Development of an AI algorithm for the automatic detection and classification of rat bone marrow cells

Morphological observation and classification of bone marrow cells in smear specimens is an important examination in toxicity studies for pharmaceuticals. However, acquiring the expertise for classifying bone marrow cells using light microscopy requires years of training, resulting in significant labor and time. To efficiently acquire accurate and objective data without oversight, a system for the automated detection and morphological classification of rat bone marrow cells was developed through machine learning using whole slide images (WSIs) obtained from smear specimens. Our system integrates SSD300 for object detection, VDSR for image super-resolution, and EfficientNetV2B0 for classification. WSIs of rat bone marrow smear specimens were obtained at 40× magnification using a WSI scanner. The fine-tuning of the bone marrow cell detection model using SSD300 was performed with 720 images obtained from WSIs of rat bone marrow smear specimens. The morphological classification model for 13 types of bone marrow cells using VDSR-EfficientNetV2B0 was optimized with a total of 144,000 cell images. The system for detection of bone mallow cells achieved an average precision of 79%. Additionally, the morphological classification achieved an accuracy of 98% when compared to expert classification. Our algorithm enabled the automated classification of cells on rat bone marrow smear specimens with extremely high accuracy and in a short time, approximately 80 sec, to classify 5,000 cells per image, without oversight. This capability suggests that the algorithm could potentially be utilized as a supportive tool for the toxicity evaluation of bone marrow smear specimens.