Short Report
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Year 2021, Volume: 1 Issue: 1, 1 - 7, 30.04.2021

Elif Güler Kazancı Deniz Güven

Abstract

References

  • 1. Wilkes AL, Wade NJ. Bain on neural networks. Brain Cogn 1997;33:295–305.
  • 2. Mc Culloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943;5:11–143.
  • 3. Zini G. Artificial intelligence in hematology. Hematology. 2005 Oct;10(5):393-400. doi: 10.1080/10245330410001727055.PMID: 16203606
  • 4. Diamond LW, Minshka VG, Seal AH, Nguyen DT. Multiparameter interpretative reporting in diagnostic laboratory hematology. Int J Biomed Comput 1994;37:211–224.
  • 5. Diamond LW, Nguyen DT, Andreeff M, Maiese RL, BraylanRC. A knowledge-based system for the interpretation of flowcytometry data in leukemias and lymphomas. Cytometry 1994;17:266–273.
  • 6. Nguyen DT, Diamond LW, Cavenagh JD, Parameswaran R, A mess JA. Haematological validation of a computer-based bone marrow reporting system. J Clin Pathol 1997; 50:375–378
  • 7. Ornstein L. Computer learning and the scientific method: A proposed solution to the information theoretical problem of meaning. J Mount Sinai Hosp 1965;32:437–494
  • 8. Erler BS, Vitagliano P, Lee S. Superiority of neural networks over discriminant functions for thalassemia minor screening of red blood cell microcytosis. Arch Pathol Lab Med 1995;119:350–354.
  • 9. Birndorf RI, Pentecost JO, Coakley JR, Spackman KA. An exper system to diagnose anemia and report results directly on hematology forms. Comput Biomed Res 1996;29:16–26.
  • 10. Amendolia SR, Brunetti A, Carta P, Cossu G, Ganadu ML, Golosio B, Mura GM, Pirastru MG. A real-time classification system of thalassemic pathologies based on artificial neual networks. Med Decis Making 2002;22:18–26.
  • 11. d’Onofrio G, Zini G. Diagnostic value of peroxidase and size parameters from a new hematological analyzer. Hematologica 1998;238–239, Proceedings of XXII Congress of ISH.
  • 12. Zini G, d’Onofrio G. Neural network in hematological malignancies. Clin Chim Acta 2003;333:195–201
  • 13. Kantardzic M, Djulbegovic B, Hamdan H. A data-mining approach to improving polycythemia vera diagnosis. Comput Ind Eng Archiv 2002;43:765–773
  • 14. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: Class discovery and class rediction by gene expression monitoring. Science 1999;286:531–537
  • 15. Ramalho-Santos M, Yoon S, Matsuzaki Y, Mulligan RC, Melton DA. Stemness: Transcriptional profiling of embryonic and adult stem cells. Science 2002;298:597–601.
  • 16. Artificial intelligence models for predicting iron deficiency anemia and iron serum level based on accessible laboratory data. Azarkhish I, et al. J Med Syst. 2012. PMID: 21503744
  • 17. Kabootarizadeh L, Jamshidnezhad A, Koohmareh Z.Differential Diagnosis of Iron-Deficiency Anemia from beta-Thalassemia Trait Using an Intelligent Model in Comparison with Discriminant Indexes. Acta Inform Med. 2019 Jun;27(2):78-84. doi: 10.5455/aim.2019.27.78-84. PMID: 31452563
  • 18. Laengsri V, Shoombuatong W, Adirojananon W, Nantasenamat C, Prachayasittikul V, Nuchnoi P. ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia. BMC Med Inform Decis Mak. 2019 Nov 7;19(1):212. doi: 10.1186/s12911-019-0929-2. PMID: 31699079
  • 19. Tong L, Kauer J, Chen X, Chu K, Dou H, Smith ZJ. Screening of nutritional and genetic anemias using elastic light scattering. Lab Chip. 2018 Oct 23;18(21):3263-3271. doi: 10.1039/c8lc00377g. PMID: 30264831
