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Deep Label-Free Cell Imaging of Liquid Biopsies for Cancer Monitoring

Project description

Innovative AI-assisted cancer diagnostics in liquid biopsies

The EU-funded LF-LB project aims to develop innovative cancer diagnostics in liquid biopsies based on label-free interferometric phase microscopy (IPM) coupled with real-time AI-assisted cell classification. The project capitalises on previous research using IPM for grading the metastatic potential of cancer cells. The proposed device will dramatically improve patient care through accurate cancer monitoring in the clinical lab using standard liquid biopsy. The objectives include building the first clinical IPM and microfluidic imaging flow cytometry devices, developing high-throughput processing of urine samples, and training a deep neural network to detect cancer cells based on the IPM images of cancer cells during flow.

Objective

We will develop and commercialize an innovative device for diagnosis and monitoring of cancer in liquid biopsies based on a label-free interferometric phase microscopy (IPM) unit, coupled with dedicated real-time artificial intelligence (AI) for cell classification. This device will materialize an innovative approach for the much-anticipated imaging flow cytometry, dramatically decreasing its costs, and improving patient care by accurate monitoring of cancer in the clinical lab from a simple lab test (liquid biopsy). The success of the project is dependent on four high-risk/high-gain aspects: (a) Building the first clinical IPM device. (b) Designing and manufacturing a disposable microfluidic device for imaging flow cytometry. (c) Obtaining high-enough acquisition and processing throughput in imaging flow cytometry of urine samples. (d) Training a deep natural network to detect cancer cells based on the information-deep label-free IPM images of cancer cells during flow. The proposed PoC project stems from my on-going ERC StG project that focuses on the application of IPM for grading the metastatic potential of cancer cells, as a basic-science research tool.

Fields of science

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Host institution

TEL AVIV UNIVERSITY
Net EU contribution
€ 150 000,00
Total cost
No data

Beneficiaries (1)