Using machine learning and/or neural networks to validate stem cells and their derivatives (2-D cells and 3-D tissues) for use in cell therapy and tissue engineered products

A computing system for non-invasively predicting characteristics of one or more cells and cell derivatives, the computing system comprising:

• a memory configured to store instructions;
• a processor disposed in communication with the memory, wherein the processor, upon execution of the instructions is configured to:
• train a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells;
• receive at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired by quantitative bright-field absorbance microscopy (QBAM);
• provide the at least one test cell image to the trained machine learning model;
• predict, using machine learning based on the trained machine learning model, characteristics of the at least one test cell;
• and
• generate, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.