  • 20. Kim G, Jo Y, Cho H, Min HS, Park Y. Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells. Biosens Bioelectron. 2019 Jan 1;123:69-76. doi:0.1016/j.bios.2018.09.068. Epub 2018 Sep 21. PMID: 30321758
  • 21. Lin YH, Liao KY, Sung KB. Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network. J Biomed Opt. 2020 Nov;25(11):116502. doi:10.1117/1.JBO.25.11.116502. PMID: 33188571
  • 22. Morita K, Wang F, Makishima H, et al. . Pan-myeloid leukemia analysis: machine learning-based approach to predict phenotype and clinical outcomes using mutation data. Blood. 2018;132(suppl 1):1801.
  • 23. Siddiqui NS, Klein A, Godara A, Varga C, Buchsbaum RJ, Hughes MC. Supervised machine learning algorithms using patient related factors to predict in-hospital mortality following acute myeloid leukemia therapy. Blood. 2019;134(suppl 1):3435
  • 24. Gerstung M, Papaemmanuil E, Martincorena I, et al. . Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet. 2017;49(3):332-340.
  • 25. Fleming S, Tsai CH, Döhner H, et al. . Use of machine learning in 2074 cases of acute myeloid leukemia for genetic risk profiling. Blood. 2019;134(suppl 1):1392. [Google Scholar]
  • 26. Shreve J, Meggendorfer M, Awada H, et al. . A personalized prediction model to risk stratify patients with acute myeloid leukemia (AML) using artificial intelligence. Blood. 2019;134(suppl 1):2091 doi:10.1182/blood- 2019-128066
  • 27. Li J, Wang Y, Ko B, Li C, Tang J, Lee C. Learning a cytometric deep phenotype embedding for automatic hematological malignancies classification. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). New York, NY: IEEE; 2019:1733-1736.
  • 28. Patkar N, Shaikh AF, Kakirde C, et al. . A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1. Blood Cancer J. 2019;9(10):79.
  • 29. Wagner S, Vadakekolathu J, Tasian SK, et al. . A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study. Blood Adv. 2019;3(8):1330-1346.
  • 30. Gal O, Auslander N, Fan Y, Meerzaman D. Predicting complete remission of acute myeloid leukemia: machine learning applied to gene expression. Cancer Inform. 2019;18:1176935119835544. 59
  • 31. Lee SI, Celik S, Logsdon BA, et al. . A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nat Commun. 2018;9(1):42.
  • 32. Chen X, Chen HY, Chen ZD, Gong JN, Chen CYC. A novel artificial intelligence protocol for finding potential inhibitors of acute myeloid leukemia. J Mater Chem B Mater Biol Med. 2020;8(10):2063-2081.
  • 33. Janssen APA, Grimm SH, Wijdeven RHM, et al. . Drug discovery maps, a machine learning model that visualizes and predicts kinome-inhibitor interaction landscapes. J Chem Inf Model. 2019;59(3):1221-1229.
  • 34. Cutler G, Fridman JS. A machine-learning analysis suggests that FLX925, a FLT3/CDK4/6 kinase inhibitor, is potent against FLT3-wild type tumors via its CDK4/6 activity. Blood. 2016;128(22):3520.
  • 35. Shouval R, Labopin M, Unger R, et al. . Prediction of hematopoietic stem cell transplantation related mortality—lessons learned from the in-silico approach: a European Society for Blood and Marrow Transplantation Acute Leukemia Working Party Data Mining Study. PLoS One. 2016;11(3):e0150637.
  • 36. Shouval R, Bonifazi F, Fein J, et al. . Validation of the acute leukemia-EBMT score for prediction of mortality following allogeneic stem cell transplantation in a multi-center GITMO cohort. Am J Hematol. 2017;92(5):429-434.
