Revista británica de investigación Acceso abierto

Abstracto

Artificial Neural Networks (ANNs) as a Reliable Tool for the Assessment of Fracture Risk in Postmenopausal Women

Gloria Bonaccorsi, Carlo Cervellati, Enzo Grossi, Enrica Fila, Leo Massari, Nicola Veronese, Francesco Pio Cafarelli, Giuseppe Guglielmi

Artificial neural networks (ANNs) are a computational tool, based on highly non-linear mathematics models with potential applications in the prediction of osteoporotic fractures. Therefore, the present study aimed to evaluate the potential of ANNs analysis in the prediction of bone fragility fractures in post-menopausal women. ANNs prognostic performance in identifying vertebral morphometric deformity was compared with that of the widely used tool FRAX^® in a sample of 587 Caucasian postmenopausal women underwent densitometry and morphometric analyses for the detection of vertebral fractures. The analysis of areas under the curve (AUCs) showed that sensitivity for ANNs (74%) almost doubled that found for FRAX^® (38%), with the latter presenting a specificity higher than the proposed tool (96 vs. 77%). Overall, ANN-based analysis was able to highlight high-risk patients with a global higher accuracy (74%) compared to that obtained by FRAX (67%). In conclusion, our data showed that compared to WHO’s algorithm ANNs had higher sensitivity in identifying vertebral deformity, thus suggesting a “promising role” in the prediction of osteoporotic fracture in postmenopausal women. However, further studies on larger sample are needed to definitely establish the clinical reliability of ANNs.