Introduction:

Acute Lymphoblastic Leukemia (ALL) is a malignancy of lymphoid progenitor cells with poor prognosis in adults with chromosomal abnormalities. Understanding the heterogeneity in ALL is critical for developing new therapies to treat the disease. Here we used VisionSort to characterize the morphological diversity in ALL. 

Summary:

1. The VisionSort platform was used to identify morphological diversity in ALL
2. An AI-driven approach was used to identify ALL subpopulations based on morphology
3. The approach is reproducible with low standard error. Unsupervised machine learning was used to confirm identification of disease-specific cells and discover novel subpopulations in ALL.

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