High-throughput viability screens are commonly used in the identification and development of chemotherapeutic drugs. These systems rely on the fidelity of the cellular model systems to recapitulate the drug response that occurs in vivo. In recent years, there has been an expansion in the utilization of patient-derived materials as well as advanced cell culture techniques, such as multi-cellular tumor organoids, to further enhance the translational relevance of cellular model systems. Simple quantitative analysis remains a challenge, primarily due to the difficulties of robust image segmentation in heterogenous 3D cultures. However, explicit segmentation is not required with the advancement of deep learning, and it can be used for both continuous (regression) or categorical classification problems. Deep learning approaches are additionally benefited by being fully data-driven and highly automatable, thus they can be established and run with minimal to no user-defined parameters. In this article, we describe the development and implementation of a regressive deep learning model trained on brightfield images of patient-derived organoids and use the terminal viability readout (CellTiter-Glo) as training labels. Ultimately, this has led to the generation of a non-invasive and label-free tool to evaluate changes in organoid viability.
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