Research Projects All Projects
Projects

Quick links

AI-improved organ on chip cultivation for personalised medicine (AImOOC)

 

 

Project Title: AI-improved organ on chip cultivation for personalised medicine (AImOOC)

Funding: European Regional Development Fund (ERDF), Measure 1.1.1.1 “Support for applied research”

Project No.: 1.1.1.1/21/A/079

Period: 1 January 2022 – 30 November 2023

Project costs: 500 000,00 EUR

Project implementer: Institute of Electronics and Computer Science 

Cooperation partner: Latvian Biomedical Research and Study Centre

Cooperation partner: SIA „Cellboxlab”

Principle Investigator BMC: Dr. biol. A. Ābols

 

Project summary:

This project focuses on the development of a machine learning algorithm to improve of patient-derived cell culturing in organ on chip devices. Such algorithm development would enable to adopt more widespread use patient-derived material for organ on chip devices, thus allowing scientists in academia and industry to derive more representative model systems. Therefore, the aim of the project is to apply machine learning (ML) algorithms on microfluidics based on bright field microscopy, TEER (Trans Epithelial Electric Resistance) and O2 biosensor data in real time to cultivate different cell cultures (including those obtained from patient samples) on OOC platform. In order to achieve this aim, we have defined the following objectives: (1) organ on chip cell culture data generation, (2) bright field microscopy system development for organ on chip monitoring in real time, (3) machine learning based computer vision algorithm development to process generated data for microfluidics and finally (4) validation of developed algorithm on organ on chip devices by using cells derived from patient samples. The main outcomes of the project are: (1) data in the form of images and sensor read out from lung and gut cell culturing using various flow parameters, (2) development of the moving stage and chip imaging system for use in culture chamber, (3) a machine learning-based system for automating cell culturing and finally (4) patient derived cell culturing in organ on chip systems controlled by the developed machine learning algorithm.

Information published 03.01.2022.



Mājas lapas izstrādi finansēja ERAF 2.1.1.2. aktivitātes projekts Nr. 2010/0196/2DP/2.1.1.2.0/10/APIA/VIAA/004 "Latvijas biomedicīnas pētījumu integrācija Eiropas zinātnes telpā".