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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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Elsayed B, Elhadary M, Elshoeibi RM, Elshoeibi AM, Badr A, Metwally O, ElSherif RA, Salem ME, Khadadah F, Alshurafa A, Mudawi D, Yassin M. Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images. Front Oncol 2023; 13:1330977. [PMID: 38125946 PMCID: PMC10731043 DOI: 10.3389/fonc.2023.1330977] [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: 10/31/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
Acute lymphoblastic leukemia (ALL) poses a significant health challenge, particularly in pediatric cases, requiring precise and rapid diagnostic approaches. This comprehensive review explores the transformative capacity of deep learning (DL) in enhancing ALL diagnosis and classification, focusing on bone marrow image analysis. Examining ten studies conducted between 2013 and 2023 across various countries, including India, China, KSA, and Mexico, the synthesis underscores the adaptability and proficiency of DL methodologies in detecting leukemia. Innovative DL models, notably Convolutional Neural Networks (CNNs) with Cat-Boosting, XG-Boosting, and Transfer Learning techniques, demonstrate notable approaches. Some models achieve outstanding accuracy, with one CNN reaching 100% in cancer cell classification. The incorporation of novel algorithms like Cat-Swarm Optimization and specialized CNN architectures contributes to superior classification accuracy. Performance metrics highlight these achievements, with models consistently outperforming traditional diagnostic methods. For instance, a CNN with Cat-Boosting attains 100% accuracy, while others hover around 99%, showcasing DL models' robustness in ALL diagnosis. Despite acknowledged challenges, such as the need for larger and more diverse datasets, these findings underscore DL's transformative potential in reshaping leukemia diagnostics. The high numerical accuracies accentuate a promising trajectory toward more efficient and accurate ALL diagnosis in clinical settings, prompting ongoing research to address challenges and refine DL models for optimal clinical integration.
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Affiliation(s)
| | | | | | | | - Ahmed Badr
- College of Medicine, Qatar University, Doha, Qatar
| | | | | | | | - Fatima Khadadah
- Cancer Genetics Lab, Kuwait Cancer Control Centre, Kuwait City, Kuwait
| | - Awni Alshurafa
- Department of Medical Oncology, National Center for Cancer Care and Research, Doha, Qatar
| | - Deena Mudawi
- Department of Medical Oncology, National Center for Cancer Care and Research, Doha, Qatar
| | - Mohamed Yassin
- College of Medicine, Qatar University, Doha, Qatar
- Department of Medical Oncology, National Center for Cancer Care and Research, Doha, Qatar
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Elsabagh AA, Elhadary M, Elsayed B, Elshoeibi AM, Ferih K, Kaddoura R, Alkindi S, Alshurafa A, Alrasheed M, Alzayed A, Al-Abdulmalek A, Altooq JA, Yassin M. Artificial intelligence in sickle disease. Blood Rev 2023; 61:101102. [PMID: 37355428 DOI: 10.1016/j.blre.2023.101102] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/12/2023] [Accepted: 06/01/2023] [Indexed: 06/26/2023]
Abstract
Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and clinical practice in numerous medical fields. Its implications have been rising and are being widely used in research, diagnostics, and treatment options for many pathologies, including sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing SCD complications early, allowing rapid intervention and reallocation of resources to high-risk patients. We reviewed the literature for established and new AI applications that may enhance management of SCD through advancements in diagnosing SCD and its complications, risk stratification, and the effect of AI in establishing an individualized approach in managing SCD patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in clinical practice for improving the management for SCD cases.
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Affiliation(s)
| | | | - Basel Elsayed
- College of Medicine, QU Health, Qatar University, Doha, Qatar
| | | | - Khaled Ferih
- College of Medicine, QU Health, Qatar University, Doha, Qatar
| | - Rasha Kaddoura
- Pharmacy Department, Heart Hospital, Hamad Medical Corporation (HMC), Doha, Qatar
| | - Salam Alkindi
- Professor of Hematology, Sultan Qaboos University, Oman
| | - Awni Alshurafa
- Department of Hematology, Hamad Medical Corporation (HMC), Doha, Qatar
| | - Mona Alrasheed
- Hematology Unit, Department of Medicine, Aladnan Hospital, Ministry of Health, Kuwait
| | | | | | | | - Mohamed Yassin
- Hematology Section, Medical Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar.
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