Labor  intensity,  complexity  of  morphology,  the  shortage  of  qualified  specialists  do  not  allow  full  use  of  the  diagnostic  potential  of   microscopic  analysis  of  biomaterials  in  mass  population  surveys.  The  article  discusses  the  technology  of  creating  an  Automatic   Scan  Microscope  Analyzer  of  Oncological  Pathologies  (ASMAOP)  that  uses  neural  network  learning  during  regular  telemedicine   consultations  with  expert  evaluation  of  digital  copies  of  biomaterials  produced  by  a  scanning  microscope.  The  scheme  of  work  of   ASMAOP  in  the  composition  of  a  telemedicine  network,  hardware  solutions  including  platform  for  deep  learning  are  considered.   The  purpose  of  creation  of  ASMAOP  is  to  perform  microscopic  analyses  at  the  level  of  the  experienced  experts  with  a  significant   advantage  in  performance  and  availability.

The article proposes a method for solving the problem of choosing the element base and constructive solutions to ensure the required reliability of promising radio equipment at minimal cost. The problem belongs to the class of Boolean linear programming and is solved using the branch and bound method. The main idea of the branch and bound method is to determine the branching rule for assigning options and further evaluating the objective function on these subsets, which allows us to exclude from consideration subsets that do not contain optimal points. The task of increasing reliability can be solved by choosing more reliable elements and using the method of structural reservation of elements at the stage of product development. The results of using the proposed method to solve the practical problem of choosing the elements are presented.