Medical School, University of Jordan, Amman, Jordan.
Department of Internal Medicine, King Hussein Cancer Center, Amman, Jordan.
Curr Hematol Malig Rep. 2024 Feb;19(1):9-17. doi: 10.1007/s11899-023-00716-5. Epub 2023 Nov 24.
This review aims to elucidate the transformative impact and potential of machine learning (ML) in the diagnosis, prognosis, and clinical management of myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). It further aims to bridge the gap between current advances of ML and their practical application in these diseases.
Recent advances in ML have revolutionized prognostication, diagnosis, and treatment of MDS and AML. ML algorithms have proven effective in predicting disease progression, optimizing treatment responses, and in the stratification of patient groups. Particularly, the use of ML in genomic and epigenomic data analysis has unveiled novel insights into the molecular heterogeneity of MDS and AML, leading to better-informed therapeutic strategies. Furthermore, deep learning techniques have shown promise in analyzing complex patterns in bone marrow biopsy images, providing a potential pathway towards early and accurate diagnosis. While still in the nascent stages, ML applications in MDS and AML signify a paradigm shift towards precision medicine. The integration of ML with traditional clinical practices could potentially enhance diagnostic accuracy, refine risk stratification, and improve therapeutic approaches. However, challenges related to data privacy, standardization, and algorithm interpretability must be addressed to realize the full potential of ML in this field. Future research should focus on the development of robust, transparent ML models and their ethical implementation in clinical settings.
综述目的：本综述旨在阐明机器学习（ML）在骨髓增生异常综合征（MDS）和急性髓系白血病（AML）的诊断、预后和临床管理中的变革性影响和潜力。它还旨在弥合 ML 现有进展与其在这些疾病中的实际应用之间的差距。
最新发现：ML 的最新进展彻底改变了 MDS 和 AML 的预后、诊断和治疗。ML 算法在预测疾病进展、优化治疗反应以及对患者群体进行分层方面已被证明是有效的。特别是，ML 在基因组和表观基因组数据分析中的应用揭示了 MDS 和 AML 分子异质性的新见解，从而为制定更明智的治疗策略提供了依据。此外，深度学习技术在分析骨髓活检图像中的复杂模式方面显示出了潜力，为早期和准确诊断提供了一种潜在途径。尽管仍处于起步阶段，但 ML 在 MDS 和 AML 中的应用标志着向精准医学的范式转变。将 ML 与传统临床实践相结合，有可能提高诊断准确性、完善风险分层并改善治疗方法。然而，必须解决与数据隐私、标准化和算法可解释性相关的挑战，以充分发挥 ML 在该领域的潜力。未来的研究应侧重于开发稳健、透明的 ML 模型，并在临床环境中对其进行伦理实施。
