Owkin Diagnostics Orb

Our diagnostics approach

A fuller picture of
a patient’s disease

We build impactful digital pathology diagnostics

Challenges

What if we could help pathologists and oncologists deliver precision medicine to their patients?

Challenge 1

Treatment burden

Treatments for cancer can be toxic, invasive, expensive and may not always be necessary or effective.
Challenge 2
Disease heterogeneity
Cancer is a highly heterogeneous disease. Even cancer of one tissue can have many subtypes. Each subtype may respond to treatments differently.
Challenge 3
Anatomic pathology is underutilized
Digital pathology slides hold vast amounts of hidden insights and unlike genomic data, they are routinely generated in the clinical workflow.

Context

Is AI the key to unlocking digital pathology’s potential?

With the advancements of machine learning, the abundance of information contained in pathology slides can be leveraged like never before.

AI solutions can support pathologists in daily tasks, prescreen for predictive biomarkers, and provide insight into patient outcomes to support treatment decision making.

Precision medicine

Not all AI tools are alike

AI-based solutions will make precision medicine more effective and accessible.
*Echie et al (Kather group), 2020, bJC
AI based solutions will make precision medicine more effective and accessibleAI based solutions will make precision medicine more effective and accessible

Owkin’s solution

Prescreen for biomarkers and assess patient outcomes

Owkin delivers AI diagnostics that integrate seamlessly into the digital pathology workflows to support accurate and timely diagnosis.

Giving healthcare providers a fuller picture of a patient’s disease means more patients can benefit from targeted therapies, making precision medicine more accessible to more patients at an earlier stage of their disease.

Research use only

Tertiary Lymphoid Structures

We also have a pre-screening tool that uses AI to detect the presence or absence of TLS across tumor types directly from H&E slides for a research setting.

TLS presence is considered a novel biomarker to stratify the overall survival risk of untreated cancer patients and as a marker of efficient immunotherapies.  A simple and standardized approach to measure TLS could support the adoption of this biomarker as an independent predictor of response to immune checkpoint inhibitors (ICI).

Companion diagnostics

We develop digital pathology based companion diagnostics, either in house or through partnership with biopharma, to support the clinical development and market access of novel therapies.
Pre-screening for known biomarkers

Identify subgroups of patients that benefit from targeted therapies.

Novel biomarkers & outcome prediction

Match new therapies to patients and/or better characterise patients’ risk.

Build a CDx with us

What makes Owkin different?

Owkin data expertise focuses on diversity and multimodality

MSI model performance
MSI model performance

Owkin's MSIntuit® CRC is the highest performing AI model in CRC on the market.1

Academic network
Academic network

Our trusted KOL network and connection to leading cancer centers in Europe/US to identify the datasets and right partners to work with.

Scientific track record
Scientific track record

Owkin has a track record of publications in leading medical peer-reviewed journals (Nature Medicine, Hepatology, JCO etc) and societies (ESMO, ASCO, USCAP).

Recognition and grants
Recognition and grants

PortrAIt consortium funded by French Public Investment Bank to develop AI diagnostic in pathology in France in 30 centers, including the 19 expert oncology centers in France.

Affiliate consortia

Owkin consortia

"Owkin's MSIntuit CRC represents a quantum leap in the use of predictive AI, at the service of diagnosis and our patients. The secure integration with Owkin's tools was a success thanks to the in-depth validation and training of our teams. At all stages of the project management, Owkin was focused on data security."
Dr Nicolas Weinbreck
MD Pathologist, Vice-President of MEDIPATH network

Citations

  1. Bilal, M., Nimir, M., Snead, D. et al. Role of AI and digital pathology for colorectal immuno-oncology. Br J Cancer 128, 3–11 (2023). https://doi.org/10.1038/s41416-022-01986-1
Information updated on 3rd June 2024