Automated bone marrow cytology using deep learning to generate a histogram of cell types

Background Bone marrow cytology is required to make a hematological diagnosis, influencing critical clinical decision points in hematology. However, bone marrow cytology is tedious, limited to experienced reference centers and associated with inter-observer variability. This may lead to a delayed or incorrect diagnosis, leaving an unmet need for innovative supporting technologies. Methods We develop an end-to-end deep learning-based system for automated bone marrow cytology. Starting with a bone marrow aspirate digital whole slide image, our system rapidly and automatically detects suitable regions for cytology, and subsequently identifies and classifies all bone marrow cells in each region. This collective cytomorphological information is captured in a representation called Histogram of Cell Types (HCT) quantifying bone marrow cell class probability distribution and acting as a cytological patient fingerprint. Results Our system achieves high accuracy in region detection (0.97 accuracy and 0.99 ROC AUC), and cell detection and cell classification (0.75 mean average precision, 0.78 average F1-score, Log-average miss rate of 0.31). Conclusions HCT has potential to eventually support more efficient and accurate diagnosis in hematology, supporting AI-enabled computational pathology. Plain language summary Identifying and counting cells in bone marrow samples, known as cytology, is critical for the diagnosis of blood disorders. This is a complex and labor-intensive process, with some variation in how hematopathologists interpret these samples. Here, we develop an artificial intelligence system for automated bone marrow cytology, which automatically detects and identifies all types of cells found in the bone marrow. This information is summarized in a chart that we call the Histogram of Cell Types (HCT), a new way to represent complex information generated in bone marrow cytology. Our system achieves high accuracy and precision in classifying the different types of bone marrow cells as a HCT. This tool may eventually help clinicians to make more efficient and accurate diagnoses. Moosavi Tayebi et al. develop a deep learning-based computational pathology tool for automated bone marrow cytology from whole slide images. Their approach generates a histogram of cell types present within the bone marrow aspirate to aid in diagnostic haematopathology.