AI-organoid integrated systems for biomedical studies and applications

Sudhiksha Maramraju, Andrew Kowalczewski, Anirudh Kaza, Xiyuan Liu, Jathin Pranav Singaraju, Mark V. Albert, Zhen Ma, Huaxiao Yang

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere10641
JournalBioengineering and Translational Medicine
Volume9
Issue number2
DOIs
StatePublished - Mar 2024

Keywords

  • artificial intelligence
  • deep learning
  • disease modeling
  • drug evaluation
  • human pluripotent stem cells (hPSCs)
  • machine learning
  • organoid
  • regenerative medicine

ASJC Scopus subject areas

  • Biotechnology
  • Biomedical Engineering
  • Pharmaceutical Science

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