Técnicas De Inteligencia Artificial Aplicadas A Pruebas Capilaroscopicas Para La Detección De Enfermedades Autoinmunes Que Comprometen La Circulación Sanguínea

The main purpose of this paper is the development of an artificial intelligence model for the automatic classification of images, in order to optimize the detection of pathologies through capillaroscopy tests of the nail fold, this technique allows obtaining images of the morphology of the capillaries in the proximal nail fold of the hands. We used a database that consists of 300 images of capillaries corresponding to the nail fold. These images were labeled as healthy or diseased subject depending on the patterns of the capillaries. The method used to classify the images into two classes was transfer learning from a MobileNet V2 base model. The results show that the network is capable of detecting the presence of pathological patterns in the capillaries with a precision of 96.667%.

Laparotomy with partial overgrowth of the birth canal

In the number of defects in the development of female genital organs, a very prominent place is occupied by innate partial overgrowths of the generic canal, although this is rare, but entailing very serious consequences for the health and life of the diseased subject. These overgrowths, or, to put it bluntly, underdevelopment of the lumen of the birth canal, can meet at the entrance to the vagina, along the length of the sleeve, or into the canal of the cervix. In addition, they can be complicated in some cases by the accumulation of blood in the tubes. As for the origin of these forms of developmental defects, at the present time all researchers agree that their cause is either sticky inflammation of the walls of the sleeves in the fetal life, or incomplete fusion of Mllerian filaments with obliteration of the cavity of one of them.