Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology

• Published in: Biosensors (Basel) Vol. 13; no. 12; p. 993
• Main Authors: Baik, Minyoung, Shin, Sanghoon, Kumar, Samir, Seo, Dongmin, Lee, Inha, Jun, Hyun Sik, Kang, Ka-Won, Kim, Byung Soo, Nam, Myung-Hyun, Seo, Sungkyu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 21.11.2023
• Subjects: Accuracy; Analysis; Antigens, CD34 - analysis; Artificial intelligence; Blood; Bone marrow; Care and treatment; CD34 antigen; CD34+ classification; Cell differentiation; Cellytics; Centrifugation; Classification; Data analysis; Datasets; Deep Learning; Diagnosis; Flow Cytometry; Fluorescence; Glycoproteins; Hematopoietic stem cells; Humans; label-free cytometry; Labels; lens-free shadow imaging; Lenses; Leukemia; Leukocytes; Leukocytes (mononuclear); Machine learning; Medical imaging; Methods; Morphology; Neural networks; Patients; Peripheral blood; Reproducibility of Results; Risk reduction; Sample preparation; Shadows; Staining; Stem cells; Technology; Transplantation; Transplants & implants; South Korea; deep learning; label-free cytometry; leukemia diagnosis; point-of-care diagnosis; Cellytics; CD34+ classification; lens-free shadow imaging; artificial intelligence
• ISSN: 2079-6374; 2079-6374
• DOI: 10.3390/bios13120993

Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, such as flow cytometry, are complex, time-consuming, and require specialized expertise and equipment. This study proposes a novel approach for the label-free identification of CD34+ cells using a deep learning model and lens-free shadow imaging technology (LSIT). LSIT is a portable and user-friendly technique that eliminates the need for cell staining, enhances accessibility to nonexperts, and reduces the risk of sample degradation. The study involved three phases: sample preparation, dataset generation, and data analysis. Bone marrow and peripheral blood samples were collected from leukemia patients, and mononuclear cells were isolated using Ficoll density gradient centrifugation. The samples were then injected into a cell chip and analyzed using a proprietary LSIT-based device (Cellytics). A robust dataset was generated, and a custom AlexNet deep learning model was meticulously trained to distinguish CD34+ from non-CD34+ cells using the dataset. The model achieved a high accuracy in identifying CD34+ cells from 1929 bone marrow cell images, with training and validation accuracies of 97.3% and 96.2%, respectively. The customized AlexNet model outperformed the Vgg16 and ResNet50 models. It also demonstrated a strong correlation with the standard fluorescence-activated cell sorting (FACS) technique for quantifying CD34+ cells across 13 patient samples, yielding a coefficient of determination of 0.81. Bland-Altman analysis confirmed the model's reliability, with a mean bias of -2.29 and 95% limits of agreement between 18.49 and -23.07. This deep-learning-powered LSIT offers a groundbreaking approach to detecting CD34+ cells without the need for cell staining, facilitating rapid CD34+ cell classification, even by individuals without prior expertise.