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AuthorElshoeibi, Amgad Mohamed
AuthorBadr, Ahmed
AuthorElsayed, Basel
AuthorMetwally, Omar
AuthorElshoeibi, Raghad
AuthorElhadary, Mohamed Ragab
AuthorElshoeibi, Ahmed
AuthorAttya, Mohamed Amro
AuthorKhadadah, Fatima
AuthorAlshurafa, Awni
AuthorAlhuraiji, Ahmad
AuthorYassin, Mohamed
Available date2024-02-25T08:53:52Z
Publication Date2023-12-22
Publication NameCancers
Identifierhttp://dx.doi.org/10.3390/cancers16010065
CitationElshoeibi, A. M., Badr, A., Elsayed, B., Metwally, O., Elshoeibi, R., Elhadary, M. R., ... & Yassin, M. (2023). Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects. Cancers, 16(1), 65.
ISSN2072-6694
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85182163714&origin=inward
URIhttp://hdl.handle.net/10576/52178
AbstractMyelodysplastic 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.
SponsorThe open access publication of this article was made possible due to a generous fund from QU Health, Qatar University.
Languageen
PublisherMultidisciplinary Digital Publishing Institute (MDPI)
Subjectartificial intelligence
bone marrow smears
flow cytometry
machine learning
myelodysplastic syndrome diagnosis
peripheral blood smears
TitleIntegrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects
TypeArticle
Issue Number1
Volume Number16
ESSN2072-6694
dc.accessType Open Access


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