Unlocking the Potential of Artificial Intelligence in Acute Myeloid Leukemia and Myelodysplastic Syndromes.

IF 2.7 3区 医学 Q2 HEMATOLOGY
Current Hematologic Malignancy Reports Pub Date : 2024-02-01 Epub Date: 2023-11-24 DOI:10.1007/s11899-023-00716-5
Abdulrahman Alhajahjeh, Aziz Nazha
{"title":"Unlocking the Potential of Artificial Intelligence in Acute Myeloid Leukemia and Myelodysplastic Syndromes.","authors":"Abdulrahman Alhajahjeh, Aziz Nazha","doi":"10.1007/s11899-023-00716-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of the review: </strong>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.</p><p><strong>Recent findings: </strong>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.</p>","PeriodicalId":10852,"journal":{"name":"Current Hematologic Malignancy Reports","volume":" ","pages":"9-17"},"PeriodicalIF":2.7000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Hematologic Malignancy Reports","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11899-023-00716-5","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/11/24 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"HEMATOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose of the review: 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 findings: 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.

释放人工智能在急性髓性白血病和骨髓增生异常综合征中的潜力。
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)和急性髓性白血病(AML)的诊断、预后和临床管理中的变革性影响和潜力。它进一步旨在弥合当前ML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算法已被证明在预测疾病进展、优化治疗反应和患者群体分层方面是有效的。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,从而导致更好的治疗策略。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前景,为早期和准确诊断提供了潜在途径。虽然ML在MDS和AML中的应用仍处于起步阶段,但它标志着向精准医学的范式转变。ML与传统临床实践的结合可以潜在地提高诊断的准确性,完善风险分层,改进治疗方法。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,以实现机器学习在该领域的全部潜力。未来的研究应侧重于开发稳健、透明的机器学习模型及其在临床环境中的伦理实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
6.00
自引率
0.00%
发文量
28
审稿时长
>12 weeks
期刊介绍: his journal intends to provide clear, insightful, balanced contributions by international experts that review the most important, recently published clinical findings related to the diagnosis, treatment, management, and prevention of hematologic malignancy. We accomplish this aim by appointing international authorities to serve as Section Editors in key subject areas, such as leukemia, lymphoma, myeloma, and T-cell and other lymphoproliferative malignancies. Section Editors, in turn, select topics for which leading experts contribute comprehensive review articles that emphasize new developments and recently published papers of major importance, highlighted by annotated reference lists. An international Editorial Board reviews the annual table of contents, suggests articles of special interest to their country/region, and ensures that topics are current and include emerging research. Commentaries from well-known figures in the field are also provided.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信