip bone fracture for elders

“…According to the advantages of nonlinearity, fault tolerance, universality, and real-time operation, ANNs have been proposed as a quite suitable algorithm for modeling complex non-linear relationships in health care research (Baxt et al, 1995;Cross et al, 1995;Kung and Hwang, 1998). Eller-Vainicher et al (2011) identified the promising role of ANN in predicting osteoporotic fracture among postmenopause osteoporosis women. For the comparison of the characteristics between ANNs and logistic regression applied to this epidemiological research field, a study has established prediction models for predicting living setting after hip fracture by ANNs and logistic regression, and shown that ANN is slightly better than logistic regression (Ottenbacher et al, 2004).…”

Classification of Osteoporosis by Extracting the Microarchitectural Properties of Trabecular Bone from DXA Scans Based on Thresholding Technique

“…Most of the existing approaches have used an SVM classifier with an RBF-kernel [33][34][35] or multi-layer perceptron (MLP)-based ANN classifier models [36][37][38][39] to distinguish osteoporotic patients from normal individuals. Therefore, the classification performance of the proposed approach was compared to that of an SVM classifier with various kernels.…”