Xuan Du, Zaozao Chen, Qiwei Li, Sheng Yang, Lincao Jiang, Yi Yang, Yanhui Li, Zhongze Gu
{"title":"Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence.","authors":"Xuan Du, Zaozao Chen, Qiwei Li, Sheng Yang, Lincao Jiang, Yi Yang, Yanhui Li, Zhongze Gu","doi":"10.1007/s42242-022-00226-y","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p><p><strong>Graphic abstract: </strong></p>","PeriodicalId":48627,"journal":{"name":"Bio-Design and Manufacturing","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9867835/pdf/","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bio-Design and Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42242-022-00226-y","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 4
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.
期刊介绍：
Bio-Design and Manufacturing reports new research, new technology and new applications in the field of biomanufacturing, especially 3D bioprinting. Topics of Bio-Design and Manufacturing cover tissue engineering, regenerative medicine, mechanical devices from the perspectives of materials, biology, medicine and mechanical engineering, with a focus on manufacturing science and technology to fulfil the requirement of bio-design.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
