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:: Volume 11, Issue 4 (Autumn 2023) ::
Shefaye Khatam 2023, 11(4): 94-107 Back to browse issues page
Application of Artificial Intelligence in Regenerative Medicine
Hekmat Farajpour , Behnaz Banimohamad-Shotorbani , Maryam Rafiei-Baharloo , Hajie Lotfi *
Cellular and Molecular Research Center, Research Institute for prevention of Non– Communicable Disease, Qazvin University of Medical Sciences, Qazvin, Iran , lotfi.hajie@yahoo.com
Abstract:   (482 Views)
Introduction: Optimizing tissue engineering processes requires models to predict structural and functional results by identifying relationships between different parameters and analyzing diverse processes. According to related medical studies, artificial intelligence has shown significant potential for data analysis and prediction. In the current study, the articles available in Google Scholar and PubMed, which were in the field of artificial intelligence applications in regenerative medicine and tissue engineering, were selected and studied. Artificial intelligence can play an effective role in designing, determining compounds, manufacturing, predicting the characteristics of various biomaterials and scaffolds, predicting cell behaviors, replacing animal studies, controlling biological reactors. Furthermore, using artificial intelligence leads to saving time and cost, accelerating and facilitating the achievement of optimal results. Conclusion: The use of artificial intelligence, including machine learning and deep learning, in the field of regenerative medicine and tissue engineering, makes it possible to analyze all types of tabular and image data, and has shown significant potential for data analysis, optimization, and prediction.

Keywords: Regenerative Medicine, Tissue Engineering, Artificial Intelligence
Full-Text [PDF 723 kb]   (245 Downloads)    
Type of Study: Review --- Open Access, CC-BY-NC | Subject: Basic research in Neuroscience
References
1. Pearce HA, Mikos AG. Machine learning and medical devices: The next step for tissue engineering. Engineering. 2021; 7(12): 1704-6. [DOI:10.1016/j.eng.2021.05.014]
2. Mackay BS, Marshall K, Grant-Jacob JA, Kanczler J, Eason RW, Oreffo RO, et al. The future of bone regeneration: integrating AI into tissue engineering. Biomedical Physics & Engineering Express. 2021; 7(5): 052002. [DOI:10.1088/2057-1976/ac154f]
3. Mills B, Heath DJ, Grant-Jacob JA, Eason RW. Predictive capabilities for laser machining via a neural network. Optics express. 2018; 26(13): 17245-53. [DOI:10.1364/OE.26.017245]
4. Xu J, Ge H, Zhou X, Yang D. Tissue engineering scheming by artificial intelligence. The International Journal of Artificial Organs. 2005; 28(1): 74-8. [DOI:10.1177/039139880502800112]
5. Chapekar MS. Tissue engineering: challenges and opportunities. Journal of Biomedical Materials Research: An Official Journal of The Society for Biomaterials, The Japanese Society for Biomaterials, and The Australian Society for Biomaterials and the Korean Society for Biomaterials. 2000; 53(6): 617-20. https://doi.org/10.1002/1097-4636(2000)53:6<617::AID-JBM1>3.0.CO;2-C [DOI:10.1002/1097-4636(2000)53:63.0.CO;2-C]
6. Al-Kharusi G, Dunne NJ, Little S, Levingstone TJ. The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research. Bioengineering. 2022; 9(10): 561. [DOI:10.3390/bioengineering9100561]
7. HARAGUCHI Y, SHIMIZU T, YAMATO M, OKANO T. Concise Review: Cell Therapy and Tissue Engineering for Cardiovascular Disease. stem Cells TRANSLATIONALMEDICINE. 2012; 1: 136-41. [DOI:10.5966/sctm.2012-0030]
