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Current Pharmaceutical Biotechnology

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ISSN (Print): 1389-2010
ISSN (Online): 1873-4316

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Review Article

A Review on the Role of Artificial Intelligence in Stem Cell Therapy: An Initiative for Modern Medicines

Author(s): Pravin Shende* and Nikita P. Devlekar

Volume 22, Issue 9, 2021

Published on: 07 October, 2020

Page: [1156 - 1163] Pages: 8

DOI: 10.2174/1389201021666201007122524

Price: $65

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Abstract

Stem Cells (SCs) show a wide range of applications in the treatment of numerous diseases, including neurodegenerative diseases, diabetes, cardiovascular diseases, cancer, etc. SC related research has gained popularity owing to the unique characteristics of self-renewal and differentiation. Artificial Intelligence (AI), an emerging field of computer science and engineering, has shown potential applications in different fields like robotics, agriculture, home automation, healthcare, banking, and transportation since its invention. This review aims to describe the various applications of AI in SC biology, including understanding the behavior of SCs, recognizing individual cell type before undergoing differentiation, characterization of SCs using mathematical models and prediction of mortality risk associated with SC transplantation. This review emphasizes the role of neural networks in SC biology and further elucidates the concepts of machine learning and deep learning and their applications in SC research.

Keywords: Machine learning, deep learning, convolutional neural network, artificial neural network, induced pluripotent stem cell, mathematical models.

