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LATVIAN

BIOMEDICAL

RESEARCH AND STUDY CENTRE


RESEARCH AND EDUCATION IN BIOMEDICINE FROM GENES TO HUMAN

Project cofinanced by REACT-EU to mitigate the effects of the pandemic crisis

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.

Progress of the project:

1 January 2022 – 31 March 2022

During this reporting period, we have developed decision tree for data classification and produced first imaging data of successful and unsuccessful OOC cultivation data for developing AI models for supervising OOC cultivation. We identified possible approaches to the generation of synthetic data for training AI models. Due to the nature of the real-world data, the most promising approach is to generate synthetic data by means of generating simple geometric shapes and subsequently deforming them. We are currently conducting a survey of literature on that topic. We have investigated integrated objective/camera units for integration in the instrument from various providers with particular focus on evaluation of image quality, digital zoom capabilities and lighting conditions. We have started working on defining the procurement specification for XYZ gantry with a suitable XY step for continuous channel imaging and Z-step for successful autofocus on the aforementioned imaging units. Additionally, during this period, we engaged in public dissemination of project topic in student council of Riga Technical University organised online interview in Spiikiizi studio, titled “What if?”.

Information published 31.03.2022.

Progress of the project:

1 April 2022 – 30 June 2022

During this reporting period, we have generated additionally 230 pictures of both lung and gut on chip models by applying both stable and primary cell lines. For each picture information such as model ID, cell type, seeding density, time of image, decision (good, bad, acceptable), artefacts were prepared.  EDI investigated state-of-the-art approaches in literature to the generation of synthetic images of biological cells by deforming simple geometric shapes. Furthermore, EDI investigated the use of generative adversarial networks (GANs) for simulation-to-real transfer, which is needed to render synthetic images more realistic. CellboxLabs conducted several hours interview with potential end users in industry about OOC real time microscopy option in combination with AI to confirm the necessity of such system. Additionally, CellboxLabs made purchases for microscopes to conduct tests with them. They made a purchase for the parts of the XYZ table and currently are working on a design for the XYZ table that would allow us to make an XYZ motion system with motion and small position errors.

Information published 30.06.2022.