Artificial intelligence in hematopoietic stem cell research and associated malignancies: From disease modeling to cell manufacturing

Table 3 Summary of key machine learning algorithms, applications, and trade-offs in hematopoietic stem cell research

Machine learning algorithm	Primary HSC/hematology applications	Key advantages (Pros)	Key limitations (Cons)	Ref.
CNN	Morphological classification of HSCs vs MPPs; 3D chromatin age prediction (ChromAgeNet); automated quality control imaging in biomanufacturing	Unparalleled performance on spatial and image data; extracts features autonomously without requiring manual gating or human-defined parameters	Highly opaque “black box” nature requiring XAI for interpretability; demands massive, accurately annotated image datasets to train	[6,11,34]
Random forest/ensemble trees	Flow cytometric LSC phenotyping; early relapse detection; predicting cord blood CD34+ cell yield	Highly robust to overfitting; handles tabular clinical and multi-omics data effectively; naturally provides feature importance rankings	Less effective than deep learning for highly unstructured data (like raw images or free text); can struggle with extrapolating data outside the training range	[22,35]
Gradient Boosting (e.g., XGBoost)	Automated MRD detection in flow cytometry (MAGIC-DR); predicting synergistic drug combinations; venetoclax response prediction	Exceptional predictive accuracy on structured clinical and omics data; handles missing data well; highly scalable for large patient cohorts	Prone to overfitting on very small sample sizes; hyperparameter tuning is complex and computationally expensive	[23,56]
Deep learning/ANN	Multi-omics integration (e.g., totalVI); modeling age-dependent HSC self-renewal; multi-center AML survival prediction	Can capture extremely complex, non-linear biological relationships across massive, high-dimensional datasets (e.g., integrating RNA and protein expression)	Computationally intensive; high risk of learning artifactual batch effects rather than true biology if data is not strictly harmonized	[10,15,17]
NLP	Extracting HLA genotypes from unstructured electronic health records; predicting and phenotyping acute and chronic GVHD from clinical notes	Unlocks vast amounts of unstructured, historical clinical data that is otherwise inaccessible to standard statistical models	Performance is heavily dependent on the quality, consistency, and language of physician documentation; it struggles with implicit clinical context	[40-42]
RL	Dynamic optimization of ex vivo HSC expansion; adaptive control of bioreactor parameters (cytokines, perfusion, metabolic flux)	Enables continuous, autonomous, real-time process optimization without requiring a pre-defined static protocol	Requires highly accurate “digital twins” or simulation environments to train the agent safely; initial validation in GMP environments is regulatory complex	[37,77,78]

HSC: Hematopoietic stem cell; CNN: Convolutional neural network; MPP: Multipotent progenitor; XAI: Explainable artificial intelligence; LSC: Leukemic stem cell; XGBoost: Extreme Gradient Boosting; MRD: Measurable residual disease; MAGIC-DR: Machine-learning guided approach to acute myeloid leukemia measurable residual disease detection in residual disease; ANN: Artificial neural network; AML: Acute myeloid leukemia; NLP: Natural language processing; HLA: Human leukocyte antigen; GVHD: Graft-vs-host disease; RL: Reinforcement learning; GMP: Good Manufacturing Practice.