文章：

整合人工智能与机器学习于骨髓增生异常综合征诊断：前沿进展与未来展望

Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects

原文发布日期：22 December 2023

DOI: 10.3390/cancers16010065

类型: Article

开放获取: 是

英文摘要：

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.

摘要翻译： 

骨髓增生异常综合征（MDS）是一组由功能异常的干细胞引发的异质性血液系统恶性肿瘤，可导致造血功能异常及血细胞减少。约30%的MDS病例会进展为侵袭性更强的急性髓系白血病（AML）。早期检测对于在MDS进展为AML前进行干预至关重要。目前MDS的诊断流程需综合分析外周血涂片、骨髓样本和流式细胞术数据，并结合临床患者信息，这一过程耗时费力。机器学习技术的最新进展为MDS的快速、自动化及精准诊断提供了新机遇。本文综述旨在系统阐述人工智能在MDS诊断中的应用现状，并重点分析其优势、局限性与性能指标。

原文链接：

Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects