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

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. 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. 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. ChatGPT Data Analyst was consistent with ImageJ for all parameters. Bland–Altman plots demonstrated strong agreement; most data points were within the 95% limits. 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. We developed an automated spheroid assessment method using ChatGPT Data Analyst to precisely quantify spheroid area, perimeter, and circularity. Microscopic images were subjected to grayscale conversion, binarization, and contour detection. The results were compared with manual ImageJ measurements to validate the method. The method exhibited high accuracy, providing a user-friendly and efficient alternative for spheroid assessment in regenerative medicine research. We established an automated method for the assessing spheroid size and morphology using ChatGPT Data Analyst.The method using ChatGPT Data Analyst exhibited high agreement with manual measurements performed using ImageJ for quantifying the spheroid area, perimeter, and circularity.Bland–Altman analysis demonstrated high reliability and agreement in measurements.Automated analysis using ChatGPT Data Analyst substantially reduces human error and labor-intensive manual processing required for spheroid analysis.This approach is a practical and accessible alternative for researchers and clinicians working on spheroid-based cell therapies. We established an automated method for the assessing spheroid size and morphology using ChatGPT Data Analyst. The method using ChatGPT Data Analyst exhibited high agreement with manual measurements performed using ImageJ for quantifying the spheroid area, perimeter, and circularity. Bland–Altman analysis demonstrated high reliability and agreement in measurements. Automated analysis using ChatGPT Data Analyst substantially reduces human error and labor-intensive manual processing required for spheroid analysis. This approach is a practical and accessible alternative for researchers and clinicians working on spheroid-based cell therapies.