Elshoeibi Amgad Mohamed, Badr Ahmed, Elsayed Basel, Metwally Omar, Elshoeibi Raghad, Elhadary Mohamed Ragab, Elshoeibi Ahmed, Attya Mohamed Amro, Khadadah Fatima, Alshurafa Awni, Alhuraiji Ahmad, Yassin Mohamed
College of Medicine, QU Health, Qatar University, Doha 2713, Qatar.
College of Medicine, Mansoura University, Mansoura 35516, Egypt.
Cancers (Basel). 2023 Dec 22;16(1):65. doi: 10.3390/cancers16010065.
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.
骨髓增生异常综合征（MDS）由功能失调的干细胞引起的多种血液系统恶性肿瘤组成，导致异常造血和血细胞减少。约30%的MDS病例会进展为急性髓系白血病（AML），这是一种更具侵袭性的疾病。早期检测对于在MDS进展为AML之前进行干预至关重要。目前MDS的诊断过程包括分析外周血涂片（PBS）、骨髓样本（BMS）和流式细胞术（FC）数据，以及临床患者信息，这既费力又耗时。机器学习的最新进展为更快、自动化和准确地诊断MDS提供了机会。在这篇综述中，我们旨在概述人工智能在MDS诊断中的当前应用，并突出其优点、缺点和性能指标。