  • 37. Bornhäuser M. Conditioning intensity and antilymphocyte globulin: towards personalized transplant strategies? Haematologica. 2019;104(6):1101-1102.
  • 38. Fuse K, Uemura S, Tamura S, et al. . Patient-based prediction algorithm of relapse after allo-HSCT for acute leukemia and its usefulness in the decision-making process using a machine learning approach. Cancer Med. 2019;8(11):5058-5067.
  • 39. Arai Y, Kondo T, Fuse K, et al. . Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation. Blood Adv. 2019;3(22):3626-3634.
  • 40. Gandelman JS, Byrne MT, Mistry AM, 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):189-196.
  • 41. Marino SR, Lee SM, Binkowski TA, Wang T, Haagenson M, Wang HL, Maiers M, Spellman S, van Besien K, Lee SJ, Karrison T, Artz A. Identification of high-risk amino-acid substitutions in hematopoietic cell transplantation: a challenging task. Bone Marrow Transplant. 2016;51:1342–1349
  • 42. Buturovic L, Shelton J, Spellman SR, Wang T, Friedman L, Loftus D, Hesterberg L, Woodring T, Fleischhauer K, Hsu KC, Verneris MR, Haagenson M, Lee SJ. Evaluation of a machine learning-based prognostic model for unrelated hematopoietic cell transplantation donor selection. Biol Blood Marrow Transplant. 2018;24:1299–1306
  • 43. Sarkar C, Srivastava J. Impact of density of lab data in EHR for prediction of potentially preventable events. In: 2013 IEEE International Conference on Healthcare Informatics. New York, IEEE. 2013.
  • 44. Sivasankaran A, Cherkassky V, Albrecht M, Williams E, Maiers M. Donor selection for hematopoietic stem cell transplant using cost-sensitive SVM. In: IEEE 14th International Conference on Machine Learning and Applications. New York, IEEE, 2015
  • 45. Shouval R, Bonifazi F, Fein J, Boschini C, Oldani E, Labopin M, Raimondi R, Sacchi N, Dabash O, Unger R, Mohty M, Rambaldi A, Nagler A. Validation of the acute leukemia-EBMT score for prediction of mortality following allogeneic stem cell transplantation in a multi-center GITMO cohort. Am J Hematol. 2017;92:429– 434
  • 46. Li CC, Ko BS, Wang YF, Li JL, Weng PF, Hou HA, Liao XW, Lin CT, Liu JH, Sun HI, Tien HF, Lee CC, Tang JL. An artificial intelligence approach for predicting allogeneic hematopoietic stem cell transplantation outcome by detecting pre-transplant minimal residual disease in acute myeloid leukemia using flow cytometry data. Blood. 2017;130(Suppl 1):2042.
  • 47. Chulián S, Martínez-Rubio Á, Pérez-García VM, Rosa M, Blázquez Goñi C, Rodríguez Gutiérrez JF, Hermosín-Ramos L, Molinos Quintana Á, Caballero-Velázquez T, Ramírez-Orellana M, Castillo Robleda A, Fernández-Martínez JL. High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia. Cancers (Basel). 2020 Dec 23;13(1):17. doi:10.3390/cancers13010017.PMID: 33374500
  • 48. Kratz A, Lee SH, Zini G, Riedl JA, Hur M, Machin S. Digital morphology analyzers in hematology: ICSH review and recommendations.; International Council for Standardization in Haematology. Int J Lab Hematol. 2019 Aug;41(4):437-447. doi: 10.1111/ijlh.13042. Epub 2019 May 2. PMID: 31046197
  • 49. Ohsaka A. Rinsho Ketsueki. [Artificial intelligence (AI) and hematological diseases: establishment of a peripheral blood convolutional neural network (CNN)-based digital morphology analysis system].2020;61(5):564-569. doi: 10.11406/rinketsu.61.564.PMID: 32507825
  • 50. Sniecinski I, Seghatchian J. Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine. Transfus Apher Sci. 2018 Jun;57(3):422-424. doi:10.1016/j.transci.2018.05.004. Epub 2018 May 9. PMID: 29784537

