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- W4317494611 abstract "In modern terminology, organoids refer to cells that grow in a specific three-dimensional (3D) environment in vitro, sharing similar structures with their source organs or tissues. Observing the morphology or growth characteristics of organoids through a microscope is a commonly used method of organoid analysis. However, it is difficult, time-consuming, and inaccurate to screen and analyze organoids only manually, a problem which cannot be easily solved with traditional technology. Artificial intelligence (AI) technology has proven to be effective in many biological and medical research fields, especially in the analysis of single-cell or hematoxylin/eosin stained tissue slices. When used to analyze organoids, AI should also provide more efficient, quantitative, accurate, and fast solutions. In this review, we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis methods. Secondly, we will summarize the development from machine learning to deep learning and the advantages of the latter, and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and analysis. Finally, we will discuss the limitations of current AI used in organoid research, as well as opportunities and future research directions." @default.
- W4317494611 created "2023-01-20" @default.
- W4317494611 creator A5020073475 @default.
- W4317494611 creator A5027118136 @default.
- W4317494611 creator A5027799678 @default.
- W4317494611 creator A5034561583 @default.
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- W4317494611 creator A5048998215 @default.
- W4317494611 creator A5054245598 @default.
- W4317494611 creator A5059503081 @default.
- W4317494611 date "2023-01-19" @default.
- W4317494611 modified "2023-10-16" @default.
- W4317494611 title "Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence" @default.
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