In this article, the authors present a groundbreaking methodology that combines artificial intelligence (AI) with 3D imaging flow cytometry to analyze intact, living cells. By capturing high-resolution images and leveraging a fused convolutional autoencoder (CAE) classifier, researchers can extract deep, predictive insights at the single-cell level. 

Unlike traditional single-cell genomics, which destroys cells during analysis, this AI-driven approach preserves cell integrity, allowing for the validation of predictions over time.

The key highlights of this study can be summarized as follows:

• Exceptional prediction accuracy: the AI model achieved 88% accuracy in predicting which cells would exhibit high protein expression and an impressive 99.4% accuracy in identifying cells that had undergone stress, such as glucose deprivation.
• Cutting-edge technology: a novel 3D imaging flow cytometer captures detailed, marker-free images of individual cells, which are then analyzed using an advanced CAE-based AI model to forecast their future properties.
• Transformative applications: this innovative approach has the potential to redefine drug development, preventive medicine, and cell therapy by enabling early identification of high-performing cells for monoclonal antibody production and the detection of precancerous conditions.