Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine

Background Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, ce...

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Published in:Skin research and technology Vol. 30; no. 9; pp. e70016 - n/a
Main Authors: Gharibshahian, Maliheh, Torkashvand, Mohammad, Bavisi, Mahya, Aldaghi, Niloofar, Alizadeh, Akram
Format: Journal Article
Language:English
Published: England John Wiley & Sons, Inc 01.09.2024
John Wiley and Sons Inc
Subjects:
Artificial Intelligence
biomaterials
Biomedical engineering
Computers
Cost analysis
Deep learning
Fabrication
Humans
Image analysis
Image processing
Image segmentation
Imaging techniques
Invited Review
Learning algorithms
Localization
Machine Learning
Manufacturing
Medical imaging
Organs
Regenerative medicine
Regenerative Medicine - methods
Scaffolds
Software
Tissue engineering
Tissue Engineering - methods
Tissue Scaffolds
tissue engineering
biomaterials
machine learning
regenerative medicine
artificial intelligence
ISSN:0909-752X, 1600-0846, 1600-0846
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Abstract Background Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role. Methods The “artificial intelligence,” “machine learning,” “tissue engineering,” “clinical evaluation,” and “scaffold” keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated. Results The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM. Conclusion The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside. Highlights The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).
AbstractList Background Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role. Methods The “artificial intelligence,” “machine learning,” “tissue engineering,” “clinical evaluation,” and “scaffold” keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated. Results The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM. Conclusion The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside. Highlights The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).
Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role.BACKGROUNDTissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role.The "artificial intelligence," "machine learning," "tissue engineering," "clinical evaluation," and "scaffold" keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated.METHODSThe "artificial intelligence," "machine learning," "tissue engineering," "clinical evaluation," and "scaffold" keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated.The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM.RESULTSThe combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM.The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside.CONCLUSIONThe findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside.The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).HIGHLIGHTSThe capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).
Background Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role. Methods The “artificial intelligence,” “machine learning,” “tissue engineering,” “clinical evaluation,” and “scaffold” keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated. Results The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM. Conclusion The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside. Highlights The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).
Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role. The "artificial intelligence," "machine learning," "tissue engineering," "clinical evaluation," and "scaffold" keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated. The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM. The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside. The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).
Author Torkashvand, Mohammad
Bavisi, Mahya
Gharibshahian, Maliheh
Alizadeh, Akram
Aldaghi, Niloofar
AuthorAffiliation 4 Department of Tissue Engineering and Applied Cell Sciences School of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran
5 Student Research Committee School of Medicine Shahroud University of Medical Sciences Shahroud Iran
1 Nervous System Stem Cells Research Center Semnan University of Medical Sciences Semnan Iran
3 College of Engineering University of Tehran Tehran Iran
2 Department of Tissue Engineering and Applied Cell Sciences School of Medicine Semnan University of Medical Sciences Semnan Iran
AuthorAffiliation_xml – name: 1 Nervous System Stem Cells Research Center Semnan University of Medical Sciences Semnan Iran
– name: 2 Department of Tissue Engineering and Applied Cell Sciences School of Medicine Semnan University of Medical Sciences Semnan Iran
– name: 3 College of Engineering University of Tehran Tehran Iran
– name: 4 Department of Tissue Engineering and Applied Cell Sciences School of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran
– name: 5 Student Research Committee School of Medicine Shahroud University of Medical Sciences Shahroud Iran
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  surname: Alizadeh
  fullname: Alizadeh, Akram
  email: alizadehbio@gmail.com
  organization: Semnan University of Medical Sciences
BackLink https://www.ncbi.nlm.nih.gov/pubmed/39189880$$D View this record in MEDLINE/PubMed
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Issue 9
Keywords tissue engineering
biomaterials
machine learning
regenerative medicine
artificial intelligence
Language English
License Attribution-NonCommercial-NoDerivs
2024 The Author(s). Skin Research and Technology published by John Wiley & Sons Ltd.
This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
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Snippet Background Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging,...
Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by...
Background Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging,...
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SubjectTerms Artificial Intelligence
biomaterials
Biomedical engineering
Computers
Cost analysis
Deep learning
Fabrication
Humans
Image analysis
Image processing
Image segmentation
Imaging techniques
Invited Review
Learning algorithms
Localization
Machine Learning
Manufacturing
Medical imaging
Organs
Regenerative medicine
Regenerative Medicine - methods
Scaffolds
Software
Tissue engineering
Tissue Engineering - methods
Tissue Scaffolds
Title Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine
URI https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fsrt.70016
https://www.ncbi.nlm.nih.gov/pubmed/39189880
https://www.proquest.com/docview/3109666406
https://www.proquest.com/docview/3097495699
https://pubmed.ncbi.nlm.nih.gov/PMC11348508
Volume 30
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