Lumbar Disc Degeneration Is Linked to Dorsal Subcutaneous Fat Thickness at the L1-L2 Intervertebral Disc Level Measured by MRI.

BACKGROUND: Obese individuals have a higher risk of degenerative disc disease (DDD). Currently, body mass index is not sensitive enough to differentiate between muscle and fat distribution, and obesity-related health issues are linked to the way body fat is distributed. Therefore, this study aims to investigate the association between the dorsal subcutaneous fat thickness (DSFT) of the lumbar spine, an alternative measurement tool of body fat distribution, and DDD. METHODS: A total of 301 patients with DDD and 123 participants without the disease were recruited. Using length functions of magnetic resonance imaging (MRI) console, the DSFT of L1 to S1 intervertebral disc levels was measured in mid-sagittal spin-echo T2 weighted image. The Mann-Whitney U test and Chi-squared test (X2) were utilized to examine any variations between the case and control groups. Logistic regression models were built to explore the association of the DSFT with DDD. RESULTS: The logistical regression model showed a positive association between DDD and DSFT [OR: 1.30, 95% CI: 1.02-1.64, p = 0.03]. In the stratified logistic regression analysis, a positive association was found between DDD and DSFT among younger participants and females [OR young: 1.48; 95% CI (1.02-2.20); p = 0.04-OR female: 1.37; 95% CI (1-1.88); p = 0.05]. CONCLUSIONS: Younger females with thicker DSFT at the L1-L2 level are more likely to develop DDD. This suggests that increased DSFT may be a contributing factor to DDD.

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN.

In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients' lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image's background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%.