8. Pallua N, Suschek CV. Tissue Engineering. Pallua N, Suschek CV, editors: Springer.; 2001.
9. Ghaemi RV, Siang LC, Yadav VG. Improving the rate of translation of tissue engineering products. Advanced Healthcare Materials. 2019; 8(19): 1900538. [DOI:10.1002/adhm.201900538]
10. Saunders L, Ma PX. Self‐healing supramolecular hydrogels for tissue engineering applications. Macromolecular bioscience. 2019; 19(1): 1800313. [DOI:10.1002/mabi.201800313]
11. Goldfracht I, Efraim Y, Shinnawi R, Kovalev E, Huber I, Gepstein A, et al. Engineered heart tissue models from hiPSC-derived cardiomyocytes and cardiac ECM for disease modeling and drug testing applications. Acta biomaterialia. 2019; 92: 145-59. [DOI:10.1016/j.actbio.2019.05.016]
12. Heo DN, Hospodiuk M, Ozbolat IT. Synergistic interplay between human MSCs and HUVECs in 3D spheroids laden in collagen/fibrin hydrogels for bone tissue engineering. Acta biomaterialia. 2019; 95: 348-56. [DOI:10.1016/j.actbio.2019.02.046]
13. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. nature. 1986; 323(6088): 533-6. [DOI:10.1038/323533a0]
14. Kassani SH, Kassani PH. A comparative study of deep learning architectures on melanoma detection. Tissue and Cell. 2019; 58: 76-83. [DOI:10.1016/j.tice.2019.04.009]
15. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics. 2018; 19(6): 1236-46. [DOI:10.1093/bib/bbx044]
16. Jordan AM. Artificial intelligence in drug design the storm before the calm? : ACS Publications; 2018. p. 1150-2. [DOI:10.1021/acsmedchemlett.8b00500]
17. Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of artificial intelligence for computer-assisted drug discovery. Chemical reviews. 2019; 119(18): 10520-94. [DOI:10.1021/acs.chemrev.8b00728]
18. Callaway E, Else H, Kwon D, Ledford H, Mallapaty S, Maxmen A, et al. Nature's 10: ten people who helped shape science in 2021. Nature. 2021 Dec;600(7890):591-604. [DOI:10.1038/d41586-021-03621-0]
19. Toosi A, Bottino AG, Saboury B, Siegel E, Rahmim A. A brief history of AI: how to prevent another winter (a critical review). PET clinics. 2021; 16(4): 449-69. [DOI:10.1016/j.cpet.2021.07.001]
20. Bell J. What is machine learning? Machine Learning and the City: Applications in Architecture and Urban Design. 2022: 207-16. [DOI:10.1002/9781119815075.ch18]
21. El Naqa I, Murphy MJ. What is machine learning? machine learning in radiation oncology: Springer; 2015. p. 3-11. [DOI:10.1007/978-3-319-18305-3_1]
22. Lotfi H, Pirmoradi S, Mahmoudi R, Teshnehlab M, Sheervalilou R, Aval SF, et al. Machine learning as new promising technique for selection of significant features in obese women with type 2 diabetes. Hormone molecular biology and clinical investigation. 2020; 41(1). [DOI:10.1515/hmbci-2019-0019]
23. Alpaydin E. Introduction to machine learning: MIT press 2020.
24. Park C, Took CC, Seong J-K. Machine learning in biomedical engineering. Biomedical Engineering Letters. 2018; 8(1): 1-3. [DOI:10.1007/s13534-018-0058-3]
25. Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B. Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences. 2019; 116(44): 22071-80. [DOI:10.1073/pnas.1900654116]
26. Talabis M, McPherson R, Miyamoto I, Martin J. Information security analytics: finding security insights, patterns, and anomalies in big data: Syngress; 2014. [DOI:10.1016/B978-0-12-800207-0.00001-0]
27. de Ridder D, de Ridder J, Reinders MJ. Pattern recognition in bioinformatics. Briefings in bioinformatics. 2013; 14(5): 633-47. [DOI:10.1093/bib/bbt020]
28. Balasubramanyam S. New technologies and environments. IFPUG Guid IT Softw Meas. 2012; 385: 28. [DOI:10.1201/b11884-28]
29. Shinde PP, Shah S, editors. A review of machine learning and deep learning applications. 2018 Fourth international conference on computing communication control and automation (ICCUBEA); 2018: IEEE. [DOI:10.1109/ICCUBEA.2018.8697857]
30. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nature medicine. 2019; 25(1): 24-9. [DOI:10.1038/s41591-018-0316-z]
31. Menden K, Marouf M, Oller S, Dalmia A, Magruder DS, Kloiber K, et al. Deep learning-based cell composition analysis from tissue expression profiles. Science advances. 2020; 6(30): eaba 2619. [DOI:10.1126/sciadv.aba2619]
