CORRELATION ANALYSIS OF THE GLOBAL ACTIVITY LIMITATION INDICATOR WITH DISABILITY MEASURES IN ADULTS UNDER 65 SUFFERING FROM SPINAL AND HEPATIC HEALTH ISSUES

Abstract

Computer image processing technology has become essential for analyzing 2D and 3D spinal images in medical imaging. Deep learning algorithms have played a vital role in improving the accuracy of diagnosing spinal illnesses. This transition has led to a significant increase in the use of deep neural networks for medical imaging and the development of three-dimensional human spine models. These advancements hold great potential for enhancing medical teaching. Correctly segmenting vertebrae remains challenging owing to their similar shapes and appearances. The research utilizes essential factors to evaluate the health of the spine and liver, using measures such as pain, range of motion, and levels of liver enzymes. Additionally, the Global Activity Limitation Indicator thoroughly assesses functional impairment. Datasets such as the LSUN Spinal Cord MRI Segmentation Dataset assist in gathering data, whereas disability measures use a quantitative methodology via the Inventory of Identification of Needs. The results add to a complete framework for comprehending the intricacies of spinal and hepatic health, focusing on providing customized services based on recognized requirements. This paves the way for improved medical diagnosis and patient treatment.