Genetic algorithm and image processing for osteoporosis diagnosis.

Osteoporosis is considered as a major public health threat. It is characterized by a decrease in the density of bone, decreasing its strength and leading to an increased risk of fracture. In this work, the morphological, topological and mechanical characteristics of 2 populations of arthritic and osteoporotic trabecular bone samples are evaluated using artificial intelligence and recently developed skeletonization algorithms. Results show that genetic algorithms associated with image processing tools can precisely separate the 2 populations.

Clinical characteristics of patients with diabetic polyneuropathy: the role of clinical and electromyographic evaluation and the effect of the various types on the quality of life.

OBJECTIVE: This study was performed to identify the relationship between the quality of life and polyneuropathy which is one of the complications of diabetes. METHODS: Total 111 patients with diabetes mellitus were taken into the study as type 1 and type 2. Patients were accepted having polyneuropathy according to their electroneuromyography (ENMG) results. To evaluate the quality of life in the patients Short Form 36 (SF-36) and World Health Organization Quality of Life Questionnaire abbreviated version (WHOQOL-BREF) were used. RESULTS: Clinical polyneuropathy was found in 46% of the patients, while polineuropathy was found in 63% of the patients with evaluation ENMG. The patients with polyneuropathy had poor quality of life according to SF-36 and WHOQOL-BREF (p < 0.001). The mean quality of life scores of patients who had sensoriomotor and mix polyneuropathy, were lower than sensory type and axonal polyneuropathy. CONCLUSION: Diabetic polyneuropathy influences the quality of life in a negative way. The quality of life scores of patients who had polyneuropathy continuing with mixed pathogenesis and sensoriomotor type, become worse for this reason, even if the patients do not have any clinical polyneuropathy, this being evaluated with ENMG.