Metacarpophalangeal deviations can be present from congenital diseases, nerve injuries, direct trauma such as fractures, autoimmune arthropathies such as that associated with lupus erythematosus (Jaccoud), to rheumatoid arthritis (RA), which is an inflammatory disease of joint components with varying degrees of destruction of the small joints of hands, producing a deformation that is evidenced in the deviation of the axis of the fingers having an impact on the functionality of the hand. The application of computational vision and machine learning in the quantification of finger angulation and joint thickening, can provide an objective way to record measures that are routinely performed subjectively if there is no specialized equipment. These measurements can be very useful to have an adequate pre and post intervention record or simply have a standardized record in the case of degenerative pathologies. Through digital image processing, it is possible to define markers that allow us to calculate the angulation and width of the metacarpophalangeal segments at the service of the clinic or useful in research. The results generated were collected in a csv file showing 80% effectiveness in images with arthritis.