Level D· Scientific groundwork from lab and animal studiesLaboratory StudyPubMed

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

Sakamoto T., Koma H., Kuwano A., Horie T., Fuku A., Kitajima H.

Laboratory Study on Osteoarthritis, Chronic Inflammation, published in Biotechniques (2025) — summary generated from the PubMed abstract.

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Level D· Scientific groundwork from lab and animal studiesEvidence level of this study

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

  • Level A · Stronger Clinical Evidence
  • Level B · Emerging clinical evidence with positive signals
  • Level C · Early human research exploring benefits
  • Level D · Scientific groundwork from lab and animal studies
  • Emerging · Emerging topic under active research
Read the A–D evidence level guide

This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.

Study type
Laboratory Study
Journal
Biotechniques (2025)
Country
England
Reported sample size
—
Source database
PubMed
PMID
40264428
DOI
10.1080/07366205.2025.2493489

Abstract (original English)

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 quanti

What this study does not prove

  • • This study does not prove SVF is an approved treatment or a replacement for standard care.
  • • This is preclinical work; animal or laboratory results cannot be applied to humans.

Evidence level

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

How we grade evidence
HumansSpheroids, CellularCell- and Tissue-Based TherapyAdipose TissueImage Processing, Computer-AssistedDeep LearningStem CellsReproducibility of ResultsCells, CulturedGenerative Artificial Intelligence

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