A deep-learning algorithm (AIFORIA) for classification of hematopoietic cells in bone marrow aspirate smears based on nine cell classes-a feasible approach for routine screening?

Abstract

Bone marrow cytology plays a key role for the diagnosis and classification of hematological disease and is often the first step in the acute setting of unclear cytopenia. AI applications represent a powerful tool in digital image analysis and can improve the diagnostic workflow and accuracy. The aim of this study was to develop an algorithm for the automated detection and classification of hematopoietic cells in digitized bone marrow aspirate smears for potential implementation in the clinical laboratory. The AIFORIA create platform (Aiforia Technologies, Plc, Helsinki, Finland) was used to develop a convolutional neural network algorithm based on nine cell classes. Digitized bone marrow aspirate smears from normal hospital controls were used for AI training. External validation was performed on separate data sets. Automated cell classification was assessed in whole-slide images (WSI) and regions of interest (ROI). A total of 1950 single-cell annotations were applied for AI training with a final total class error of 0.15% with 99.9% precision and sensitivity (FI-score 99.2%). External validation showed an overall precision and sensitivity of 96% and 97% and a F1-score of 96%. Automated cell classification correlated highly across ROI with variable correlation to WSI. The average execution time for classifying 500 hematopoietic cells was < 1 s and ≤ 260 s for WSI. A cloud-based, deep-learning algorithm for automated detection and classification of hematopoietic cells in bone marrow aspirate smears is a very useful, reliable, and rapid screening tool in combination with cytomorphology.