32. Guo JL, Januszyk M, Longaker MT. Machine Learning in Tissue Engineering. Tissue Engineering Part A. 2022.
33. Guo JL, Januszyk M, Longaker MT. Machine learning in tissue engineering. Tissue Engineering Part A. 2023; 29(1-2): 2-19. [DOI:10.1089/ten.tea.2022.0128]
34. Bermejillo Barrera MD, Franco-Martínez F, Díaz Lantada A. Artificial intelligence aided design of tissue engineering scaffolds employing virtual tomography and 3D Convolutional Neural Networks. Materials. 2021; 14(18): 5278. [DOI:10.3390/ma14185278]
35. Langer, R. , Vacanti J. (1993) Tissue engineering. Science, 260, 920-926. http://dx.doi.org/10.1126/science.8493529 [DOI:10.1126/science.8493529]
36. Shi J, Votruba AR, Farokhzad OC, Langer R. Nanotechnology in drug delivery and tissue engineering: from discovery to applications. Nano letters. 2010; 10(9): 3223-30. [DOI:10.1021/nl102184c]
37. Erdemir A, Guess TM, Halloran J, Tadepalli SC, Morrison TM. Considerations for reporting finite element analysis studies in biomechanics. Journal of biomechanics. 2012; 45(4): 625-33. [DOI:10.1016/j.jbiomech.2011.11.038]
38. Rahmani S, Jarrahi A, Saed B, Navidbakhsh M, Farjpour H, Alizadeh M. Three-dimensional modeling of Marfan syndrome with elastic and hyperelastic materials assumptions using fluid-structure interaction. Bio-medical materials and engineering. 2019; 30(3): 255-66. [DOI:10.3233/BME-191049]
39. Farajpour H, Jamshidi N. Effects of different angles of the traction table on lumbar spine ligaments: A finite element study. Clinics in Orthopedic Surgery. 2017; 9(4): 480-8. [DOI:10.4055/cios.2017.9.4.480]
40. Boccaccio A, Ballini A, Pappalettere C, Tullo D, Cantore S, Desiate A. Finite element method (FEM), mechanobiology and biomimetic scaffolds in bone tissue engineering. International journal of biological sciences. 2011; 7(1): 112-32. [DOI:10.7150/ijbs.7.112]
41. Egan PF, Gonella VC, Engensperger M, Ferguson SJ, Shea K. Computationally designed lattices with tuned properties for tissue engineering using 3D printing. PloS one. 2017; 12(8): e0182902. [DOI:10.1371/journal.pone.0182902]
42. Safiaghdam H, Nokhbatolfoghahaei H, Farzad‐Mohajeri S, Dehghan MM, Farajpour H, Aminianfar H, et al. 3D‐printed MgO nanoparticle loaded polycaprolactone β‐tricalcium phosphate composite scaffold for bone tissue engineering applications: In‐vitro and in‐vivo evaluation. Journal of Biomedical Materials Research Part A. 2022. [DOI:10.1002/jbm.a.37465]
43. Nokhbatolfoghahaei H, Bastami F, Farzad‐Mohajeri S, Rezai Rad M, Dehghan MM, Bohlouli M, et al. Prefabrication technique by preserving a muscular pedicle from masseter muscle as an in vivo bioreactor for reconstruction of mandibular critical‐sized bone defects in canine models. Journal of Biomedical Materials Research Part B: Applied Biomaterials. 2022; 110(7): 1675-86. [DOI:10.1002/jbm.b.35028]
44. Khojasteh A, Safiaghdam H, Farajpour H. Pedicled segmental rotation techniques for posterior mandible augmentation: a preliminary study. International journal of oral and maxillofacial surgery. 2019; 48(12): 1584-93. [DOI:10.1016/j.ijom.2019.04.009]
45. Jiao P, Alavi AH. Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends. International Materials Reviews. 2021; 66(6): 365-93. [DOI:10.1080/09506608.2020.1815394]
46. Bonfanti S, Guerra R, Font-Clos F, Rayneau-Kirkhope D, Zapperi S. Automatic design of mechanical metamaterial actuators. Nature communications. 2020; 11(1): 1-10. [DOI:10.1038/s41467-020-17947-2]
47. Conev A, Litsa EE, Perez MR, Diba M, Mikos AG, Kavraki LE. Machine learning-guided three-dimensional printing of tissue engineering scaffolds. Tissue Engineering Part A. 2020; 26(23-24): 1359-68. [DOI:10.1089/ten.tea.2020.0191]
48. Nasouri K. Novel estimation of morphological behavior of electrospun nanofibers with artificial intelligence system (AIS). Polymer Testing. 2018; 69: 499-507. [DOI:10.1016/j.polymertesting.2018.06.001]
