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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| Veröffentlicht in: | Skin research and technology 2024-09, Vol.30 (9), p.e70016-n/a |
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| creator | Gharibshahian, Maliheh Torkashvand, Mohammad Bavisi, Mahya Aldaghi, Niloofar Alizadeh, Akram |
| description | 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). |
| doi_str_mv | 10.1111/srt.70016 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11348508</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3097495699</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3346-c8ed105d6fde9b395f851b2a07bd92e2793af805eb2582bf8d1bc1dbfe583f7c3</originalsourceid><addsrcrecordid>eNp1kU1r3DAQhkVoSbZJD_kDxdBLe3AysixZOoUQ-gWBQpJCb0KWRo6CV04le0v-fbXdNLSB6iLEPDyamZeQYwontJzTnOaTDoCKPbKiAqAG2YoXZAUKVN3x5vsBeZXzHQBwRdk-OWCKSiUlrAheocU4V8ZtTLSYqxArk-bggw1mLK8ZxzEMWyTPycw4hAL5KVVzyHnBCuMQImIKcahMdFXCAmMhwwarNbriiXhEXnozZnz9eB-Sbx8_3Fx8ri-_fvpycX5ZW8ZaUVuJjgJ3wjtUPVPcS077xkDXO9Vg0ylmvASOfcNl03vpaG-p6z1yyXxn2SE523nvl778vZ0smVHfp7A26UFPJuh_KzHc6mHaaEpZKznIYnj3aEjTjwXzrNch27IDE3FasmagulZxoVRB3z5D76YlxTKfZhSUEKIFUaj3O8qmKeeE_qkbCnqbni7p6d_pFfbN3-0_kX_iKsDpDvgZRnz4v0lfX93slL8Ac7indg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3109666406</pqid></control><display><type>article</type><title>Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine</title><source>PubMed (Medline)</source><source>MEDLINE</source><source>Wiley Online Library</source><source>Wiley-Blackwell Full Collection</source><source>EZB Electronic Journals Library</source><creator>Gharibshahian, Maliheh ; Torkashvand, Mohammad ; Bavisi, Mahya ; Aldaghi, Niloofar ; Alizadeh, Akram</creator><creatorcontrib>Gharibshahian, Maliheh ; Torkashvand, Mohammad ; Bavisi, Mahya ; Aldaghi, Niloofar ; Alizadeh, Akram</creatorcontrib><description>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).</description><identifier>ISSN: 0909-752X</identifier><identifier>ISSN: 1600-0846</identifier><identifier>EISSN: 1600-0846</identifier><identifier>DOI: 10.1111/srt.70016</identifier><identifier>PMID: 39189880</identifier><language>eng</language><publisher>England: John Wiley & Sons, Inc</publisher><subject>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</subject><ispartof>Skin research and technology, 2024-09, Vol.30 (9), p.e70016-n/a</ispartof><rights>2024 The Author(s). published by John Wiley & Sons Ltd.</rights><rights>2024 The Author(s). Skin Research and Technology published by John Wiley & Sons Ltd.</rights><rights>2024. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3346-c8ed105d6fde9b395f851b2a07bd92e2793af805eb2582bf8d1bc1dbfe583f7c3</cites><orcidid>0000-0002-4346-0969</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11348508/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11348508/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,1411,11541,27901,27902,45550,45551,46027,46451,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39189880$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gharibshahian, Maliheh</creatorcontrib><creatorcontrib>Torkashvand, Mohammad</creatorcontrib><creatorcontrib>Bavisi, Mahya</creatorcontrib><creatorcontrib>Aldaghi, Niloofar</creatorcontrib><creatorcontrib>Alizadeh, Akram</creatorcontrib><title>Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine</title><title>Skin research and technology</title><addtitle>Skin Res Technol</addtitle><description>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).