Fractal analysis of the effects on mandibular bone of botulinum toxin therapy of the masseter muscle in patients with bruxism

OBJECTIVE

We examined changes in the mandibular angle, ramus, and condylar neck of patients with bruxism after botulinum toxin-A (BTX-A) injection into the masseter muscle as calculated with fractal analysis (FA) on panoramic radiographs (PRs).

METHODS

We examined the PRs of 3 groups of 22 patients each (n = 66) obtained upon presentation and 6 months later. One group included healthy controls without bruxism, one group included patients with untreated bruxism, and one group included patients with bruxism who had undergone BTX-A injection into the masseter muscle. We performed FA of the bilateral angle, ramus, and condylar neck of the PRs to calculate fractal dimension (FD).

RESULTS

The FD values of the angle on the second PRs of the untreated bruxism group were significantly higher than those of the other groups (P = .026), specifically when compared to the BTX-A injection group (P = .017). The FD values in the angle and ramus of the bruxism group were significantly higher on the second PRs (P ≤..005)) Conversely, the FD values in the angle of the BTX-A injection group were significantly lower on the second PR (P = .039).

CONCLUSIONS

Masseter muscle hyperactivity due to bruxism increases bone density in masseter muscle attachment regions. BTX-A injection restricts muscle activity, thereby chnging bone structure and decreasing FD.

Multifractal approach for a biological porous media: Human dentin case

A multifractal characterization to human dentin porosity is made. Micrographs of human dentin samples gotten from scanning electron microscopy (SEM) were studied in order to characterize porosity. We got the generalized dimensions (multifractal moments)D q for pore space and matrix skeleton on gray scale and binary images for samples of both gender. For the case, we found that superficial porosityη s is linked to mass fractal dimensionD 0 in an approximately linear relationship asD 0 ≈ η s + d ¯ for binary images. In addition, probability density distribution (PDD) for pore diameters is found a normal PDD type. Other quantities of interest like mean, standard deviation, maximum and minimum pore diameters, voit ratios, and percentage of chemical composition are reported. RESEARCH HIGHLIGHTS: SEM sample, images and procedure for multifractal analysis are detailed. Micro patterns in grayscale are multifractal and in binary scale are monofractals. Superficial porosity is relate to mass fractal dimension approximately linearly. From the porosity values, voit ratios are determined. Pores diameters obey a normal PDD.

Automatic detection of pneumonia in chest X-ray images using textural features

Fast and accurate diagnosis is critical for the triage and management of pneumonia, particularly in the current scenario of a COVID-19 pandemic, where this pathology is a major symptom of the infection. With the objective of providing tools for that purpose, this study assesses the potential of three textural image characterisation methods: radiomics, fractal dimension and the recently developed superpixel-based histon, as biomarkers to be used for training Artificial Intelligence (AI) models in order to detect pneumonia in chest X-ray images. Models generated from three different AI algorithms have been studied: K-Nearest Neighbors, Support Vector Machine and Random Forest. Two open-access image datasets were used in this study. In the first one, a dataset composed of paediatric chest X-ray, the best performing generated models achieved an 83.3% accuracy with 89% sensitivity for radiomics, 89.9% accuracy with 93.6% sensitivity for fractal dimension and 91.3% accuracy with 90.5% sensitivity for superpixels based histon. Second, a dataset derived from an image repository developed primarily as a tool for studying COVID-19 was used. For this dataset, the best performing generated models resulted in a 95.3% accuracy with 99.2% sensitivity for radiomics, 99% accuracy with 100% sensitivity for fractal dimension and 99% accuracy with 98.6% sensitivity for superpixel-based histons. The results confirm the validity of the tested methods as reliable and easy-to-implement automatic diagnostic tools for pneumonia.

Fractal analysis of the computed tomography images of vertebrae on the thoraco-lumbar region in diagnosing osteoporotic bone damage

Fractal analysis was used in the study to determine a set of feature descriptors which could be applied in the process of diagnosing bone damage caused by osteoporosis. The subject of the research involved the computed tomography images of vertebrae on the thoraco-lumbar region. The data set contained the images of healthy patients and patients diagnosed with osteoporosis. On the basis of fractal analysis and feature selection by linear stepwise regression, three descriptors were obtained. They were two fractal dimensions calculated with the variation method (transect - first differences and filter 1 estimators) and one fractal lacunarity calculated by means of the box counting method. The first two descriptors were obtained as a result of the analysis of grey images, and the third was the result of analysis of binary images. The effectiveness of the descriptors was verified using six popular supervised classification methods: linear and quadratic discriminant analysis, naive Bayes classifier, decision tree, K-nearest neighbours and random forests. The best results were obtained using the K-nearest neighbours classifier; they were as follows: overall classification accuracy - 81%, classification sensitivity - 78%, classification specificity - 90%, positive predictive value - 90%, and negative predictive value - 77%. The results of the research showed that fractal analysis can be a useful tool to extract feature vector of spinal computed tomography images in the diagnosis of osteoporotic bone defects.