Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects
Date
2023-12-22Author
Elshoeibi, Amgad MohamedBadr, Ahmed
Elsayed, Basel
Metwally, Omar
Elshoeibi, Raghad
Elhadary, Mohamed Ragab
Elshoeibi, Ahmed
Attya, Mohamed Amro
Khadadah, Fatima
Alshurafa, Awni
Alhuraiji, Ahmad
Yassin, Mohamed
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Metadata
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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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