Manifold Learning methods are one of the most exciting developments in machine learning in recent years, yet not fully exploited.
The central idea is to take advantage of the geometry of any data to make the algorithms more efficient by finding what is relevant to your own question. GeodAIsics is expert to launch Manifold Analysis so as to redefine the normal values in Medicine, including for blood sample analysis, quantitative imaging and so on.
Why Manifold Learning when Deep Learning is everywhere?
Deep Learning is based on black box algorithms, while we are proposing fully explainable algorithms. In Sciences, the goal is to explain underlying mechanisms. Because we believe that there is no explanation when using black boxes, Manifold Learning is the ideal solution to help build a better future for healthcare providers.
While both approaches could be associated, depending on your specific project, we believe that Deep Learning is not adapted in an high number of diseases for clinical care. For example, in the case of single subject longitudinal data or in rare diseases and heterogeneous population, Deep Learning will tend to smooth your data specificity and does require a huge amount of data, clearly not adapted to all the situations.
Finally, At GeodAIsics, the protection of the environment is the top-priority. A Manifold analysis relies on sustainable AI with low carbon emissions. Deep Learning is a major actor to energy demand and needs to be used with economy.
Diagnosis / Prognosis
Whatever your field of origin (Imaging, Clinic, Research, Biology, Genetic), we have a solution for individual diagnosis and prognosis.
Blood Sample analysis
GeodAIsics provides Next-Gen reference interval for blood sample analysis. We improve the precision of blood abnormalities detection with our AI technology.
Boost your point-of-care devices!
Evidenced-based medicine is too often based on outdated mathematical tools. Precision medicine requires ready-to-use algorithms for individual diagnosis / prognosis. We support all health providers in their daily routine to help detecting disease whatever the device used for recording data.
Quality check has become an essential step for marked tools to give confidence to regulation authorities. We can implement automatic detector of quality markers.