Volume 16, Issue 4 (December 2024 2024)                   Iranian Journal of Blood and Cancer 2024, 16(4): 9-19 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Tajvidi Asr R, Rahimi M, Hossein Pourasad M, Zayer S, Momenzadeh M, Ghaderzadeh M. Hematology and Hematopathology Insights Powered by Machine Learning: Shaping the Future of Blood Disorder Management. Iranian Journal of Blood and Cancer 2024; 16 (4) :9-19
URL: http://ijbc.ir/article-1-1661-en.html
Hematology and Hematopathology Insights Powered by Machine Learning: Shaping the Future of Blood Disorder Management
Rahime Tajvidi Asr1 , Milad Rahimi2 , Mohammad Hossein Pourasad3 , Salar Zayer4 , Mohammadreza Momenzadeh5 , Mustafa Ghaderzadeh *6
1- Health and biomedical informatics Research Centers, Urmia University of Medical Sciences,Urmia,Iran.
2- Health and biomedical informatics Research Centers, Urmia University of Medical Sciences,Urmia,Iran. & Student Research Committee, Urmia University of Medical Sciences, Urmia, Iran.
3- School of paramedical, Kermanshah University of Medical Sciences, Kermanshah, Iran.
4- School of Medicine, Urmia University of Medical Sciences, Urmia, West Azerbaijan, Iran.
5- Department of Artificial Intelligence in Medical Sciences, Smart University of Medical Sciences.
6- Boukan Faculty of Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran. , mustafa.ghaderzadeh@gmail.com
Abstract:   (309 Views)
Introduction: The field of hematology faces significant challenges in data analysis, especially in the diagnosis and prediction of diseases. Traditional methods of analysis are often time-consuming, complex, or inadequate to handle the complex nature of blood-related data. This requires the development of advanced techniques for accurate prediction and classification. Artificial Intelligence (AI)-based methods have emerged as a powerful solution that enables more efficient and accurate analysis of hematological data. This study aims to systematically review published research on the use of different artificial intelligence algorithms in the analysis of this field of data.
Methods: Using a combination of keywords related to blood data analysis and artificial intelligence, we searched medical and scientific databases to identify relevant articles. A data extraction form was developed to collect relevant information from selected studies based on predefined inclusion and exclusion criteria. The content analysis method was used to analyze the extracted data and the findings were organized in tables and figures to meet the research objectives.
Results: After reviewing 7300 studies, 25 full-text studies were selected for final analysis based on their relevance to the research objectives. The findings showed that AI methods, especially deep learning (DL), are widely used to predict and diagnose hematological and Hematopathological diseases. Among the most common algorithms used in ML were XGBoost, which was one of the most important deep learning algorithms, as well as Convolutional Neural Networks (CNN). AI-based models had Accuracy, Specificity, and Sensitivity of 96.6%, 95%, and 96%, respectively.
Conclusion: This review shows that AI-based models have the potential to be significantly applied to the analysis of blood data. As artificial intelligence continues to evolve, medical professionals and researchers will have access to powerful ML-based tools to quickly and accurately diagnose.
Keywords: Hematology, Hematopathology, Machine Learning, Blood Disorder
Full-Text [PDF 626 kb]   (208 Downloads)    
: Review Article | Subject: AI in Medicine
Received: 2024/11/19 | Accepted: 2024/12/2 | Published: 2024/12/30
References
1. M. Ghaderzadeh, A. Shalchian, G. Irajian, H. Sadeghsalehi, and B. Sabet, "Artificial intelligence in drug discovery and development against antimicrobial resistance: A narrative review," Iran. J. Med. Microbiol., vol. 18, no. 3, pp. 135-147, 2024. [DOI:10.30699/ijmm.18.3.135]
2. M. Ghaderzadeh, M. Aria, A. Hosseini, F. Asadi, D. Bashash, and H. Abolghasemi, "A fast and efficient CNN model for B-ALL diagnosis and its subtypes classification using peripheral blood smear images," Int. J. Intell. Syst., vol. 37, no. 8, pp. 5113-5133, 2022. [DOI:10.1002/int.22753]
3. A. Bazinet et al., "Automated quantification of measurable residual disease in chronic lymphocytic leukemia using an artificial intelligence‐assisted workflow," Cytom. Part B Clin. Cytom., vol. 106, no. 4, pp. 264-271, 2024. [DOI:10.1002/cyto.b.22116]
4. T. Dehkharghanian, Y. Mu, H. R. Tizhoosh, and C. J. V Campbell, "Applied machine learning in hematopathology," Int. J. Lab. Hematol., vol. 45, no. S2, pp. 87-94, 2023. [DOI:10.1111/ijlh.14110]
5. Y. Hu, Y. Luo, G. Tang, Y. Huang, J. Kang, and D. Wang, "Artificial intelligence and its applications in digital hematopathology," Blood Sci., vol. 4, no. 3, pp. 136-142, 2022. [DOI:10.1097/BS9.0000000000000130]
6. D. Moher et al., "Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement," Syst. Rev., vol. 4, pp. 1-9, 2015. [DOI:10.1186/2046-4053-4-1]
7. P. F. Whiting et al., "QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies," Ann. Intern. Med., vol. 155, no. 8, pp. 529-536, 2011. [DOI:10.7326/0003-4819-155-8-201110180-00009]
8. S. El Hussein, P. Chen, L. J. Medeiros, J. D. Hazle, J. Wu, and J. D. Khoury, "Artificial intelligence-assisted mapping of proliferation centers allows the distinction of accelerated phase from large cell transformation in chronic lymphocytic leukemia," Mod. Pathol., vol. 35, no. 8, pp. 1121-1125, 2022. [DOI:10.1038/s41379-022-01015-9]
9. N. Aydin Atasoy and A. Faris Abdulla Al Rahhawi, "Examining the classification performance of pre-trained capsule networks on imbalanced bone marrow cell dataset," Int. J. Imaging Syst. Technol., vol. 34, no. 3, p. e23067, May 2024. [DOI:10.1002/ima.23067]
10. S. Koga, "Exploring the pitfalls of large language models: Inconsistency and inaccuracy in answering pathology board examination-style questions," Pathol. Int., vol. 73, no. 12, pp. 618-620, Dec. 2023. [DOI:10.1111/pin.13382]
11. J. S. Mohlman, S. D. Leventhal, T. Hansen, J. Kohan, V. Pascucci, and M. E. Salama, "Improving Augmented Human Intelligence to Distinguish Burkitt Lymphoma from Diffuse Large B-Cell Lymphoma Cases," Am. J. Clin. Pathol., vol. 153, no. 6, pp. 743-759, 2020. [DOI:10.1093/ajcp/aqaa001]
12. A. M. Tsakiroglou et al., "Lymphoma triage from H&E using AI for improved clinical management," J. Clin. Pathol., vol. 78, no. 1, pp. 28-33, 2025.
13. K. Sasaki et al., "The LEukemia Artificial Intelligence Program (LEAP) in chronic myeloid leukemia in chronic phase: A model to improve patient outcomes," Am. J. Hematol., vol. 96, no. 2, pp. 241-250, 2021. [DOI:10.1002/ajh.26047]
14. S. Fazeli, A. Samiei, T. D. Lee, and M. Sarrafzadeh, "Beyond Labels: Visual Representations for Bone Marrow Cell Morphology Recognition," in Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023, 2023, pp. 111-117. [DOI:10.1109/ICHI57859.2023.00025]
15. M. Osman et al., "Classification of monocytes, promonocytes and monoblasts using deep neural network models: An area of unmet need in diagnostic hematopathology," J. Clin. Med., vol. 10, no. 11, 2021. [DOI:10.3390/jcm10112264]
16. Z. Lu et al., "Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders," Diagnostics, vol. 14, no. 4, 2024. [DOI:10.3390/diagnostics14040420]
17. E. Hasan, Q. Eichbaum, A. C. Seegmiller, C. Stratton, and J. S. Trueblood, "Improving Medical Image Decision-Making by Leveraging Metacognitive Processes and Representational Similarity," Top. Cogn. Sci., vol. 14, no. 2, pp. 400-413, Apr. 2022, [DOI:10.1111/tops.12588]
18. Y. Mu, H. R. Tizhoosh, T. Dehkharghanian, and C. J. V Campbell, "Whole slide image representation in bone marrow cytology," Comput. Biol. Med., vol. 166, p. 107530, 2023. [DOI:10.1016/j.compbiomed.2023.107530]
19. S. Saxena, P. Sanyal, M. Bajpai, R. Prakash, and S. Kumar, "Trials and tribulations: Developing an artificial intelligence for screening malaria parasite from peripheral blood smears," Med. J. Armed Forces India, 2023. [DOI:10.1016/j.mjafi.2023.10.007]
20. Z. Zhang et al., "The Diagnosis of Chronic Myeloid Leukemia with Deep Adversarial Learning," Am. J. Pathol., vol. 192, no. 7, pp. 1083-1091, 2022. [DOI:10.1016/j.ajpath.2022.03.016]
21. N. Abele et al., "Noninferiority of Artificial Intelligence-Assisted Analysis of Ki-67 and Estrogen/Progesterone Receptor in Breast Cancer Routine Diagnostics," Mod. Pathol., vol. 36, no. 3, p. 100033, 2023. [DOI:10.1016/j.modpat.2022.100033]
22. G. S. Raju et al., "Natural language processing as an alternative to manual reporting of colonoscopy quality metrics," Gastrointest. Endosc., vol. 82, no. 3, pp. 512-519, 2015. [DOI:10.1016/j.gie.2015.01.049]
23. Y. Mu, H. R. Tizhoosh, T. Dehkharghanian, S. Alfasly, and C. J. V Campbell, "Model-Agnostic Binary Patch Grouping for Bone Marrow Whole Slide Image Representation," Am. J. Pathol., vol. 194, no. 5, pp. 721-734, 2024. [DOI:10.1016/j.ajpath.2024.01.012]
24. C.-W. Wang, S.-C. Huang, Y.-C. Lee, Y.-J. Shen, S.-I. Meng, and J. L. Gaol, "Deep learning for bone marrow cell detection and classification on whole-slide images," Med. Image Anal., vol. 75, p. 102270, 2022. [DOI:10.1016/j.media.2021.102270]
25. T. J. Brinker et al., "Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours," Eur. J. Cancer, vol. 154, pp. 227-234, 2021. [DOI:10.1016/j.ejca.2021.05.026]
26. K. Sirinukunwattana et al., "Artificial intelligence-based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients," Blood Adv., vol. 4, no. 14, pp. 3284-3294, 2020. [DOI:10.1182/bloodadvances.2020002230]
27. S. El Hussein et al., "Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia," J. Pathol., vol. 256, no. 1, pp. 4-14, Jan. 2022. [DOI:10.1002/path.5795]
28. J. Su, S. Liu, and J. Song, "A segmentation method based on HMRF for the aided diagnosis of acute myeloid leukemia," Comput. Methods Programs Biomed., vol. 152, pp. 115-123, 2017. [DOI:10.1016/j.cmpb.2017.09.011]
29. Z. Y. Xu-Monette et al., "A refined cell-of-origin classifier with targeted NGS and artificial intelligence shows robust predictive value in DLBCL," Blood Adv., vol. 4, no. 14, pp. 3391-3404, 2020. [DOI:10.1182/bloodadvances.2020001949]
30. A. Acevedo, A. Merino, L. Boldú, Á. Molina, S. Alférez, and J. Rodellar, "A new convolutional neural network predictive model for the automatic recognition of hypogranulated neutrophils in myelodysplastic syndromes," Comput. Biol. Med., vol. 134, p. 104479, 2021. [DOI:10.1016/j.compbiomed.2021.104479]
31. A. K. Yenamandra, C. Hughes, and A. S. Maris, "Artificial Intelligence in Plasma Cell Myeloma: Neural Networks and Support Vector Machines in the Classification of Plasma Cell Myeloma Data at Diagnosis," J. Pathol. Inform., vol. 12, no. 1, p. 35, 2021. [DOI:10.4103/jpi.jpi_26_21]
Add your comments about this article : Your username or Email:
CAPTCHA
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2025 All Rights Reserved | Iranian Journal of Blood and Cancer

Designed & Developed by : Yektaweb