Technologies
We have created a unique technology for analyzing medical images using artificial intelligence. Our datasets contain thousands of images marked up by qualified specialists
The platform consists of:
Artificial intelligence for medical
image analysis
Tools for visualization of
analysis results
Customizable processes for implementing the interaction of a doctor and platform technologies (Botkin Workflows)
Multimodal DICOM viewer and tools for marking up studies
Integration with MIS by HL7/FHIR
Creation of protocols according to the DICOM-SR and SC standard
Key elements of the platform
Professional DICOM viewer with artificial intelligence functionality

Access via the web interface anywhere:
• Visualization of pathologies detected by artificial intelligence
• Automatic prioritization of research
• Comparison of several series in one window
• Research Markup tools
• Tools for accessing collaboration with research
DICOM Viewer
Botkin Workflow
Our solution allows you to manage the flow of research using the tools of orchestration, research routing, Botkin Gateway integration gateway and infrastructure for scaling services for automatic medical image processing.

• Configuring the research processing scenario in accordance with the tasks, the content of DICOM tags, types of studies, pathologies
• Determination of the processing status at each step of the research scenario
• Logging of all stages of research processing
• Visual representation of processing statuses to improve the efficiency of working with a large flow of research

Botkin Gateway
Our gateway is designed for integration with medical imaging archive and PACS. It is deployed in the network circuit of a medical organization and allows to configure interaction between the PACS server of a medical organization and a cloud-based research processing platform.

Gateway functionality
• Depersonalization of studies by selected DICOM tags.
• Filtering of PACS server studies by the content of tags
for further data processing .
• interacts with any any number of PACS servers
Project options
The process of employing the artificial intelligence to reevaluate the data arrays stored within the archives of a medical establishment
Retrospective Analysis
Purpose
Process
Advantages
Analysis and detection of secondary pathologies
  • Iintegration of the platform with the clinic
  • Automated data collection, depersonalization, analysis
  • Demonstration of research results in the doctor's workspace
  • Additional options include the quality control of diagnostic procedures
  • Automated process without additional workload for doctors
  • Setting up neural networks for the project


Real-time AI-based diagnosis assistance
Prospective analysis
•‎ Up to 6 minutes for CT-scans
•‎ Up to 1 minute for x-rays
•‎ Up to 3 minutes for mammography


Processing time
  • To detect and to describe any pathologies the patient might have
  • To highlight any particularly dangerous spots on the image to minimize the chance of a diagnostic error
Purpose
  • AI analyses the medical image in real time
  • AI spots any pathologies on the image
  • AI then returns the processed image, with the pathologies highlighted and described
Process
Additional analysis of mammographic studies using artificial intelligence.
SECOND OPINION
  • Quality control of medical research
  • Remote image analysis (teleradiology module)
  • Double reading of mammograms (artificial intelligence + doctor) ensures high diagnostic efficiency


Advantages
  • To reduce the number of diagnostic errors
  • To increase the detection of oncology in the early stages
Purpose
  • Integration with PACS server or medical image archive
  • Automated data collection, depersonalization
  • Routing of the image to doctors of the second reading
  • Analysis of research using artificial intelligence, protocol formation
  • Uploading the results to the platform or transferring to medical image archive
Process
Patents Botkin.AI
A method of forming mathematical models of a patient using artificial intelligence technologies

Patent for invention No. 2720363
Priority of the invention on December 29, 2017
Date of state registration in the State Register of Inventions of the Russian Federation April 29, 2020

Authors: Drokin Ivan Sergeevich, Bukhvalov Oleg Leonidovich, Sorokin Sergey Yurievich
A method and system for supporting medical decision-making using mathematical models of patient representation

Patent for invention No. 2703679
Priority of the invention on December 29, 2017
Date of state registration in the State Register of Inventions of the Russian Federation October 21, 2020

Authors: Drokin Ivan Sergeevich, Bukhvalov Oleg Leonidovich, Sorokin Sergey Yurievich
Automatic system for processing medical images of organs of the patient's body and the method implemented by this system

Patent for invention No. №2771512
Priority of the invention on September 24, 2040
Date of state registration in the State Register of Inventions of the Russian Federation May 05, 2022

Authors: Drokin Ivan Sergeevich, Bukhvalov Oleg Leonidovich, Sorokin Sergey Yurievich
Scientific articles
Опыт разработки и внедрения системы поиска онкологических образований с помощью искусственного интеллекта
A Model-Free Comorbidities-Based Events Prediction in ICU Unit
End-to-end lung nodule detection framework with model-based feature projection block
Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans
GANs 'N Lungs: improving pneumonia prediction. Accepted to MIDL 2019 as extended abstract
Data Augmentation with GAN: Improving Chest X-Ray Pathologies Prediction on Class-Imbalanced Cases
10.06.2021
07.05.2020
17.09.2019
01.08.2019
15.12.2019
27.09.2018
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Contacts
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Sales:
Marketing:
Bol'shoy Bul'var, 42/1,
Skolkovo, Moscow, 143026

Professora Popova str., 23 V,
Saint Peterburg, 197022


8 (495) 649-13-00
info@botkin.ai
hr@botkin.ai
sales@botkin.ai
media@botkin.ai
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Research is carried out with grant support from the Skolkovo Foundation