A literature review: machine learning-based stem cell investigation.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Regenerative Medicine

Background:

• Stem cells (SC) are vital for human organs and clinical applications.
• Accurate SC classification and prediction for therapeutics remain challenging.
• Machine learning (ML) offers effective solutions for SC-based diagnosis and disease identification.

Purpose of the Study:

• To review ML applications in stem cell (SC) investigation for clinical solutions.
• To assess the effectiveness of ML techniques in SC observation from clinical perspectives.
• To identify current challenges and future directions for ML in SC research.

Main Methods:

• A systematic review of 15 articles from five major scientific databases.
• Keywords focused on ML, stem cells, and clinical applications.
• Data extraction and summarization across ten key aspects, including ML classifiers and performance.

Main Results:

• Most studies utilized Convolutional Neural Networks and Support Vector Machines for disease identification.
• Key limitations include narrow focus, small datasets, and inadequate validation.
• No single ML technique was definitively optimal for SC classification or prediction.

Conclusions:

• Further research is needed to develop and refine ML solutions for SC analysis.
• Expanding datasets and employing advanced evaluation processes are crucial.
• Integrating diverse ML techniques can enhance SC-based clinical applications.