During surgical interventions for colorectal cancer, the doctor needs to analyze at least 12 lymph nodes per 1 patient. Under conditions of lack of personnel and increase in the incidence of colorectal cancer, such routine procedures carry a huge burden on the pathomorphologist. By identifying colorectal cancer metastases in the lymph nodes, Medical Neuronets algorithms allow doctors to save time to solve more complex problems and improve diagnostic accuracy

One of the key problems that doctors face when diagnosing lung cancer is the lack of biological material for pathomorphological and molecular genetic studies. To determine the histological affiliation of a mass in the lung, it is often necessary to use one or several IHC tests, where each one wastes precious biomaterial. Our AI algorithms make it possible to identify two main histological variants of non-small cell lung cancer: adenocarcinoma and squamous cell carcinoma - by standard staining, thereby reducing the financial and labor costs for verifying the diagnosis and saving biomaterial for molecular genetic studies

All cases of mammary cancer are subject to IHC evaluation for HER2, ER, PR, Ki-67 markers to determine the patient's treatment tactics. The calculation of these markers by a pathomorphologist in a high-load laboratory is a very labor-consuming process. Our company's AI algorithms allow to automatically count the specified markers on a full slide image and help the doctor make decisions on treatment tactics faster and in a more balanced way

We have developed the cloud platform "MEDICAL NEURONETS," which provides users with an accessible and user-friendly interface for interacting with artificial intelligence modules. This platform can be used for analysis in pathomorphological laboratories and clinics.

User manual