CALIPSO

CALIPSO (Computer Assisted Live Imaging for Predictive Screening of Organoids) utilises deep learning in microscopy to simplify studying organoids’ morphology in real-time 3D, enabling streamlined growth, imaging, and analysis. This leads to breakthroughs in micro-organism imagery. Data from CALIPSO undergoes machine learning to provide new quantitative statistical analysis, allowing for individual organoid outcome prediction in a non-destructive way. CALIPSO resets lab practices with a user-centred architecture for smoother and more efficient usage. It enhances researchers’ freedom of movement with an L-shaped opening for easier access to the stage chamber, improving the placement of petri dishes and other experiment elements. The automated sliding door minimises unwanted movements during experiments and ensures complete blackout when closed, enhancing research accuracy and reliability in a controlled environment. The machine was designed to streamline tasks, establish a clear hierarchy of functions, and enhance manipulations. This enables lab users to focus on image acquisition, eliminating challenges from outdated methodologies or archaic product architectures usually found in this high-precision sector. A fully functional prototype was built to operate long-term test runs in real lab conditions. Red Dot Award: Design Concept | Concept | Medical Devices and Technology