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PLoS By Category | Recent PLoS Articles
Computer Science - Mathematics - Rheumatology - Women's Health

Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database
Published: Friday, November 04, 2011
Author: Cristina Eller-Vainicher et al.

by Cristina Eller-Vainicher, Iacopo Chiodini, Ivana Santi, Marco Massarotti, Luca Pietrogrande, Elisa Cairoli, Paolo Beck-Peccoz, Matteo Longhi, Valter Galmarini, Giorgio Gandolini, Maurizio Bevilacqua, Enzo Grossi

Background

It 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.

Methodology

We 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.

Conclusions

ANNs 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.

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