Background: From the epidemiological point of view, certain factors involved in the appearance of varicose veins are preponderant such as multiple pregnancies, age, and also certain races. Physiologically, venous valve dysfunction can also be a factor. Here, radiologists intervene to determine venous insufficiency. Doppler ultrasound (US) and tomography are often the most used in this detection. Certain other factors contribute to their recidivism. Aims: Some factors that occur in the recurrence of varicose veins are extrinsic such as age, sex, or genetic factor. On the other hand, certain factors are linked to an inadequate surgical procedure that can be partly explained by a poor radiological or methodological reading. The aim of this study is to prevent recurring complications that may occur the analysis of the factors of these is necessary. Materials and Methods: In our study, 62 patients were operated in our general surgery department during the period from January 2016 to September 2017. The pre-operative clinical examination included, among others, the radiological examination using a Doppler US. Patients who have had a recurrence are classified from the identification of the possible causes. Since the causes are complex and vary from one person to another, this makes them very difficult to analyze by conventional methods. We proposed an intelligent system based on artificial neural networks. Results: Once the system is established, this will identify the most important factor in the recurrence of varicose veins. By randomly changing the parameters at the input one by one and we record the effect that each produces on the recurrence rate at the output. Conclusion: The proposed system with its very strong inters connectivity, and the support of all possible combinations with the weight of each factor makes it possible to extract the predominant cause. With its learning from the real values recorded, and the optimal function created between the two input-output spaces, it becomes very easy to identify the main cause that leads to recidivism.

Background: Clinical examination and endoscopic biopsy are often not sufficient in the analysis of the evolution of laryngeal cancers. The imaging extension of laryngeal tumors is based on the helical multiband scanner. Aims: Some deep extensions of the initial tumor pathology are only accessible using imaging. This is decisive in the therapeutic choice. It is the same in the case of ganglionic extensions in the management of lymphocytic tumors. The precise identification of these is necessary in the therapeutic orientation. Materials and Methods: In our study, we give an overview of the imaging techniques used in the follow-up of 100 patients at the University Hospital of Setif in Algeria, and the impact of our imaging results on the prognosis and clinical management of these patients. Results: From the results obtained in imaging of these patients, there are findings relating to volumes, shapes, density, location, and extension. These variations are very complex to analyze using conventional mathematical tools. We propose a system that uses artificial intelligence techniques for their analysis. Conclusion: A fuzzy inference system is proposed. The input variables of the system express the tumor characteristics that are mapped to the output variable that expresses the therapeutic protocol to be adopted.

Aim: Often post-mortem radiography as a judicial procedure is intended to know the causes of death. X-rays are systematic on putrefied, charred or severely altered bodies when identifying a body. Nowadays other radiological techniques are used in post mortem recognition. In the case of collective disasters (war, air accident, or industrial ...etc.) the task is easy when comparing ante-mortem radiographs. In the absence of these, vestibular craniography and positional morpho-metric analysis is necessary. Specific characters of a skull are taken into account in this study. It refers us to his race as the first identification. Method:In this study, a database is based on the data that specifying each ethnic group (Gallo-Romans, Japanese, Ainu, Amerindians, Melanesians, African Blacks, Australians, Tasmanians ...). Each group is distinguished by specific characters (the shape of the structures and for their position in the axes, their structure and their reciprocal articulation). From measurements made on radiography skull and artificial neural network analysis, it will be possible to attribute this to the ethnic group to which it belongs. Conclusion:In this study, we consider these characters (distances, circumferences, curve, volumes, and angles) are considered as input variables of the network. These variables are related to an output variable that refers to the individual race. This can be a valuable tool for identification in forensic medicine.

Many factors involved in the detection of lung cancer. The confirmation depends on Age; Tobacco use; Historical malignancy; Growth rate; Edge characteristics; Size). The factors analyzed in this study are not precise beings and do not induce the same effects in the groups of the population. These variables are uncertain and therefore fuzzy variables. The principles of fuzzy inference prove adequate in this case. A fuzzy system is constructed with four input variables (Age, Tobacco use, Historical malignancy, density and morphological tumor) and one output represents the rate of exactitude in diagnosis. A base rules was established. It will be possible to read the expected degree of confirmation of malign tumor or benign tumor of lung cancer by introducing randomly the values in inputs variables. The analysis of factors at diagnosis, differ for the different types of lung cancer. Several detection radiography techniques to improve sensitivity for the representation of nodules modeling in association with other factors remain imprecise. The results of statistical analysis using Bayesian approach or logistic regression are limited to significant or insignificant with degree of incertitude. After the analysis system is done, the analysis system proposed takes into consideration uncertainties in these factors.