Abstract 2311: Precise assessment of cancer cell growth and survival by artificial intelligence
Authors: Cheung Pang Wong, Nasrin Khazamipour, Soroush Alibagi, Joya Saade, Daria Golanarian, Negin Farivar, Mads Daugaard, Nader Al Nakouzi
Journal: Cancer Research (2024)

Abstract Introduction: Cancer research requires accurate methods for measuring analytical parameters like cell culture confluence, cell count, colony numbers, viability, and motility. These methods must be unbiased and user-independent for reproducible data. Cell analytics involves manual processes (e.g., manual cell counting) or reagent-based approaches (e.g., viability kits). In recent years, semi-automated systems have been introduced that can either count cells, measure cell growth by density tracking, and/or determine cell viability. However, these methods are often time-consuming, require reagents and labeling, and may involve costly instrumentation. Artificial Intelligence (AI) has made strides in clinical and laboratory research, holding promise for swift integration into cancer research. Here, we present the development and validation of SnapCyte™, an AI performing accurate, unbiased, label- and reagent-free cell analytics from basic cell culture images, independent of specialized instrumentation. Materials and Methods: Cell images were generated using diverse cell lines (MCF7, PC3, Hela, Raji⋯) cultured in various vessels with and without treatments (Taxane, Cisplatin, Heatshock⋯). For the cell count dataset, adherent cells were detached using trypsin, stained with trypan blue and loaded into a standard hemocytometer. Images were acquired using the SnapCyte™ adaptor and diverse microscopes (Leica, Hund, Zeiss, Nikon) and cell phones. Multiple datasets of 500 annotated images each were created, with images masked for confluency or for singular live and dead cells by experienced scientists. Object segmentation utilized the UNet architecture for localizing cells in cell culture images and iterative training was applied to achieve the required accuracy. Results: After multiple training iterations, SnapCyte™ AI detection models achieved 99% precision for confluency and &amp;gt;95% precision and recall for cell count. SnapCyte™ surpassed standard methods (Crystal Violet, WST1, MTT, Presto blue, CyQuant, Incucyte®, and Bio-Rad TC20 cell counter), displaying high accuracy and smaller standard error variation than reagent-based assays. Compared to IncuCyte® Bio-Rad TC20, SnapCyte™ demonstrated similar accuracy and greater user-independent results. Furthermore, SnapCyte™ acquired data in under 10 minutes, with non-invasive measurements, allowing direct use of cells in downstream assays. Conclusion: We have developed and validated an AI model for advanced cell analytics. Our data show that the SnapCyte ™ AI is at par or better than existing reagent and instrument-based solutions in assessing cell confluency, number, and viability. This technology offers a fast, accurate, and unbiased cell analytics platform that is resistant to user variations, and independent of reagents and costly equipment. Citation Format: Cheung Pang Wong, Nasrin Khazamipour, Soroush Alibagi, Joya Saade, Daria Golanarian, Negin Farivar, Mads Daugaard, Nader Al Nakouzi. Precise assessment of cancer cell growth and survival by artificial intelligence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2311.