Spinal Muscular Atrophy Type B Awareness and Symptoms Prediction Using 3D Segmentation Process in MRI Images

Abstract

Spinal muscular atrophy (SMA) describes a group of disorders associated with spinal motor neuron loss. In this review we provide an update regarding the most common form of SMA, proximal or 5q SMA, and discuss the contemporary approach to diagnosis and treatment. Electromyography and muscle biopsy features of denervation were once the basis for diagnosis, but molecular testing for homozygous deletion or mutation of the SMN1 gene allows efficient and specific diagnosis. In combination with loss of SMN1, patients retain variable numbers of copies of a second similar gene, SMN2, which produce reduced levels of the survival motor neuron (SMN) protein that are insufficient for normal motor neuron function. Despite the fact that the understanding of how ubiquitous reduction of SMN protein leads to motor neuron loss remains incomplete, several promising therapeutics are now being tested in early phase clinical trials. This proposed model investigates the symptoms and scans readings from the initial MRI scan images of babies with mutation progress and SMN proteins formation benchmark values for this particular disorder SMA and further this segmented parameters are acquitted into the K-means clustering technique that predict the report with the disorder symptoms with MSE (mean square error) values that helps the babies in future to take prevention measures to overcome this problem.