Evaluation of a new biochemical index for the estimation of bone demineralization using artificial intelligence.

Abstract

The use of artificial neural networks (ANN) for the identification of a positive correlation between the QuiOs quotient, a single-valued indicator of bone mineral density, and multisite dual energy x-ray absorptiometry (DEXA) measurements is described. The measurements were obtained using the Hologic QDRR-2000 x-ray bone densitometer in a multicenter clinical trial including 374 female patients. The QuiOs quotient estimates bone mineral density and determines the severity of bone density loss. This quotient is calculated by using a proprietary software program to perform a multivariate analysis of the results of testing of serum levels of calcium, phosphate, total alkaline phosphate, two alkaline phosphatase isoenzymes (liver and intestine), estradiol, and progesterone. The results show that given the four DEXA measurements obtained from the bone densitometer, the trained neural network can predict whether the corresponding QuiOs score of the same patient will be below the cutoff score of 0.690. The findings in this study indicate a positive correlation between DEXA measurements and the QuiOs quotient obtained from two different sources.