Artificial Intelligence Applications in Hematology

Year 2021, Volume: 1 Issue: 1, 1 - 7, 30.04.2021

Elif Güler Kazancı Deniz Güven

Abstract

Artificial intelligence (AI) is a computer-based science that aims to simulate the human brain using a system. One of the most used areas of artificial intelligence in medicine is hematology. In this study, the Pubmed database was scanned using the words "hematology, artificial intelligence". The chronological development of artificial intelligence in hematology was evaluated by examining the articles found. AI was used firstly in hematology with peripheral blood interpretation in laboratory systems. It was followed by flow cytometry for immunophenotyping and bone marrow reporting. The diagnosis of iron
eficiency anemia, hemoglobinopathies, Polistemia Vera and classification of hematological malignancies such as leukemia and lymphoma were made. AI-supported algorithms are developed for
mmunotherapy and evaluation of recipient compatibility before stem cell transplantation. Also they used evaluation of recurrence and complications after transplantation. Together with the new generation digital image analyzers, images has been transferred to a central laboratory and an unique automation that can be archived with fast, high-quality consultation. As a result, artificial intelligence systems have been used in the diagnosis and treatment of hematological diseases from past to present and seem to play an important role in the future.

Keywords

artificial intelligence hematology medicine

References

  • 1. Wilkes AL, Wade NJ. Bain on neural networks. Brain Cogn 1997;33:295–305.
  • 2. Mc Culloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943;5:11–143.
  • 3. Zini G. Artificial intelligence in hematology. Hematology. 2005 Oct;10(5):393-400. doi: 10.1080/10245330410001727055.PMID: 16203606
  • 4. Diamond LW, Minshka VG, Seal AH, Nguyen DT. Multiparameter interpretative reporting in diagnostic laboratory hematology. Int J Biomed Comput 1994;37:211–224.
  • 5. Diamond LW, Nguyen DT, Andreeff M, Maiese RL, BraylanRC. A knowledge-based system for the interpretation of flowcytometry data in leukemias and lymphomas. Cytometry 1994;17:266–273.
  • 6. Nguyen DT, Diamond LW, Cavenagh JD, Parameswaran R, A mess JA. Haematological validation of a computer-based bone marrow reporting system. J Clin Pathol 1997; 50:375–378
  • 7. Ornstein L. Computer learning and the scientific method: A proposed solution to the information theoretical problem of meaning. J Mount Sinai Hosp 1965;32:437–494
  • 8. Erler BS, Vitagliano P, Lee S. Superiority of neural networks over discriminant functions for thalassemia minor screening of red blood cell microcytosis. Arch Pathol Lab Med 1995;119:350–354.
  • 9. Birndorf RI, Pentecost JO, Coakley JR, Spackman KA. An exper system to diagnose anemia and report results directly on hematology forms. Comput Biomed Res 1996;29:16–26.
  • 10. Amendolia SR, Brunetti A, Carta P, Cossu G, Ganadu ML, Golosio B, Mura GM, Pirastru MG. A real-time classification system of thalassemic pathologies based on artificial neual networks. Med Decis Making 2002;22:18–26.
  • 11. d’Onofrio G, Zini G. Diagnostic value of peroxidase and size parameters from a new hematological analyzer. Hematologica 1998;238–239, Proceedings of XXII Congress of ISH.
  • 12. Zini G, d’Onofrio G. Neural network in hematological malignancies. Clin Chim Acta 2003;333:195–201
  • 13. Kantardzic M, Djulbegovic B, Hamdan H. A data-mining approach to improving polycythemia vera diagnosis. Comput Ind Eng Archiv 2002;43:765–773
  • 14. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: Class discovery and class rediction by gene expression monitoring. Science 1999;286:531–537
  • 15. Ramalho-Santos M, Yoon S, Matsuzaki Y, Mulligan RC, Melton DA. Stemness: Transcriptional profiling of embryonic and adult stem cells. Science 2002;298:597–601.
  • 16. Artificial intelligence models for predicting iron deficiency anemia and iron serum level based on accessible laboratory data. Azarkhish I, et al. J Med Syst. 2012. PMID: 21503744
