AiforCancerDX | Deep learning AI in cancer diagnostics

Summary
Aging populations and increasing cancer rates are increasing the number of samples in healthcare. Despite the growing trend of digitalisation, pathologists are still reviewing the samples manually, which is a subjective, time-consuming, expensive manual process that exposes patients to the risk of misdiagnosis. Thus, there is a clear need for an affordable tool that supports and streamlines the workflows of pathologist, complementing humans with automated and objective analysis.

Aiforia® Cloud platform augments the efficiency and consistency of a pathologist’s clinical workflow. It removes the slow and inconsistent manual work by automatically performing a range of laborious image analysis tasks, in a fraction of time with unprecedented accuracy and consistency. The productivity leap is enabled by our deep learning AI software, implemented on a cloud platform, and developed specifically for pathology. The platform has been built by Aiforia Technologies Oy, a Finnish SME, bringing together a team of experts in medicine, business, and software development.

The award-winning Aiforia® Cloud is already used by more than 50 organisations for research purposes. With the AiforCancerDx project, the platform will be introduced to clinical use. We will build the world’s first ready-made deep learning algorithms for automated cancer diagnostics. The project allows Aiforia Technologies to remain at the forefront of the digital revolution of pathology and capture a significant share of the global market expected to grow to €900 million by 2020.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/880718
Start date: 01-11-2019
End date: 31-10-2021
Total budget - Public funding: 2 930 000,00 Euro - 2 051 000,00 Euro
Cordis data

Original description

Aging populations and increasing cancer rates are increasing the number of samples in healthcare. Despite the growing trend of digitalisation, pathologists are still reviewing the samples manually, which is a subjective, time-consuming, expensive manual process that exposes patients to the risk of misdiagnosis. Thus, there is a clear need for an affordable tool that supports and streamlines the workflows of pathologist, complementing humans with automated and objective analysis.

Aiforia® Cloud platform augments the efficiency and consistency of a pathologist’s clinical workflow. It removes the slow and inconsistent manual work by automatically performing a range of laborious image analysis tasks, in a fraction of time with unprecedented accuracy and consistency. The productivity leap is enabled by our deep learning AI software, implemented on a cloud platform, and developed specifically for pathology. The platform has been built by Aiforia Technologies Oy, a Finnish SME, bringing together a team of experts in medicine, business, and software development.

The award-winning Aiforia® Cloud is already used by more than 50 organisations for research purposes. With the AiforCancerDx project, the platform will be introduced to clinical use. We will build the world’s first ready-made deep learning algorithms for automated cancer diagnostics. The project allows Aiforia Technologies to remain at the forefront of the digital revolution of pathology and capture a significant share of the global market expected to grow to €900 million by 2020.

Status

CLOSED

Call topic

EIC-SMEInst-2018-2020

Update Date

27-10-2022
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Horizon 2020
H2020-EU.2. INDUSTRIAL LEADERSHIP
H2020-EU.2.1. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies
H2020-EU.2.1.0. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Cross-cutting calls
H2020-EIC-SMEInst-2018-2020
H2020-SMEInst-2018-2020-2
H2020-EU.2.3. INDUSTRIAL LEADERSHIP - Innovation In SMEs
H2020-EU.2.3.0. INDUSTRIAL LEADERSHIP - Innovation In SMEs - Cross-cutting calls
H2020-EIC-SMEInst-2018-2020
H2020-SMEInst-2018-2020-2
H2020-EU.3. SOCIETAL CHALLENGES
H2020-EU.3.0. Cross-cutting call topics
H2020-EIC-SMEInst-2018-2020
H2020-SMEInst-2018-2020-2