TY - JOUR
T1 - An integrated microflow cytometry platform with artificial intelligence capabilities for point-of-care cellular phenotype analysis
AU - Kuo, Ju Nan
AU - Jian, Ming Shen
AU - Chiang, Chia Huang
AU - Kuo, Wen Kai
AU - Lin, I. En
AU - Kuo, Yung Ming
AU - Chen, Chung Yu
AU - Ye, Yi Ling
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2025/3/1
Y1 - 2025/3/1
N2 - The EZ DEVICE is an integrated fluorescence microflow cytometer designed for automated cell phenotyping and enumeration using artificial intelligence (AI). The platform consists of a laser diode, optical filter, objective lens, CMOS image sensor, and microfluidic chip, enabling automated sample pretreatment, labeling, and detection within a single compact unit. AI algorithms segment and identify objects in images captured by the CMOS sensor at 532 and 586 nm emission wavelengths. The device's counting performance, tested with rainbow and FITC-labeled beads, closely matched results from manual counting using an Olympus IX73 microscope. Antibody-staining efficiency was evaluated with antibody-particle beads and IgG k isotype-FITC, achieving a 99.06% staining efficiency. Practical feasibility was demonstrated through the phenotyping of immune cell lines (Jurkat T and THP-1) using specific fluorescent antibodies. The system successfully detected MHC I expression and distinguished CD3 expression patterns in Jurkat T-cells, demonstrating its capability to recognize distinct cell statuses. With its compact design, automated features, and efficient staining and detection, the EZ DEVICE shows promise for diverse applications, including clinical diagnostics, point-of-care testing, and research, enabling real-time immune monitoring and precise cell analysis. Future enhancements, such as optimized microfluidics, advanced imaging, and AI-driven algorithms, aim to improve single-cell throughput and expand its utility in personalized medicine and public health.
AB - The EZ DEVICE is an integrated fluorescence microflow cytometer designed for automated cell phenotyping and enumeration using artificial intelligence (AI). The platform consists of a laser diode, optical filter, objective lens, CMOS image sensor, and microfluidic chip, enabling automated sample pretreatment, labeling, and detection within a single compact unit. AI algorithms segment and identify objects in images captured by the CMOS sensor at 532 and 586 nm emission wavelengths. The device's counting performance, tested with rainbow and FITC-labeled beads, closely matched results from manual counting using an Olympus IX73 microscope. Antibody-staining efficiency was evaluated with antibody-particle beads and IgG k isotype-FITC, achieving a 99.06% staining efficiency. Practical feasibility was demonstrated through the phenotyping of immune cell lines (Jurkat T and THP-1) using specific fluorescent antibodies. The system successfully detected MHC I expression and distinguished CD3 expression patterns in Jurkat T-cells, demonstrating its capability to recognize distinct cell statuses. With its compact design, automated features, and efficient staining and detection, the EZ DEVICE shows promise for diverse applications, including clinical diagnostics, point-of-care testing, and research, enabling real-time immune monitoring and precise cell analysis. Future enhancements, such as optimized microfluidics, advanced imaging, and AI-driven algorithms, aim to improve single-cell throughput and expand its utility in personalized medicine and public health.
UR - https://www.scopus.com/pages/publications/85214311165
UR - https://www.scopus.com/pages/publications/85214311165#tab=citedBy
U2 - 10.1016/j.bios.2024.117074
DO - 10.1016/j.bios.2024.117074
M3 - Article
C2 - 39718483
AN - SCOPUS:85214311165
SN - 0956-5663
VL - 271
JO - Biosensors and Bioelectronics
JF - Biosensors and Bioelectronics
M1 - 117074
ER -