  • 17. Kabootarizadeh L, Jamshidnezhad A, Koohmareh Z.Differential Diagnosis of Iron-Deficiency Anemia from beta-Thalassemia Trait Using an Intelligent Model in Comparison with Discriminant Indexes. Acta Inform Med. 2019 Jun;27(2):78-84. doi: 10.5455/aim.2019.27.78-84. PMID: 31452563
  • 18. Laengsri V, Shoombuatong W, Adirojananon W, Nantasenamat C, Prachayasittikul V, Nuchnoi P. ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia. BMC Med Inform Decis Mak. 2019 Nov 7;19(1):212. doi: 10.1186/s12911-019-0929-2. PMID: 31699079
  • 19. Tong L, Kauer J, Chen X, Chu K, Dou H, Smith ZJ. Screening of nutritional and genetic anemias using elastic light scattering. Lab Chip. 2018 Oct 23;18(21):3263-3271. doi: 10.1039/c8lc00377g. PMID: 30264831
  • 20. Kim G, Jo Y, Cho H, Min HS, Park Y. Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells. Biosens Bioelectron. 2019 Jan 1;123:69-76. doi:0.1016/j.bios.2018.09.068. Epub 2018 Sep 21. PMID: 30321758
  • 21. Lin YH, Liao KY, Sung KB. Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network. J Biomed Opt. 2020 Nov;25(11):116502. doi:10.1117/1.JBO.25.11.116502. PMID: 33188571
  • 22. Morita K, Wang F, Makishima H, et al. . Pan-myeloid leukemia analysis: machine learning-based approach to predict phenotype and clinical outcomes using mutation data. Blood. 2018;132(suppl 1):1801.
  • 23. Siddiqui NS, Klein A, Godara A, Varga C, Buchsbaum RJ, Hughes MC. Supervised machine learning algorithms using patient related factors to predict in-hospital mortality following acute myeloid leukemia therapy. Blood. 2019;134(suppl 1):3435
  • 24. Gerstung M, Papaemmanuil E, Martincorena I, et al. . Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet. 2017;49(3):332-340.
  • 25. Fleming S, Tsai CH, Döhner H, et al. . Use of machine learning in 2074 cases of acute myeloid leukemia for genetic risk profiling. Blood. 2019;134(suppl 1):1392. [Google Scholar]
  • 26. Shreve J, Meggendorfer M, Awada H, et al. . A personalized prediction model to risk stratify patients with acute myeloid leukemia (AML) using artificial intelligence. Blood. 2019;134(suppl 1):2091 doi:10.1182/blood- 2019-128066
  • 27. Li J, Wang Y, Ko B, Li C, Tang J, Lee C. Learning a cytometric deep phenotype embedding for automatic hematological malignancies classification. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). New York, NY: IEEE; 2019:1733-1736.
  • 28. Patkar N, Shaikh AF, Kakirde C, et al. . A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1. Blood Cancer J. 2019;9(10):79.
  • 29. Wagner S, Vadakekolathu J, Tasian SK, et al. . A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study. Blood Adv. 2019;3(8):1330-1346.
  • 30. Gal O, Auslander N, Fan Y, Meerzaman D. Predicting complete remission of acute myeloid leukemia: machine learning applied to gene expression. Cancer Inform. 2019;18:1176935119835544. 59
  • 31. Lee SI, Celik S, Logsdon BA, et al. . A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nat Commun. 2018;9(1):42.
  • 32. Chen X, Chen HY, Chen ZD, Gong JN, Chen CYC. A novel artificial intelligence protocol for finding potential inhibitors of acute myeloid leukemia. J Mater Chem B Mater Biol Med. 2020;8(10):2063-2081.
  • 33. Janssen APA, Grimm SH, Wijdeven RHM, et al. . Drug discovery maps, a machine learning model that visualizes and predicts kinome-inhibitor interaction landscapes. J Chem Inf Model. 2019;59(3):1221-1229.
  • 34. Cutler G, Fridman JS. A machine-learning analysis suggests that FLX925, a FLT3/CDK4/6 kinase inhibitor, is potent against FLT3-wild type tumors via its CDK4/6 activity. Blood. 2016;128(22):3520.
  • 35. Shouval R, Labopin M, Unger R, et al. . Prediction of hematopoietic stem cell transplantation related mortality—lessons learned from the in-silico approach: a European Society for Blood and Marrow Transplantation Acute Leukemia Working Party Data Mining Study. PLoS One. 2016;11(3):e0150637.
  • 36. Shouval R, Bonifazi F, Fein J, et al. . Validation of the acute leukemia-EBMT score for prediction of mortality following allogeneic stem cell transplantation in a multi-center GITMO cohort. Am J Hematol. 2017;92(5):429-434.
  • 37. Bornhäuser M. Conditioning intensity and antilymphocyte globulin: towards personalized transplant strategies? Haematologica. 2019;104(6):1101-1102.
  • 38. Fuse K, Uemura S, Tamura S, et al. . Patient-based prediction algorithm of relapse after allo-HSCT for acute leukemia and its usefulness in the decision-making process using a machine learning approach. Cancer Med. 2019;8(11):5058-5067.
  • 39. Arai Y, Kondo T, Fuse K, et al. . Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation. Blood Adv. 2019;3(22):3626-3634.
  • 40. Gandelman JS, Byrne MT, Mistry AM, 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):189-196.
  • 41. Marino SR, Lee SM, Binkowski TA, Wang T, Haagenson M, Wang HL, Maiers M, Spellman S, van Besien K, Lee SJ, Karrison T, Artz A. Identification of high-risk amino-acid substitutions in hematopoietic cell transplantation: a challenging task. Bone Marrow Transplant. 2016;51:1342–1349
  • 42. Buturovic L, Shelton J, Spellman SR, Wang T, Friedman L, Loftus D, Hesterberg L, Woodring T, Fleischhauer K, Hsu KC, Verneris MR, Haagenson M, Lee SJ. Evaluation of a machine learning-based prognostic model for unrelated hematopoietic cell transplantation donor selection. Biol Blood Marrow Transplant. 2018;24:1299–1306
  • 43. Sarkar C, Srivastava J. Impact of density of lab data in EHR for prediction of potentially preventable events. In: 2013 IEEE International Conference on Healthcare Informatics. New York, IEEE. 2013.
  • 44. Sivasankaran A, Cherkassky V, Albrecht M, Williams E, Maiers M. Donor selection for hematopoietic stem cell transplant using cost-sensitive SVM. In: IEEE 14th International Conference on Machine Learning and Applications. New York, IEEE, 2015
  • 45. Shouval R, Bonifazi F, Fein J, Boschini C, Oldani E, Labopin M, Raimondi R, Sacchi N, Dabash O, Unger R, Mohty M, Rambaldi A, Nagler A. Validation of the acute leukemia-EBMT score for prediction of mortality following allogeneic stem cell transplantation in a multi-center GITMO cohort. Am J Hematol. 2017;92:429– 434
  • 46. Li CC, Ko BS, Wang YF, Li JL, Weng PF, Hou HA, Liao XW, Lin CT, Liu JH, Sun HI, Tien HF, Lee CC, Tang JL. An artificial intelligence approach for predicting allogeneic hematopoietic stem cell transplantation outcome by detecting pre-transplant minimal residual disease in acute myeloid leukemia using flow cytometry data. Blood. 2017;130(Suppl 1):2042.
  • 47. Chulián S, Martínez-Rubio Á, Pérez-García VM, Rosa M, Blázquez Goñi C, Rodríguez Gutiérrez JF, Hermosín-Ramos L, Molinos Quintana Á, Caballero-Velázquez T, Ramírez-Orellana M, Castillo Robleda A, Fernández-Martínez JL. High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia. Cancers (Basel). 2020 Dec 23;13(1):17. doi:10.3390/cancers13010017.PMID: 33374500
  • 48. Kratz A, Lee SH, Zini G, Riedl JA, Hur M, Machin S. Digital morphology analyzers in hematology: ICSH review and recommendations.; International Council for Standardization in Haematology. Int J Lab Hematol. 2019 Aug;41(4):437-447. doi: 10.1111/ijlh.13042. Epub 2019 May 2. PMID: 31046197
  • 49. Ohsaka A. Rinsho Ketsueki. [Artificial intelligence (AI) and hematological diseases: establishment of a peripheral blood convolutional neural network (CNN)-based digital morphology analysis system].2020;61(5):564-569. doi: 10.11406/rinketsu.61.564.PMID: 32507825
  • 50. Sniecinski I, Seghatchian J. Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine. Transfus Apher Sci. 2018 Jun;57(3):422-424. doi:10.1016/j.transci.2018.05.004. Epub 2018 May 9. PMID: 29784537
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Details

Primary Language English
Subjects Clinical Sciences, Engineering
Journal Section Short Reports
Authors

Elif Güler Kazancı

Deniz Güven

Publication Date April 30, 2021
Published in Issue Year 2021 Volume: 1 Issue: 1

Cite

APA Kazancı, E. G., & Güven, D. (2021). Artificial Intelligence Applications in Hematology. Artificial Intelligence Theory and Applications, 1(1), 1-7.
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