Objective:

This scoping review aims to map and identify the available artificial intelligence-based imaging analysis of different stem cells types and characterization of stem cells differentiation and transdifferentiation pathways.

Introduction:

Stem cells are cells that can transform into more specialized cells and include pluripotent, embryonic, and adult subtypes. They are currently receiving huge attention due to the advancing technology of artificial intelligence(AI), a field that has found its way into many disciplines and reaching clinical settings. AI adopts multiple algorithms such as machine learning (ML), and the more useful deep learning (DL) as it provides convolutional neural networks (CNNs) capable of characterizing various stem cell differentiation pathways and classifying images based on their morphology, textures, and physical features thus allowing for future clinical utilization in regenerative medicine.

Methods:

Five different electronic databases (PubMed, Medline, Web of Science, Cochrane, and Scopus) will be searched, based on a specific searching strategy, for published studies investigating the artificial intelligence-based imaging analysis technique on various types of stem cells. Two independent reviewers will be screening the titles and abstracts of the collected studies, stored in Zotero 5.0, against the inclusion criteria; all potential studies will be subjected to a second examination for the full text before the final decision. Afterward, data will be extracted from the included articles and presented in a table.