Artificial neural networks (ANN's) recognize patterns relating input and output data in a manner analogous to the function of biological neurons. Here, we show that ANN's can predict rib deformity in scoliosis more accurately than regression analysis. ANN's and linear regression models were developed to predict rib rotation from several combinations of input spinal indices including Cobb angle, vertebral rotation, apex location and orientation of the plane of maximal curvature. ANN's averaged 60% correct predictions compared to 34% for regression analysis. This study provides evidence for the utility of artificial neural networks in scoliosis research. These data lend credence to the use of ANN's in future work on the prediction of scoliotic spinal deformity from torso surface data, which would permit assessment of scoliosis severity with minimal use of harmful X-rays.
A patient is described who has Morquio syndrome (MPS IVA). He is a member of the Hutterite Brethren and genealogic analysis discloses a high inbreeding coefficient for the proband. The proband's sibship is segregating two autosomal recessive disorders, ie, MPS IVA and infantile hypophosphatasia. Two other families each have one or the other of these diseases but not both. The three families are distantly related.
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