49. Toscano JD, Li Z, Segura LJ, Sun H, editors. A machine learning approach to model the electrospinning process of biocompatible materials. International Manufacturing Science and Engineering Conference; 2020: American Society of Mechanical Engineers. [DOI:10.1115/MSEC2020-8362]
50. Farajpour H, Bastami F, Bohlouli M, Khojasteh A. Reconstruction of bilateral ramus-condyle unit defect using custom titanium prosthesis with preservation of both condyles. Journal of the Mechanical Behavior of Biomedical Materials. 2021; 124: 104765. [DOI:10.1016/j.jmbbm.2021.104765]
51. Kim J, McKee JA, Fontenot JJ, Jung JP. Engineering tissue fabrication with machine intelligence: generating a blueprint for regeneration. Frontiers in bioengineering and biotechnology. 2020; 7: 443. [DOI:10.3389/fbioe.2019.00443]
52. Ng WL, Chan A, Ong YS, Chua CK. Deep learning for fabrication and maturation of 3D bioprinted tissues and organs. Virtual and Physical Prototyping. 2020; 15(3): 340-58. [DOI:10.1080/17452759.2020.1771741]
53. Shotorbani BB, Alizadeh E, Salehi R, Barzegar A. Adhesion of mesenchymal stem cells to biomimetic polymers: A review. Materials Science and Engineering: C. 2017; 71: 1192-200. [DOI:10.1016/j.msec.2016.10.013]
54. Banimohamad-Shotorbani B, Rahmani Del Bakhshayesh A, Mehdipour A, Jarolmasjed S, Shafaei H. The efficiency of PCL/HAp electrospun nanofibers in bone regeneration: A review. Journal of Medical Engineering & Technology. 2021; 45(7): 511-31. [DOI:10.1080/03091902.2021.1893396]
55. Fathi-Karkan S, Banimohamad-Shotorbani B, Saghati S, Rahbarghazi R, Davaran S. A critical review of fibrous polyurethane-based vascular tissue engineering scaffolds. Journal of Biological Engineering. 2022; 16(1): 1-18. [DOI:10.1186/s13036-022-00286-9]
56. Tourlomousis F, Jia C, Karydis T, Mershin A, Wang H, Kalyon DM, et al. Machine learning metrology of cell confinement in melt electrowritten three-dimensional biomaterial substrates. Microsystems & nanoengineering. 2019; 5(1): 15. [DOI:10.1038/s41378-019-0055-4]
57. Mackay BS, Praeger M, Grant-Jacob JA, Kanczler J, Eason RW, Oreffo RO, et al. Modeling adult skeletal stem cell response to laser-machined topographies through deep learning. Tissue and Cell. 2020; 67: 101442. [DOI:10.1016/j.tice.2020.101442]
58. Mehrian M, Lambrechts T, Marechal M, Luyten FP, Papantoniou I, Geris L. Predicting in vitro human mesenchymal stromal cell expansion based on individual donor characteristics using machine learning. Cytotherapy. 2020; 22(2): 82-90. [DOI:10.1016/j.jcyt.2019.12.006]
59. Chen D, Sarkar S, Candia J, Florczyk SJ, Bodhak S, Driscoll MK, et al. Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues. Biomaterials.2016; 104: 104-18. [DOI:10.1016/j.biomaterials.2016.06.040]
60. Williams B, Löbel W, Finklea F, Halloin C, Ritzenhoff K, Manstein F, et al. Prediction of human induced pluripotent stem cell cardiac differentiation outcome by multifactorial process modeling. Frontiers in bioengineering and biotechnology. 2020; 8: 851. [DOI:10.3389/fbioe.2020.00851]
61. Fan K, Zhang S, Zhang Y, Lu J, Holcombe M, Zhang X. A machine learning assisted, label-free, non-invasive approach for somatic reprogramming in induced pluripotent stem cell colony formation detection and prediction. Scientific reports. 2017; 7(1): 13496. [DOI:10.1038/s41598-017-13680-x]
62. Waisman A, La Greca A, Möbbs AM, Scarafía MA, Velazque NLS, Neiman G, et al. Deep learning neural networks highly predict very early onset of pluripotent stem cell differentiation. Stem cell reports. 2019; 12(4): 845-59. [DOI:10.1016/j.stemcr.2019.02.004]
63. Arora M, Cutler CS, Jagasia MH, Pidala J, Chai X, Martin PJ, et al. Late acute and chronic graft-versus-host disease after allogeneic hematopoietic cell transplantation. Biology of Blood and Marrow Transplantation. 2016; 22(3): 449-55. [DOI:10.1016/j.bbmt.2015.10.018]
