Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis

Precision medicine for tumors represents a pivotal focus in contemporary medical research. Nonetheless, the diversity of tumor types and the complexity of their pathogenesis present significant challenges in the diagnostic process. Extracellular vesicles (EVs), as a category of nanoparticles, carry a wealth of biological information and play a crucial role in tumor initiation and progression, thereby offering novel approaches for early tumor diagnosis. In recent years, machine learning (ML) technology in the medical field has gained momentum, which utilize various algorithms to analyze input data, identify potential patterns and trends, develop predictive models, and generate high-precision predictions of unknown data, demonstrating its clinical potential in disease diagnosis. This review provides a comprehensive summary of advancements in EVs analysis technology based on ML for auxiliary tumor diagnosis, including early diagnosis, classification, stage recognition, and molecular diagnosis, and discusses their advantages in clinical applications. Additionally, the article anticipates future development trends in the field, aiming to serve as a reference for researchers engaged in ML-assisted liquid biopsy for tumor diagnosis.