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DoMore Diagnostics

DoMore Diagnostics develops AI-driven diagnostic tools for precision medicine to personalize cancer treatment decisions. The company utilizes digital pathology and novel algorithms to create prognostic assays, such as Histotype Px® Colorectal. This technology aims to provide objective prognosis for cancer patients, reducing overtreatment and undertreatment.

Oslo, NorwayFounded 2021111K+ followers
Updated 20 months ago

Funding

$12.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

EA
Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Current cancer diagnostics lack objective methods for accurately predicting disease progression and prognosis, hindering the development of personalized treatment plans and potentially impacting patient outcomes. This limitation stems from the reliance on subjective assessments and the absence of precise tools for gauging individual patient risk.

Solution

DoMore Diagnostics leverages artificial intelligence to provide a more objective and accurate assessment of cancer progression and prognosis. By applying advanced AI algorithms to digital pathology data, the company aims to transform cancer diagnostics, enabling clinicians to make more informed treatment decisions tailored to the individual patient. This approach seeks to reduce both over- and under-treatment by providing a new system for cancer prognosis. The AI-driven technology analyzes complex patterns in patient data to predict disease trajectory and identify optimal treatment strategies.

Target Audience

The primary target audience includes oncologists, pathologists, and pharmaceutical companies involved in cancer drug development.

Features

  • AI-powered analysis of digital pathology images for objective cancer prognosis
  • Algorithms trained on data from leading universities (University in Oslo, Oxford University and University College of London)
  • Identification of patterns indicative of cancer progression and treatment response
  • Integration with existing digital pathology workflows
This profile is AI-generated and may contain inaccuracies.