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Lin Y, Chen Q, Chen T. Recent advancements in machine learning for bone marrow cell morphology analysis. Front Med (Lausanne) 2024; 11:1402768. [PMID: 38947236 PMCID: PMC11211563 DOI: 10.3389/fmed.2024.1402768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 05/31/2024] [Indexed: 07/02/2024] Open
Abstract
As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition, classification, and visualization. Within the domain of bone marrow cell morphology analysis, deep learning offers substantial benefits due to its robustness, ability for automatic feature learning, and strong image characterization capabilities. Deep neural networks are a machine learning paradigm specifically tailored for image processing applications. Artificial intelligence serves as a potent tool in supporting the diagnostic process of clinical bone marrow cell morphology. Despite the potential of artificial intelligence to augment clinical diagnostics in this domain, manual analysis of bone marrow cell morphology remains the gold standard and an indispensable tool for identifying, diagnosing, and assessing the efficacy of hematologic disorders. However, the traditional manual approach is not without limitations and shortcomings, necessitating, the exploration of automated solutions for examining and analyzing bone marrow cytomorphology. This review provides a multidimensional account of six bone marrow cell morphology processes: automated bone marrow cell morphology detection, automated bone marrow cell morphology segmentation, automated bone marrow cell morphology identification, automated bone marrow cell morphology classification, automated bone marrow cell morphology enumeration, and automated bone marrow cell morphology diagnosis. Highlighting the attractiveness and potential of machine learning systems based on bone marrow cell morphology, the review synthesizes current research and recent advances in the application of machine learning in this field. The objective of this review is to offer recommendations to hematologists for selecting the most suitable machine learning algorithms to automate bone marrow cell morphology examinations, enabling swift and precise analysis of bone marrow cytopathic trends for early disease identification and diagnosis. Furthermore, the review endeavors to delineate potential future research avenues for machine learning-based applications in bone marrow cell morphology analysis.
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Affiliation(s)
- Yifei Lin
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
| | - Qingquan Chen
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
- The School of Public Health, Fujian Medical University, Fuzhou, Fujian, China
| | - Tebin Chen
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
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Elshoeibi AM, Badr A, Elsayed B, Metwally O, Elshoeibi R, Elhadary MR, Elshoeibi A, Attya MA, Khadadah F, Alshurafa A, Alhuraiji A, Yassin M. Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects. Cancers (Basel) 2023; 16:65. [PMID: 38201493 PMCID: PMC10778500 DOI: 10.3390/cancers16010065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 01/12/2024] Open
Abstract
Myelodysplastic syndrome (MDS) is composed of diverse hematological malignancies caused by dysfunctional stem cells, leading to abnormal hematopoiesis and cytopenia. Approximately 30% of MDS cases progress to acute myeloid leukemia (AML), a more aggressive disease. Early detection is crucial to intervene before MDS progresses to AML. The current diagnostic process for MDS involves analyzing peripheral blood smear (PBS), bone marrow sample (BMS), and flow cytometry (FC) data, along with clinical patient information, which is labor-intensive and time-consuming. Recent advancements in machine learning offer an opportunity for faster, automated, and accurate diagnosis of MDS. In this review, we aim to provide an overview of the current applications of AI in the diagnosis of MDS and highlight their advantages, disadvantages, and performance metrics.
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Affiliation(s)
| | - Ahmed Badr
- College of Medicine, QU Health, Qatar University, Doha 2713, Qatar
| | - Basel Elsayed
- College of Medicine, QU Health, Qatar University, Doha 2713, Qatar
| | - Omar Metwally
- College of Medicine, QU Health, Qatar University, Doha 2713, Qatar
| | | | | | | | | | - Fatima Khadadah
- Kuwait Cancer Centre, Sabah Medical Region, Shuwaikh 1031, Kuwait
| | - Awni Alshurafa
- Hematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha 3050, Qatar
| | - Ahmad Alhuraiji
- Kuwait Cancer Centre, Sabah Medical Region, Shuwaikh 1031, Kuwait
| | - Mohamed Yassin
- College of Medicine, QU Health, Qatar University, Doha 2713, Qatar
- Hematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha 3050, Qatar
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Salafranca J, Ko JK, Mukherjee AK, Fritzsche M, van Grinsven E, Udalova IA. Neutrophil nucleus: shaping the past and the future. J Leukoc Biol 2023; 114:585-594. [PMID: 37480361 PMCID: PMC10673716 DOI: 10.1093/jleuko/qiad084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 07/24/2023] Open
Abstract
Neutrophils are innate immune cells that are key to protecting the host against infection and maintaining body homeostasis. However, if dysregulated, they can contribute to disease, such as in cancer or chronic autoinflammatory disorders. Recent studies have highlighted the heterogeneity in the neutrophil compartment and identified the presence of immature neutrophils and their precursors in these pathologies. Therefore, understanding neutrophil maturity and the mechanisms through which they contribute to disease is critical. Neutrophils were first characterized morphologically by Ehrlich in 1879 using microscopy, and since then, different technologies have been used to assess neutrophil maturity. The advances in the imaging field, including state-of-the-art microscopy and machine learning algorithms for image analysis, reinforce the use of neutrophil nuclear morphology as a fundamental marker of maturity, applicable for objective classification in clinical diagnostics. New emerging approaches, such as the capture of changes in chromatin topology, will provide mechanistic links between the nuclear shape, chromatin organization, and transcriptional regulation during neutrophil maturation.
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Affiliation(s)
- Julia Salafranca
- The Kennedy Institute of Rheumatology, University of Oxford, Old Road Campus Research Build, Roosevelt Dr, Headington, Oxford OX3 7DQ, United Kingdom
| | - Jacky Ka Ko
- The Kennedy Institute of Rheumatology, University of Oxford, Old Road Campus Research Build, Roosevelt Dr, Headington, Oxford OX3 7DQ, United Kingdom
| | - Ananda K Mukherjee
- The Kennedy Institute of Rheumatology, University of Oxford, Old Road Campus Research Build, Roosevelt Dr, Headington, Oxford OX3 7DQ, United Kingdom
| | - Marco Fritzsche
- The Kennedy Institute of Rheumatology, University of Oxford, Old Road Campus Research Build, Roosevelt Dr, Headington, Oxford OX3 7DQ, United Kingdom
| | - Erinke van Grinsven
- The Kennedy Institute of Rheumatology, University of Oxford, Old Road Campus Research Build, Roosevelt Dr, Headington, Oxford OX3 7DQ, United Kingdom
| | - Irina A Udalova
- The Kennedy Institute of Rheumatology, University of Oxford, Old Road Campus Research Build, Roosevelt Dr, Headington, Oxford OX3 7DQ, United Kingdom
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van Eekelen L, Litjens G, Hebeda KM. Artificial Intelligence in Bone Marrow Histological Diagnostics: Potential Applications and Challenges. Pathobiology 2023; 91:8-17. [PMID: 36791682 PMCID: PMC10937040 DOI: 10.1159/000529701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
The expanding digitalization of routine diagnostic histological slides holds a potential to apply artificial intelligence (AI) to pathology, including bone marrow (BM) histology. In this perspective, we describe potential tasks in diagnostics that can be supported, investigations that can be guided, and questions that can be answered by the future application of AI on whole-slide images of BM biopsies. These range from characterization of cell lineages and quantification of cells and stromal structures to disease prediction. First glimpses show an exciting potential to detect subtle phenotypic changes with AI that are due to specific genotypes. The discussion is illustrated by examples of current AI research using BM biopsy slides. In addition, we briefly discuss current challenges for implementation of AI-supported diagnostics.
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Affiliation(s)
- Leander van Eekelen
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
- Computational Pathology Group, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
- Computational Pathology Group, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Konnie M. Hebeda
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
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