Characterisation of Selected Materials in Medical Applications.

Tissue engineering is an interdisciplinary field of science that has developed very intensively in recent years. The first part of this review describes materials with medical and dental applications from the following groups: metals, polymers, ceramics, and composites. Both positive and negative sides of their application are presented from the point of view of medical application and mechanical properties. A variety of techniques for the manufacture of biomedical components are presented in this review. The main focus of this work is on additive manufacturing and 3D printing, as these modern techniques have been evaluated to be the best methods for the manufacture of medical and dental devices. The second part presents devices for skull bone reconstruction. The materials from which they are made and the possibilities offered by 3D printing in this field are also described. The last part concerns dental transitional implants (scaffolds) for guided bone regeneration, focusing on polylactide-hydroxyapatite nanocomposite due to its unique properties. This section summarises the current knowledge of scaffolds, focusing on the material, mechanical and biological requirements, the effects of these devices on the human body, and their great potential for applications.

Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet.

A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time-consuming tasks. This research-based paper proposes an automatic software-based alternative method to count blood cells accurately using the RetinaNet deep learning network, which is used to recognize and classify objects in microscopic images. After training, the network automatically recognizes and counts red blood cells, white blood cells, and platelets. We tested a model trained on smear images and found that the trained model has generalized capabilities. We assessed the quality of detection and cell counting using performance measures, such as accuracy, sensitivity, precision, and F1-score. Moreover, we studied the dependence of the confidence thresholds and the number of learning epochs on the obtained results of recognition and counting. We compared the performance of the proposed approach with those obtained by other authors who dealt with the subject of cell counting and show that object detection and labeling can be an additional advantage in the task of counting objects.