We increase the efficiency of pathomorphological studies using AI technologies
What we do?
OUR SOLUTIONS
Colorectal cancer
Tasks to be solved
Search for metastases in the lymph nodes
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
Решаемые задачи
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
Search for metastases in the lymph nodes
Lung cancer
Tasks to be solved
Determination of the two main histological subtypes of non-small cell lung cancer from standard stained histological images (H&E)
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
Решаемые задачи
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
Determination of the two main histological subtypes of non-small cell lung cancer from standard stained histological images (H&E)
Mammary cancer
Tasks to be solved
Quantification of 4 key IHC markers of mammary cancer
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
Решаемые задачи
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
Quantification of 4 key IHC markers of mammary cancer
Why us?
We reduce the time of research
We increase the efficiency of diagnostics
We reduce the workload on doctors
We help personalize therapy
Media about us
Mayor of Moscow opened the Technopark of the Moscow Center for Healthcare Innovations
The winners of the Future Healthcare accelerator announced in Moscow
Contact us
Ruslan Parchiev, CEO
© 2022 Medical Neuronets, Moscow