Differentiation performance of artificial intelligence models using deep learning in lymphocyte morphology recognition: Potential of artificial intelligence in lymphocytosis analysis

• Published in: Japanese Journal of Medical Technology Vol. 74; no. 2; pp. 293 - 303
• Main Authors: OGASAWARA, Shu, KAWASHIMA, Kentaro, KAMATA, Kosuke, UENO, Hiroki, NOZAKA, Hiroyuki, KUSHIBIKI, Mihoko, ISHIYAMA, Masahiro, YAMAGATA, Kazufumi
• Format: Journal Article
• Language: English; Japanese
• Published: Japanese Association of Medical Technologists 25.04.2025
• Subjects: acute lymphoblastic leukemia; artificial intelligence; convolutional neural network; deep learning; reactive lymphocytosis
• ISSN: 0915-8669; 2188-5346
• DOI: 10.14932/jamt.24-59

Deep learning in artificial intelligence is a method of algorithmically detecting hidden features in data by training on a large amount of data. This method can generate an accurate decision model in the form of a multi-layered neural network inspired by the neural circuits of the brain. Although automated morphology classification requires high accuracy in differentiating various cell types, it has been reported that some conventional systems using machine learning cannot achieve high accuracy for reactive or neoplastic cells. In this study, we developed models for normal–reactive–abnormal lymphocyte differentiation to demonstrate the usefulness of artificial intelligence-assisted technology in blood morphology testing. Five models using residual neural networks were applied to deep learning, and their performance in automated morphological differentiation was evaluated. The original image set for training consisted of 6,402 typical nucleated blood cell images. A data augmentation process was applied to the original images, and transfer learning and fine-tuning were performed on each model. The subjects for clinical assessment were 25 healthy persons, 25 cases of reactive lymphocytosis, and 15 cases of acute lymphoblastic leukemia. The results of clinical assessments showed that the total accuracy ranges were 0.9433–0.9791 for healthy subjects, 0.8108–0.8425 for reactive lymphocytosis, 0.8248–0.8545 for acute lymphoblastic leukemia, and 0.8645–0.8875 overall. Our proposed artificial intelligence model of lymphocyte morphology differentiation using deep learning achieved a high recognition accuracy. We expect that this approach will be beneficial in developing morphological differentiation assistance technology for blood smear screening.