</description><subject>Artificial Intelligence</subject><subject>biomaterials</subject><subject>Biomedical engineering</subject><subject>Computers</subject><subject>Cost analysis</subject><subject>Deep learning</subject><subject>Fabrication</subject><subject>Humans</subject><subject>Image analysis</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Imaging techniques</subject><subject>Invited Review</subject><subject>Learning algorithms</subject><subject>Localization</subject><subject>Machine Learning</subject><subject>Manufacturing</subject><subject>Medical imaging</subject><subject>Organs</subject><subject>Regenerative medicine</subject><subject>Regenerative Medicine - methods</subject><subject>Scaffolds</subject><subject>Software</subject><subject>Tissue engineering</subject><subject>Tissue Engineering - methods</subject><subject>Tissue Scaffolds</subject><issn>0909-752X</issn><issn>1600-0846</issn><issn>1600-0846</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>EIF</sourceid><recordid>eNp1kU1r3DAQhkVoSbZJD_kDxdBLe3AysixZOoUQ-gWBQpJCb0KWRo6CV04le0v-fbXdNLSB6iLEPDyamZeQYwontJzTnOaTDoCKPbKiAqAG2YoXZAUKVN3x5vsBeZXzHQBwRdk-OWCKSiUlrAheocU4V8ZtTLSYqxArk-bggw1mLK8ZxzEMWyTPycw4hAL5KVVzyHnBCuMQImIKcahMdFXCAmMhwwarNbriiXhEXnozZnz9eB-Sbx8_3Fx8ri-_fvpycX5ZW8ZaUVuJjgJ3wjtUPVPcS077xkDXO9Vg0ylmvASOfcNl03vpaG-p6z1yyXxn2SE523nvl778vZ0smVHfp7A26UFPJuh_KzHc6mHaaEpZKznIYnj3aEjTjwXzrNch27IDE3FasmagulZxoVRB3z5D76YlxTKfZhSUEKIFUaj3O8qmKeeE_qkbCnqbni7p6d_pFfbN3-0_kX_iKsDpDvgZRnz4v0lfX93slL8Ac7indg</recordid><startdate>202409</startdate><enddate>202409</enddate><creator>Gharibshahian, Maliheh</creator><creator>Torkashvand, Mohammad</creator><creator>Bavisi, Mahya</creator><creator>Aldaghi, Niloofar</creator><creator>Alizadeh, Akram</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4346-0969</orcidid></search><sort><creationdate>202409</creationdate><title>Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine</title><author>Gharibshahian, Maliheh ; Torkashvand, Mohammad ; Bavisi, Mahya ; Aldaghi, Niloofar ; Alizadeh, Akram</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3346-c8ed105d6fde9b395f851b2a07bd92e2793af805eb2582bf8d1bc1dbfe583f7c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Artificial Intelligence</topic><topic>biomaterials</topic><topic>Biomedical engineering</topic><topic>Computers</topic><topic>Cost analysis</topic><topic>Deep learning</topic><topic>Fabrication</topic><topic>Humans</topic><topic>Image analysis</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Imaging techniques</topic><topic>Invited Review</topic><topic>Learning algorithms</topic><topic>Localization</topic><topic>Machine Learning</topic><topic>Manufacturing</topic><topic>Medical imaging</topic><topic>Organs</topic><topic>Regenerative medicine</topic><topic>Regenerative Medicine - methods</topic><topic>Scaffolds</topic><topic>Software</topic><topic>Tissue engineering</topic><topic>Tissue Engineering - methods</topic><topic>Tissue Scaffolds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gharibshahian, Maliheh</creatorcontrib><creatorcontrib>Torkashvand, Mohammad</creatorcontrib><creatorcontrib>Bavisi, Mahya</creatorcontrib><creatorcontrib>Aldaghi, Niloofar</creatorcontrib><creatorcontrib>Alizadeh, Akram</creatorcontrib><collection>Wiley Online Library</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Skin research and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gharibshahian, Maliheh</au><au>Torkashvand, Mohammad</au><au>Bavisi, Mahya</au><au>Aldaghi, Niloofar</au><au>Alizadeh, Akram</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine</atitle><jtitle>Skin research and technology</jtitle><addtitle>Skin Res Technol</addtitle><date>2024-09</date><risdate>2024</risdate><volume>30</volume><issue>9</issue><spage>e70016</spage><epage>n/a</epage><pages>e70016-n/a</pages><issn>0909-752X</issn><issn>1600-0846</issn><eissn>1600-0846</eissn><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).</abstract><cop>England</cop><pub>John Wiley & Sons, Inc</pub><pmid>39189880</pmid><doi>10.1111/srt.70016</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-4346-0969</orcidid><oa>free_for_read</oa></addata></record> |
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| 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 |
| title | Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine |
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