Anusha Klett ^1,2* Dennis Raith ³ Paula Silvestrini ⁴ Matías Stingl ⁵ Jonas Bermeitinger ⁵ Avani Sapre ^1,2,5 Martin Condor ⁵ Roman Melachrinos ⁵ Mira Kusterer ⁶ Alexandra Brand ⁶ Guido Pisani ^1,2 Evelyn Ullrich ^{1,10,11,12,7,8,9} Marie Follo ^13,6 Jesús Duque-Afonso ⁶ Roland Mertelsmann ^1,2

• ¹ Collaborative Research Institute Intelligent Oncology, Freiburg, Germany
• ² Mertelsmann Foundation, Freiburg, Germany
• ³ Neurorobotics Lab, Department of Computer Science, University of Freiburg, Germany., Freiburg, Germany
• ⁴ Laboratory of Applied Cellular and Molecular Biology, Institute of Veterinary Sciences of the Litoral (ICIVET), Universidad Nacional del Litoral (UNL) - (CONICET), Esperanza, Argentina
• ⁵ LABMaiTE GmbH, Freiburg, Germany
• ⁶ Department of Medicine I, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany., Freiburg, Germany
• ⁷ Department of Pediatrics, Goethe University Frankfurt, Halle, Bavaria, Germany
• ⁸ Experimental Immunology and Cell Therapy, Frankfurt am Main, Germany, Frankfurt, Germany
• ⁹ Frankfurt Cancer Institute (FCI), Frankfurt, Hesse, Germany
• ¹⁰ University Cancer Center (UCT) Frankfurt; Mildred Scheel Career Center (MSNZ), Frankfurt, Germany
• ¹¹ Hospital of the Goethe University Frankfurt, Germany, Frankfurt, Germany
• ¹² German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, Frankfurt, Hesse, Germany
• ¹³ Lighthouse Core Facility, Medical Center – University of Freiburg, Freiburg, Germany

The colony forming assay (CFA) stands as a cornerstone technique for evaluating the clonal expansion ability of single cancer cells and is crucial for assessing drug efficacy.However, traditional CFAs rely on labor-intensive, endpoint manual counting, offering limited insights into the dynamic effects of treatment. To overcome these limitations, we developed an Artificial Intelligence (AI)-assisted automated CFA combining time-lapse microscopy for real-time tracking of colony formation. Using B-acute lymphoblastic leukemia (B-ALL) cells from an E2A-PBX1 mouse model, we cultured them in a collagen-based 3D matrix with cytokines under static conditions in a low volume (60 µl) culture vessel and validated its comparability to methylcellulose-based media. No significant differences in final colony count or plating efficiency were observed. Our automated platform utilizes a deep learning and multi-object tracking approach for colony counting. Brightfield images were used to train a YOLOv8 object detection network, achieving a mAP50 score of 86% for identifying single cells, clusters, and colonies, and 97% accuracy for Z-stack colony identification with a multi-object tracking algorithm.The detection model accurately identified the majority of objects in the dataset. This AIassisted CFA was successfully applied for density optimization, enabling the determination of seeding densities that maximize plating efficiency (PE), and for IC50 determination, offering an efficient, less labor-intensive method for testing drug concentrations.In conclusion, our novel AI-assisted automated colony counting platform enables automated, high-throughput analysis of colony dynamics, significantly reducing labor and increasing accuracy. Furthermore, it allows detailed, long-term studies of cell-cell interactions and treatment responses using live-cell imaging and AI-assisted cell tracking.Future integration with a perfusion-based drug screening system promises to enhance personalized cancer therapy by optimizing broad drug screening approaches and enabling real-time evaluation of therapeutic efficacy.