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[1]
Hamet, P.; Tremblay, J. Artificial intelligence in medicine. Metabolism, 2017, 69S(0), S36-S40.
[http://dx.doi.org/10.1016/j.metabol.2017.01.011] [PMID: 28126242]
[2]
Rajaraman, V. John McCarthy - father of artificial intelligence. Reson, 2014, 19, 198-207.
[3]
Ekins, S. Success stories of computer-aided design. Comp. Appl. Pharm. Res. Devel., 2006, 377-424.http://doi.wiley.com/10.1002/0470037237
[4]
Brady, M. Artificial intelligence and robotics. Artif. Intell., 1985, 26(1), 79-121.
[http://dx.doi.org/10.1016/0004-3702(85)90013-X]
[5]
Murase, H. Artificial intelligence in agriculture. Comput. Electron. Agric., 2000, 29(1-2), 1-2.
[http://dx.doi.org/10.1016/S0168-1699(00)00132-0]
[6]
Cook, D.J. How smart is your home? Science (80-)., 2012, 335(6076), 1579-1581.
[http://dx.doi.org/10.1126/science.1217640]
[7]
Jiang, F.; Jiang, Y.; Zhi, H.; Dong, Y.; Li, H.; Ma, S.; Wang, Y.; Dong, Q.; Shen, H.; Wang, Y. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc. Neurol., 2017, 2(4), 230-243.
[http://dx.doi.org/10.1136/svn-2017-000101] [PMID: 29507784]
[8]
Hengstler, M.; Enkel, E.; Duelli, S. Applied artificial intelligence and trust-The case of autonomous vehicles and medical assistance devices. Technol. Forecast. Soc. Change, 2016, 105, 105-120.
[http://dx.doi.org/10.1016/j.techfore.2015.12.014]]
[9]
Sikchi, S.S.; Sikchi, S.; Ali, M.S. Artificial intelligence in medical diagnosis. Int. J. Appl. Eng. Res., 2012, 7(11), 1539-1543.
[10]
Ardizzone, E.; Bonadonna, F.; Gaglio, S.; Marcenò, R.; Nicolini, C.; Ruggiero, C.; Sorbello, F. Artificial intelligence techniques for cancer treatment planning. Med. Inform. (Lond.), 1988, 13(3), 199-210.
[http://dx.doi.org/10.3109/14639238809010100] [PMID: 3185024]
[11]
Duch, W.; Swaminathan, K.; Meller, J. Artificial intelligence approaches for rational drug design and discovery. Curr. Pharm. Des., 2007, 13(14), 1497-1508.
[http://dx.doi.org/10.2174/138161207780765954] [PMID: 17504169]
[12]
Langen, P.A.; Katz, J.S.; Dempsey, G.; Pompano, J. Remote monitoring of high-risk patients using artificial intelligence. U.S. Patent 357,427A, 1993.
[13]
Alison, M.R.; Poulsom, R.; Forbes, S.; Wright, N.A. An introduction to stem cells. J. Pathol., 2002, 197(4), 419-423.
[14]
Shende, P.; Subedi, M. Pathophysiology, mechanisms and applications of mesenchymal stem cells for the treatment of spinal cord injury. Biomed. Pharmacother., 2017, 91, 693-706.
[http://dx.doi.org/10.1016/j.biopha.2017.04.126] [PMID: 28499241]
[15]
Kalra, K.; Tomar, P.C. Stem cell: Basics, classification and applications. Am. J. Phytomed. Clin. Ther., 2014, 2(7), 913-30.www.ajpct.org
[16]
Bongso, A.; Richards, M. History and perspective of stem cell research. Best Pract. Res. Clin. Obstet. Gynaecol., 2004, 18(6), 827-842.
[http://dx.doi.org/10.1016/j.bpobgyn.2004.09.002] [PMID: 15582541]
[17]
Hyun, I. The bioethics of stem cell research and therapy. J. Clin. Invest., 2010, 120(1), 71-75.
[http://dx.doi.org/10.1172/JCI40435] [PMID: 20051638]
[18]
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. Sci. Rep., 2017, 7(1), 13496.
[http://dx.doi.org/10.1038/s41598-017-13680-x] [PMID: 29044152]
[19]
Ilic, D.; Polak, J.M. Stem cells in regenerative medicine. Introduction. Brit. Med. Bullet., 2011, 98(1), 117-126.
[http://dx.doi.org/10.1093/bmb/ldr012]
[20]
Can, A. A concise review on the classification and nomenclature of stem cells. Turk. J. Haematol., 2008, 25(2), 57-59.
[21]
Shende, P.; Gupta, H.; Gaud, R.S. Cytotherapy using stromal cells: Current and advance multi-treatment approaches. Biomed. Pharmacother., 2018, 97(97), 38-44.
[http://dx.doi.org/10.1016/j.biopha.2017.10.127] [PMID: 29080456]
[22]
Shende, P.; Bhandarkar, S.; Prabhakar, B. Heat shock proteins and their protective roles in stem cell biology. Stem Cell Rev. Reports, 2019, 15(5), 637-651.
[http://dx.doi.org/10.1007/s12015-019-09903-5]
[23]
Smith, A. A glossary for stem-cell biology. Nature, 2006, 441, 4954.
[http://dx.doi.org/10.1038/nature04954]
[24]
Politis, M.; Lindvall, O. Clinical application of stem cell therapy in Parkinson’s disease. BMC Med., 2012, 10, 1-7.
[http://dx.doi.org/10.1186/1741-7015-10-1] [PMID: 22216957]
[25]
Segers, V.F.M.; Lee, R.T. Stem-cell therapy for cardiac disease. Nature, 2008, 451(7181), 937-942.
