Artificial intelligence in forensic medicine and forensic dentistry.

This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.

3-D image analysis of abdominal aortic aneurysm.

In this paper we propose a technique for 3-D segmentation of abdominal aortic aneurysm (AAA) from computed tomography (CT) angiography images. Output data form the proposed method can be used for measurement of aortic shape and dimensions. Knowledge of aortic shape and size is very important for selection of appropriate stent graft device for treatment of AAA. The technique is based on a 3-D deformable model and utilizes the level-set algorithm for implementation of the method. The method performs 3-D segmentation of CT images and extracts a 3-D model of aortic wall. Once the 3-D model of aortic wall is available it is easy to perform all required measurements for appropriate stent graft selection. The method proposed in this paper uses the level-set algorithm instead of the classical active contour algorithm developed by Kass et al. The main advantage of the level set algorithm is that it enables easy segmentation surpassing most of the drawbacks of the classical approach. In the level-set approach for shape modeling, a 3-D surface is represented by a real 3-D function or equivalent 4-D surface. The 4-D surface is then evolved through an iterative process of solving the differential equation of surface motion. Surface motion is defined by velocity at each point. The velocity is a sum of constant velocity and curvature-dependent velocity. The stopping criterion is calculated based on image gradient. The algorithm has been implemented in MATLAB and C languages. Experiments have been performed using real patient CT angiography images and have shown good results.