โก Quick Summary
This review highlights the transformative role of machine learning (ML) in managing acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS), showcasing its applications in diagnosis, prognostication, and treatment prediction. The findings suggest that ML technologies can significantly enhance clinical decision-making and patient outcomes.
๐ Key Details
- ๐ Focus: Applications of ML in AML and MDS
- ๐งฉ Techniques: Deep learning, unsupervised clustering, and neural networks
- โ๏ธ Diagnostic Performance: High sensitivity and specificity compared to conventional methods
- ๐ Prognostic Models: Dynamic, personalized survival predictions
- ๐ Research Innovations: Generative approaches for synthetic cohorts and digital twins
๐ Key Takeaways
- ๐ฌ ML enhances diagnostic accuracy in AML and MDS through advanced imaging analysis.
- ๐ Prognostic models provide personalized survival predictions, improving patient management.
- ๐ ML predicts treatment responses to hypomethylating agents and venetoclax-based regimens.
- ๐ ๏ธ Research tools like synthetic cohorts facilitate trial design and data analysis.
- ๐ Future integration into clinical practice requires validation and regulatory oversight.
- ๐ Potential for improved patient outcomes through enhanced decision-making processes.
๐ Background
Myeloid malignancies, including AML and MDS, pose significant challenges in hematology due to their complex nature and variable patient outcomes. Traditional diagnostic and prognostic methods often fall short in providing timely and accurate information. The advent of machine learning offers a promising avenue to refine these processes, potentially leading to better patient management and treatment strategies.
๐๏ธ Study
This review synthesizes current research on the applications of ML in AML and MDS, focusing on its role in diagnostics, prognostication, and treatment prediction. The authors discuss various ML techniques, including deep learning and unsupervised clustering, and their impact on disease classification and patient outcomes.
๐ Results
The application of ML in diagnostics has demonstrated high sensitivity and specificity, surpassing traditional methods. Additionally, ML-driven models have successfully identified genomic subtypes with prognostic significance, enabling more tailored treatment approaches. The integration of these technologies into clinical workflows is anticipated to enhance decision-making and patient care.
๐ Impact and Implications
The findings from this review underscore the potential of ML to revolutionize the management of AML and MDS. By improving diagnostic accuracy and enabling personalized treatment strategies, ML can significantly enhance patient outcomes. However, successful integration into clinical practice will require ongoing validation, development of explainable algorithms, and adherence to regulatory standards to ensure safety and equity in patient care.
๐ฎ Conclusion
This review highlights the transformative potential of machine learning in the field of myeloid malignancies. As we move forward, the integration of these advanced technologies into clinical practice promises to improve diagnostic and treatment paradigms, ultimately leading to better patient outcomes. Continued research and collaboration will be essential to fully realize this potential.
๐ฌ Your comments
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Artificial intelligence in myeloid malignancies: Clinical applications of machine learning in myelodysplastic syndromes and acute myeloid Leukemia.
Abstract
This review summarizes applications of machine learning (ML) in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS), spanning diagnosis, prognostication, treatment prediction, and research tools. In diagnostics, deep learning applied to bone marrow smears, peripheral blood films, and flow cytometry has shown high sensitivity and specificity, outperforming conventional methods. ML-driven unsupervised clustering and consensus classification have refined disease taxonomies, identifying genomic subtypes with prognostic value. Prognostic models and neural networks enable dynamic, personalized survival predictions. In treatment, ML assists in predicting responses to hypomethylating agents and venetoclax-based regimens, supporting clinical decision-making. In research, generative approaches create privacy-preserving synthetic cohorts and digital twins, facilitating trial design and overcoming data limitations. Future integration into clinical practice will require rigorous validation, explainable algorithms, seamless workflow incorporation, and regulatory oversight to ensure trust, equity, and safety. ML has potential to enhance multiple aspects of AML and MDS management.
Author: [‘Al-Nusair J’, ‘Lanino L’, ‘Durmaz A’, ‘Porta MGD’, ‘Zeidan AM’, ‘Kewan T’]
Journal: Blood Rev
Citation: Al-Nusair J, et al. Artificial intelligence in myeloid malignancies: Clinical applications of machine learning in myelodysplastic syndromes and acute myeloid Leukemia. Artificial intelligence in myeloid malignancies: Clinical applications of machine learning in myelodysplastic syndromes and acute myeloid Leukemia. 2025; (unknown volume):101340. doi: 10.1016/j.blre.2025.101340