[http://dx.doi.org/10.1038/nature06800] [PMID: 18288183]
[26]
Tang, C.; Ang, B.T.; Pervaiz, S. Cancer stem cell: Target for anti-cancer therapy. FASEB J., 2007, 21(14), 3777-3785.
[http://dx.doi.org/10.1096/fj.07-8560rev] [PMID: 17625071]
[27]
Golas, M.M.; Sander, B. Use of human stem cells in Huntington disease modeling and translational research. Exp. Neurol., 2016, 278, 76-90.
[http://dx.doi.org/10.1016/j.expneurol.2016.01.021] [PMID: 26826449]
[28]
Karussis, D.; Karageorgiou, C.; Vaknin-dembinsky, A.; Gowda-kurkalli, B.; Gomori, J.M.; Kassis, I. Safety and immunological effects of mesenchymal stem cell transplantation in patients with multiple sclerosis and amyotrophic lateral sclerosis. Arch. Neurol., 2015, 67(10), 1187-1194.
[29]
Lindvall, O.; Kokaia, Z. Stem cells for the treatment of neurological disorders. Nature, 2006, 441(7097), 1094-1096.
[http://dx.doi.org/10.1038/nature04960] [PMID: 16810245]
[30]
Trounson, A.; Thakar, R.G.; Lomax, G.; Gibbons, D. Clinical trials for stem cell therapies. BMC Med., 2011, 9, 52.
[31]
Trounson, A.; McDonald, C. Stem cell therapies in clinical trials: Progress and challenges. Cell Stem Cell, 2015, 17(1), 11-22.
[http://dx.doi.org/10.1016/j.stem.2015.06.007] [PMID: 26140604]
[32]
Rashidi, H.H.; Tran, N.K.; Betts, E.V.; Howell, L.P.; Green, R. Artificial intelligence and machine learning in pathology: The present landscape of supervised methods. Acad. Pathol., 2019, 6, 2374289519873088.
[33]
Shouval, R.; Bondi, O.; Mishan, H.; Shimoni, A.; Unger, R.; Nagler, A. Application of machine learning algorithms for clinical predictive modeling: A data-mining approach in SCT. Bone Marrow Transplant., 2014, 49(3), 332-337.
[http://dx.doi.org/10.1038/bmt.2013.146] [PMID: 24096823]
[34]
Sathya, R.; Abraham, A. Comparison of supervised and unsupervised learning algorithms for pattern classification. Int. J. Adv. Res. Artif. Intell., 2013, 2(2), 34-38.
[http://dx.doi.org/10.14569/IJARAI.2013.020206]
[35]
Johnson, K.W.; Torres Soto, J.; Glicksberg, B.S.; Shameer, K.; Miotto, R.; Ali, M.; Ashley, E.; Dudley, J.T. Artificial intelligence in cardiology. J. Am. Coll. Cardiol., 2018, 71(23), 2668-2679.
[http://dx.doi.org/10.1016/j.jacc.2018.03.521] [PMID: 29880128]
[36]
Ghahramani, Z. Unsupervised learning. Mach. Learn., 2004, LNAI 3176, 72-112.
[37]
Hosny, A.; Parmar, C.; Quackenbush, J.; Schwartz, L.H.; Aerts, H.J.W.L. Artificial intelligence in radiology. Nat. Rev. Cancer, 2018, 18(8), 500-510.
[http://dx.doi.org/10.1038/s41568-018-0016-5] [PMID: 29777175]
[38]
Shende, P.; Rodrigues, B.; Gaud, R.S. Transplantation and alternatives to treat autoimmune diseases. Adv. Exp. Med. Biol., 2018, 1089, 59-72.
[http://dx.doi.org/10.1007/5584_2018_177] [PMID: 29516308]
[39]
Bell, J. Artificial Neural Networks; Mach Learn Hands-On Dev Tech Prof, 2014, pp. 91-116.
[40]
Doss, M.X.; Sachinidis, A. Current challenges of iPSC-based disease modeling and therapeutic implications. Cells, 2019, 8(5), 403.
[http://dx.doi.org/10.3390/cells8050403] [PMID: 31052294]
[41]
Joutsijoki, H.; Haponen, M.; Rasku, J.; Aalto-Setälä, K.; Juhola, M. Machine learning approach to automated quality identification of human induced pluripotent stem cell colony images. Comput. Math. Methods Med., 2016, 2016, 3091039.
[http://dx.doi.org/10.1155/2016/3091039]
[42]
Kavitha, M.S.; Kurita, T.; Park, S.Y.; Chien, S.I.; Bae, J.S.; Ahn, B.C. Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells. PLoS One, 2017, 12(12), e0189974.
[http://dx.doi.org/10.1371/journal.pone.0189974] [PMID: 29281701]
[43]
d’Inverno, M.; Theise, N.; Prophet, J. Mathematical modeling of stem cells: A complexity primer for the stem-cell biologist. Tissue Stem Cells: Biology and Applications, 2nd ed; Marcell Dekker Inc: New York, NY, 2006, pp. 1-16.
[44]
Orozco, L.E.W.S.; Neganova, F.I.; Shukurov, M.L.A. The recent advances in the mathematical modelling of human pluripotent stem cells. SN Appl. Sci., 2020, 2(2), 1-14.
[http://dx.doi.org/10.1007/s42452-020-2070-3]
[45]
Turner, C.; Stinchcombe, A.R.; Kohandel, M.; Sivaloganathan, S. Characterization of brain cancer stem cells: A mathematical approach. Cell Prolif., 2009, 42(4), 529-540.
[http://dx.doi.org/10.1111/j.1365-2184.2009.00619.x]
[46]
Argentati, C.; Morena, F.; Tortorella, I.; Bazzucchi, M.; Porcellati, S.; Emiliani, C.; Martino, S. Insight into mechanobiology: How stem cells feel mechanical forces and orchestrate biological functions. Int. J. Mol. Sci., 2019, 20(21), E5337.
[http://dx.doi.org/10.3390/ijms20215337] [PMID: 31717803]
[47]
Vargas-Díez, E.; García-Díez, A.; Marín, A.; Fernández-Herrera, J. Life-threatening graft-vs-host disease. Clin. Dermatol., 2005, 23(3), 285-300.
[http://dx.doi.org/10.1016/j.clindermatol.2004.06.005] [PMID: 15896544]
[48]
Alsuliman, T.; Humaidan, D.; Sliman, L. Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality? Curr. Res. Transl. Med., 2020, 2020 S2452-3186(20), 30019-2.
[http://dx.doi.org/10.1016/j.retram.2020.01.002] [PMID: 32029403]
[49]
Erkaymaz, O.; Ozer, M. Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes. Chaos Solitons Fractals, 2016, 83, 178-185.
[http://dx.doi.org/10.1016/j.chaos.2015.11.029]
[50]
Fathi Metwally, N.; Khaled AbuSharekh, E.; Abu-Naser, S. Diagnosis of hepatitis virus using artificial neural network. Int. J. Acad. Dev., 2018, 2(11), 1-7.
[51]
Thomas, M.; Chandran, A. Artificial neural network for diagnosing autism spectrum disorder. Proc. 2nd Int. Conf. Trends Electron Informat., ICOEI, 2018. 2018, 930-933.
[http://dx.doi.org/10.1109/ICOEI.2018.8553781]
[52]
Caocci, G.; Baccoli, R.; Vacca, A.; Mastronuzzi, A.; Bertaina, A.; Piras, E.; Littera, R.; Locatelli, F.; Carcassi, C.; La Nasa, G. Comparison between an artificial neural network and logistic regression in predicting acute graft-vs-host disease after unrelated donor hematopoietic stem cell transplantation in thalassemia patients. Exp. Hematol., 2010, 38(5), 426-433.
[http://dx.doi.org/10.1016/j.exphem.2010.02.012] [PMID: 20206661]
[53]
Waisman, A.; La Greca, A.; Möbbs, A.M.; Scarafía, M.A.; Santín Velazque, N.L.; Neiman, G.; Moro, L.N.; Luzzani, C.; Sevlever, G.E.; Guberman, A.S.; Miriuka, S.G. Deep learning neural networks highly predict very early onset of pluripotent stem cell differentiation. Stem Cell Reports, 2019, 12(4), 845-859.
[http://dx.doi.org/10.1016/j.stemcr.2019.02.004] [PMID: 30880077]
[54]
Chang, Y.H.; Abe, K.; Yokota, H.; Sudo, K.; Nakamura, Y.; Tsai, M.D. Human induced pluripotent stem cell region detection in bright-field microscopy images using convolutional neural networks. Biomed. Eng. Appl. Basis Commun., 2019, 31(2), 4058-4061.
[http://dx.doi.org/10.4015/S1016237219500091]
[55]
Talo, M.; Baloglu, U.B.; Yıldırım, Ö.; Rajendra, A.U. Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn. Syst. Res., 2019, 54, 176-188. [Internet].
[http://dx.doi.org/10.1016/j.cogsys.2018.12.007]
[56]
Kusumoto, D.; Yuasa, S. The application of convolutional neural network to stem cell biology. Inflamm. Regen., 2019, 39(1), 14.https://inflammregen.biomedcentral.com/articles/10.1186/s41232-019-0103-3
[http://dx.doi.org/10.1186/s41232-019-0103-3] [PMID: 31312276]
[57]
Shouval, R.; Labopin, M.; Bondi, O.; Mishan-Shamay, H.; Shimoni, A.; Ciceri, F.; Esteve, J.; Giebel, S.; Gorin, N.C.; Schmid, C.; Polge, E.; Aljurf, M.; Kroger, N.; Craddock, C.; Bacigalupo, A.; Cornelissen, J.J.; Baron, F.; Unger, R.; Nagler, A.; Mohty, M. Prediction of allogeneic hematopoietic stem-cell transplantation mortality 100 days after transplantation using a machine learning algorithm: A European group for blood and marrow transplantation acute leukemia working party retrospective data mining stud. J. Clin. Oncol., 2015, 33(28), 3144-3151.
[http://dx.doi.org/10.1200/JCO.2014.59.1339] [PMID: 26240227]
[58]
Muhsen, I.N.; ElHassan, T.; Hashmi, S.K. Artificial intelligence approaches in hematopoietic cell transplantation: A review of the current status and future directions. Turk. J. Haematol., 2018, 35(3), 152-157.
[PMID: 29880463]
[59]
Rao, R.R.; Stice, S.L. Gene expression profiling of embryonic stem cells leads to greater understanding of pluripotency and early developmental events. Biol. Reprod., 2004, 71(6), 1772-1778.
[http://dx.doi.org/10.1095/biolreprod.104.030395] [PMID: 15140800]
[60]
Concentration, S.; Kumar, S.; Blangero, J.; Curran, J.E. induced pluripotent stem cells in disease modeling and gene identification BT - disease gene identification: Methods and protocols. Cells Tissues Organs, 2018, 1706, 17-38.
[http://dx.doi.org/10.1007/978-1-4939-7471-9_2]

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