Brainscan.ai
Empowering radiologist to do more

Market

The global CT scanners market size is poised to grow by USD 1.79 billion during 2020-2024

Projected increase: CAGR of 6.5% by 2026..

23 million brain computed tomographies a year

52 million CT scans are performed annually, and over 51 million within the EEA

Problems

High number of tests

There is a much higher rate of computed tomography examinations in the US. 7084 brain tests per 100,000 persons, and in Poland that amounts to 765 examinations per 100,000 people.

Diagnosis takes to long

There is a lack of 16,000 radiologists in Great Britain alone. Specialists need tools that will speed up the interpretations process and reduce the likelihood of misdiagnosis.

Errors in interpretations

As much as 60% of CT imaging tests are those of the brain, and at that scale, errors in interpretations reach 20%.

Based on the analysis of more than 116 596 brain CT scans, we developed a CE certified BrainScan.ai system

Dataset Q2'2020

0

Providers # of all examinations
Data source A 4 616
Data source B 48 840
Data source C 14 864
Data source D 15 571
Data source E 32 705
Total 116 596

Dataset Q4'2020

0

Providers # of all examinations
Data source A 66 675
Data source B 48 840
Data source C 45 000
Data source D 14 864
Data source E 15 571
Data source F 32 705
Data source G 88 000
Total 316 271

Solution

BrainScan system based on Artificial Intelligence enables automatic detection and classification of pathological changes occurring in CT examinations of the brain. That Provides doctors with additional information that allows for a faster and more efficient interpretations.

We help radiologists interpret brain CT scans faster and more confidently by using machine learning methods to generate valuable data.

How does it work?

CT Scanning
The series of scans from the machine is sent as DICOM files to the PACS server. The script filters all brain CT scans. Anonymizes them and forward to the BrainScan Cloud.
Analysis
Algorithms powered by Artificial Intelligence automatically analyze scans finding potential pathological changes. The results are saved in a form of infographic and are visible by the doctors as the next serie in the study and saved in a DICOM file.
Results
The results of the analysis in a form of infographic (an additional series saved in the DICOM file) and in a structured text form (JSON file) are returned to the PACS server from which they were sent. The advantages of a form of infographic is that it is available in every DICOM viewers used by doctors.

The effectiveness of our algorithms

Classifier assessment based on ROC - Area Under the Curve (AUC) The ROC curve is one way to visualize the quality of the classification, showing the relationship between TPR (True Positive Rate) and FPR (False Positive Rate) ratios. Classifier assessment based on ROC - Area Under ROC Curve.

Plans for H2’2020

BrainScan CT - is a system supporting the interpretations of computed tomography examinations of the brain using machine learning methods

PN-EN ISO 13485:2016-04
TNP/MDD/0264/4823/2019
version 1.1, release date: 14.02.2019

Benefits:

  • Shorter time between examination and scan description
  • Early alert - detailed auto-detection of pathological changes

  • Increased number of analysed CT scans in time unit, cause higher efficiency of radiologists.
  • Downsed error level of incorrect diagnosis by omitting existing pathological changes in the CT scans.
  • Empowering the image of the facility through the use of the latest technologies and scientific achievements.

  • Double verification of the diagnosis
  • Increased work efficiency, give human beings more time to analyze much more difficult cases

Partners

Awards & prizes

Brainscan.ai
Registration address:

BrainScan Sp. z o. o.
Szkolna 10/1
81-363 Gdynia
NIP: PL5862321880
Correspondence address (offices):

BrainScan Sp. z o. o.
ul. Kieturakisa 10/14
80-741 Gdańsk
contact@brainscan.ai
BrainScan Sp. z o.o. implements the project "BrainScan - a system for automatically supporting medical diagnostics, detection and location of potential pathological changes occurring in imaging of the head", co-financed under the Intelligent Development Operational Programme 2014-2020 (IDOP) measure 1.1 / sub-measure 1.1.1 co-financed by the European Regional Development Fund (ERDF). Application # POIR.01.01.01-00-0573 / 18.