SpineServ specializes in mechanical testing of  surgical implants, surgical instruments and implant materials. Spinal implants, osteosynthesis implants, endoprostheses and dental implants, surgical instruments, implant materials and biomaterials undergo static testing (stiffness, strength), dynamic testing (fatigue testing), wear testing or corrosion testing either according to standards (ASTM, ISO) or following custom testing procedures. Due to our expertise in biomechanics, medicine and biology we also offer our know-how to answer questions around the spine through scientific consulting, expert opinion and literature reviews. SpineServ is a spin-off company of the research group of Prof. Dr. Hans-Joachim Wilke (Institute of Orthopaedic Research, University of Ulm, www.biomechanics.de). It was founded in 2007 and is specialized in providing a broad variety of services around the spine. Spineserv was recognized in 2008 as one of the top ranked start-up companies in Baden-Württemberg. We have long term interdisciplinary specialist skills in mechanics, biomechanics, engineering and biology. Our close connection to university research in orthopaedic research and biomechanics guarantees consideration of the latest scientific knowledge. Also based on this cooperation, SpineServ is committed to research and development. For example, testing procedures are continuously developed and optimized to address the design and the function of all kinds of implant and their newest developments. Also, research and development is used to open up new business areas. One of the newest research and development projects in this field is dedicated to the development of a machine learning software tool, which allows a standardized evaluation of clinical radiographs, CT scans and MR images. The aim is to feed this tool with clinical images of the spinal column and to receive a standardized evaluation of various parameters such as disc height, lordosis angle, fusion status or degree of intervertebral disc degeneration. We were able to show the feasibility of this new computational approach (no-one was using machine learning algorithms for this purpose previously) however there is more work to be completed to ensure validation and viable for use.