AI-organoid integrated systems for biomedical studies and applications.
Sudhiksha MaramrajuAndrew KowalczewskiAnirudh KazaXiyuan LiuJathin Pranav SingarajuMark V AlbertZhen MaHuaxiao YangPublished in: Bioengineering & translational medicine (2024)
In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.
Keyphrases
- artificial intelligence
- deep learning
- big data
- machine learning
- label free
- stem cells
- drug discovery
- quality control
- decision making
- induced pluripotent stem cells
- high resolution
- endothelial cells
- oxidative stress
- convolutional neural network
- emergency department
- risk assessment
- electronic health record
- rna seq
- drug induced
- low cost
- solid state