64. Gandelman JS, Byrne MT, Mistry AM, Polikowsky HG, Diggins KE, Chen H, et al. Machine learning reveals chronic graft-versus-host disease phenotypes and stratifies survival after stem cell transplant for hematologic malignancies. Haematologica. 2019; 104(1): 189. [DOI:10.3324/haematol.2018.193441]
65. Marklein RA, Klinker MW, Drake KA, Polikowsky HG, Lessey-Morillon EC, Bauer SR. Morphological profiling using machine learning reveals emergent subpopulations of interferon-γ-stimulated mesenchymal stromal cells that predict immunosuppression. Cytotherapy. 2019 Jan;21(1):17-31. doi: 10.1016/j.jcyt.2018.10.008. Epub 2018 Nov 28. [DOI:10.1016/j.jcyt.2018.10.008]
66. Jaccard N, Macown RJ, Super A, Griffin LD, Veraitch FS, Szita N. Automated and online characterization of adherent cell culture growth in a microfabricated bioreactor. Journal of Laboratory Automation. 2014; 19(5): 437-43. [DOI:10.1177/2211068214529288]
67. Hulsart-Billström G, Dawson J, Hofmann S, Müller R, Stoddart M, Alini M, et al. A surprisingly poor correlation between in vitro and in vivo testing of biomaterials for bone regeneration: results of a multicentre analysis. 2016. [DOI:10.22203/eCM.v031a20]
68. Singh AV, Ansari MHD, Rosenkranz D, Maharjan RS, Kriegel FL, Gandhi K, et al. Artificial intelligence and machine learning in computational nanotoxicology: unlocking and empowering nanomedicine. Advanced Healthcare Materials. 2020; 9(17): 1901862. [DOI:10.1002/adhm.201901862]
69. Strobel HA, Schultz A, Moss SM, Eli R, Hoying JB. Quantifying vascular density in tissue engineered constructs using machine learning. Frontiers in Physiology. 2021; 12: 650714. [DOI:10.3389/fphys.2021.650714]
70. Power L, Acevedo L, Yamashita R, Rubin D, Martin I, Barbero A. Deep learning enables the automation of grading histological tissue engineered cartilage images for quality control standardization. Osteoarthritis and Cartilage. 2021; 29(3): 433-43. [DOI:10.1016/j.joca.2020.12.018]
71. Galan EA, Zhao H, Wang X, Dai Q, Huck WT, Ma S. Intelligent microfluidics: The convergence of machine learning and microfluidics in materials science and biomedicine. Matter. 2020; 3(6): 1893-922. [DOI:10.1016/j.matt.2020.08.034]
72. Wu H, Uchimura K, Donnelly EL, Kirita Y, Morris SA, Humphreys BD. Comparative analysis and refinement of human PSC-derived kidney organoid differentiation with single-cell transcriptomics. Cell stem cell. 2018; 23(6): 869-81. e8. [DOI:10.1016/j.stem.2018.10.010]
73. Monzel AS, Hemmer K, Kaoma T, Smits LM, Bolognin S, Lucarelli P, et al. Machine learning-assisted neurotoxicity prediction in human midbrain organoids. Parkinsonism & Related Disorders. 2020; 75: 105-9. [DOI:10.1016/j.parkreldis.2020.05.011]
74. Liu Q, Fang L, Yu G, Wang D, Xiao C-L, Wang K. Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data. Nature communications. 2019; 10(1): 2449. [DOI:10.1038/s41467-019-10168-2]
75. Xu J, Ge H, Zhou X, Yan J, Chi Q, Zhang Z. Prediction of vascular tissue engineering results with artificial neural networks. Journal of Biomedical Informatics. 2005; 38(6): 417-21. [DOI:10.1016/j.jbi.2005.03.002]
76. Zeraati M, Pourmohamad R, Baghchi B, Chauhan NPS, Sargazi G. Optimization and predictive modelling for the diameter of nylon-6, 6 nanofibers via electrospinning for coronavirus face masks. Journal of Saudi Chemical Society. 2021; 25(11): 101348. [DOI:10.1016/j.jscs.2021.101348]
77. Allen B. The role of the FDA in ensuring the safety and efficacy of artificial intelligence software and devices. Journal of the American College of Radiology. 2019; 16(2): 208-10. [DOI:10.1016/j.jacr.2018.09.007]



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Farajpour H, Banimohamad-Shotorbani B, Rafiei-Baharloo M, Lotfi H. Application of Artificial Intelligence in Regenerative Medicine. Shefaye Khatam 2023; 11 (4) :94-107
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مجله علوم اعصاب شفای خاتم The Neuroscience Journal of Shefaye Khatam
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