Contract no. 126.96.36.199/21/A/079.
The project aims to improve the cultivation of patient-derived cell cultures in OOC (Organ-on-Chip) equipment by applying real-time machine learning algorithms to microfluidics and using light-field microscopy, transepithelial electrical resistance, and O2 sensor data.
The project is implemented by EDI in cooperation with the Latvian Biomedical Research and Study Centre (BNC) and the Limited Liability Company “Cellboxlab”
During first 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?”.