TY - JOUR
T1 - AI-organoid integrated systems for biomedical studies and applications
AU - Maramraju, Sudhiksha
AU - Kowalczewski, Andrew
AU - Kaza, Anirudh
AU - Liu, Xiyuan
AU - Singaraju, Jathin Pranav
AU - Albert, Mark V.
AU - Ma, Zhen
AU - Yang, Huaxiao
N1 - Publisher Copyright:
© 2024 The Authors. Bioengineering & Translational Medicine published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.
PY - 2024/3
Y1 - 2024/3
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - deep learning
KW - disease modeling
KW - drug evaluation
KW - human pluripotent stem cells (hPSCs)
KW - machine learning
KW - organoid
KW - regenerative medicine
UR - http://www.scopus.com/inward/record.url?scp=85182853170&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182853170&partnerID=8YFLogxK
U2 - 10.1002/btm2.10641
DO - 10.1002/btm2.10641
M3 - Review article
AN - SCOPUS:85182853170
SN - 2380-6761
VL - 9
JO - Bioengineering and Translational Medicine
JF - Bioengineering and Translational Medicine
IS - 2
M1 - e10641
ER -