AI-enabled organoids: Construction, analysis, and application

被引:77
作者
Bai, Long
Wu, Yan [1 ,2 ]
Li, Guangfeng [2 ,3 ,5 ]
Zhang, Wencai [4 ]
Zhang, Hao [1 ,2 ,3 ]
Su, Jiacan [1 ,2 ,3 ]
机构
[1] Shanghai Jiao Tong Univ, Xinhua Hosp, Sch Med, Shanghai 200092, Peoples R China
[2] Shanghai Univ, Inst Translat Med, Organoid Res Ctr, Shanghai 200444, Peoples R China
[3] Shanghai Univ, Natl Ctr Translat Med Shanghai SHU Branch, Shanghai 200444, Peoples R China
[4] Jinan Univ, Affiliated Hosp 1, Dept Orthoped, Guangzhou 510632, Peoples R China
[5] Shanghai Zhongye Hosp, Dept Orthoped, Shanghai 201941, Peoples R China
基金
中国国家自然科学基金;
关键词
Organoids; Artificial intelligence; Construction strategy; Data analysis; Preclinical evaluation and application; PLURIPOTENT STEM-CELLS; GRAPH NEURAL-NETWORKS; ARTIFICIAL-INTELLIGENCE; CEREBRAL ORGANOIDS; LOGISTIC-REGRESSION; OMICS DATA; DISEASE; GENERATION; MODEL; HYDROGEL;
D O I
10.1016/j.bioactmat.2023.09.005
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.
引用
收藏
页码:525 / 548
页数:24
相关论文
共 176 条
[1]   Establishment of patient-derived organoid models of lower-grade glioma [J].
Abdullah, Kalil G. ;
Bird, Cylaina E. ;
Buehler, Joseph D. ;
Gattie, Lauren C. ;
Savani, Milan R. ;
Sternisha, Alex C. ;
Xiao, Yi ;
Levitt, Michael M. ;
Hicks, William H. ;
Li, Wenhao ;
Ramirez, Denise M. O. ;
Patel, Toral ;
Garzon-Muvdi, Tomas ;
Barnett, Samuel ;
Zhang, Gao ;
Ashley, David M. ;
Hatanpaa, Kimmo J. ;
Richardson, Timothy E. ;
McBrayer, Samuel K. .
NEURO-ONCOLOGY, 2022, 24 (04) :612-623
[2]   From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where [J].
Ahmed, Imran ;
Jeon, Gwanggil ;
Piccialli, Francesco .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (08) :5031-5042
[3]   Predicting reaction performance in C-N cross-coupling using machine learning [J].
Ahneman, Derek T. ;
Estrada, Jesus G. ;
Lin, Shishi ;
Dreher, Spencer D. ;
Doyle, Abigail G. .
SCIENCE, 2018, 360 (6385) :186-190
[4]   Multiscale 3D phenotyping of human cerebral organoids [J].
Albanese, Alexandre ;
Swaney, Justin M. ;
Yun, Dae Hee ;
Evans, Nicholas B. ;
Antonucci, Jenna M. ;
Velasco, Silvia ;
Sohn, Chang Ho ;
Arlotta, Paola ;
Gehrke, Lee ;
Chung, Kwanghun .
SCIENTIFIC REPORTS, 2020, 10 (01)
[5]  
Aleman J, 2016, TISSUE ENG PT A, V22, pS65
[6]  
Alghodhaifi H, 2019, PROC NAECON IEEE NAT, P374, DOI [10.1109/NAECON46414.2019.9057822, 10.1109/naecon46414.2019.9057822]
[7]   A comparative dimensionality reduction study in telecom customer segmentation using deep learning and PCA [J].
Alkhayrat, Maha ;
Aljnidi, Mohamad ;
Aljoumaa, Kadan .
JOURNAL OF BIG DATA, 2020, 7 (01)
[8]   Mechanism and performance relevance of nanomorphogenesis in polyamide films revealed by quantitative 3D imaging and machine learning [J].
An, Hyosung ;
Smith, John W. ;
Ji, Bingqiang ;
Cotty, Stephen ;
Zhou, Shan ;
Yao, Lehan ;
Kalutantirige, Falon C. ;
Chen, Wenxiang ;
Ou, Zihao ;
Su, Xiao ;
Feng, Jie ;
Chen, Qian .
SCIENCE ADVANCES, 2022, 8 (08)
[9]   Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review [J].
Antonopoulos, Ioannis ;
Robu, Valentin ;
Couraud, Benoit ;
Kirli, Desen ;
Norbu, Sonam ;
Kiprakis, Aristides ;
Flynn, David ;
Elizondo-Gonzalez, Sergio ;
Wattam, Steve .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2020, 130 (130)
[10]   Prostate Cancer Localization With Multispectral MRI Using Cost-Sensitive Support Vector Machines and Conditional Random Fields [J].
Artan, Yusuf ;
Haider, Masoom A. ;
Langer, Deanna L. ;
van der Kwast, Theodorus H. ;
Evans, Andrew J. ;
Yang, Yongyi ;
Wernick, Miles N. ;
Trachtenberg, John ;
Yetik, Imam Samil .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2010, 19 (09) :2444-2455