Efficient spheroid morphology assessment with a ChatGPT data analyst: implications for cell therapy

Background: Adipose-derived stem cells (ADSCs) exhibit promising potential for the treatment of various diseases, including osteoarthritis. Spheroids derived from ADSCs are a viable treatment option with enhanced anti-inflammatory effects and tissue repair capabilities. Objective: SphereRing^® is a rotating donut-shaped tube that efficiently produces large quantities of spheroids. However, accurately measuring spheroid size for spheroid quality assessment is challenging. This study aimed to develop an automated method for measuring spheroid size using deep learning through the ChatGPT Data Analyst for image recognition and processing. Method: The area, perimeter, and circularity of spheroids generated with the SphereRing system were analyzed using ChatGPT Data Analyst and ImageJ. Measurement accuracy was validated using Bland–Altman analysis and scatter plot correlation coefficients. Results: ChatGPT Data Analyst was consistent with ImageJ for all parameters. Bland–Altman plots demonstrated strong agreement; most data points were within the 95% limits. Conclusion: The ChatGPT Data Analyst provides a reliable and efficient alternative for assessing spheroid quality. This method reduces human error and improves reproducibility to enhance spheroid quality control. Thus, this method has potential applications in regenerative medicine.