Artificial intelligence-enabled quantitative phase imaging methods for life sciences

J Park, B Bai, DH Ryu, T Liu, C Lee, Y Luo, MJ Lee… - Nature …, 2023 - nature.com
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and
label-free investigation of the physiology and pathology of biological systems. This review …

On the use of deep learning for phase recovery

K Wang, L Song, C Wang, Z Ren, G Zhao… - Light: Science & …, 2024 - nature.com
Phase recovery (PR) refers to calculating the phase of the light field from its intensity
measurements. As exemplified from quantitative phase imaging and coherent diffraction …

[HTML][HTML] Advancing healthcare: synergizing biosensors and machine learning for early cancer diagnosis

M Kokabi, MN Tahir, D Singh, M Javanmard - Biosensors, 2023 - mdpi.com
Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods
for cancer detection often have limitations in identifying the disease in its early stages, and …

Label-free cell classification in holographic flow cytometry through an unbiased learning strategy

G Ciaparrone, D Pirone, P Fiore, L Xin, W Xiao, X Li… - Lab on a Chip, 2024 - pubs.rsc.org
Nowadays, label-free imaging flow cytometry at the single-cell level is considered the
stepforward lab-on-a-chip technology to address challenges in clinical diagnostics, biology …

[HTML][HTML] Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry

D Pirone, A Montella, D Sirico, M Mugnano… - APL …, 2023 - pubs.aip.org
To efficiently tackle certain tumor types, finding new biomarkers for rapid and complete
phenotyping of cancer cells is highly demanded. This is especially the case for the most …

Application of artificial intelligence (AI)-enhanced biochemical sensing in molecular diagnosis and imaging analysis: Advancing and challenges

H Li, H Xu, Y Li, X Li - TrAC Trends in Analytical Chemistry, 2024 - Elsevier
Biochemical sensing plays a vital role in the research of life and natural science. However,
with the increase of environmental complexity and amount of sample, traditional sensors …

Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry

D Pirone, A Montella, DG Sirico, M Mugnano… - Scientific Reports, 2023 - nature.com
Image-based identification of circulating tumor cells in microfluidic cytometry condition is one
of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a …

Screening for urothelial carcinoma cells in urine based on digital holographic flow cytometry through machine learning and deep learning methods

L Xin, X Xiao, W Xiao, R Peng, H Wang, F Pan - Lab on a Chip, 2024 - pubs.rsc.org
The incidence of urothelial carcinoma continues to rise annually, particularly among the
elderly. Prompt diagnosis and treatment can significantly enhance patient survival and …

Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning

L Xin, W Xiao, H Zhang, Y Liu, X Li, P Ferraro… - Sensors and Actuators B …, 2024 - Elsevier
Progress has been made in chemotherapy drugs, but drug resistance remains a major
challenge in cancer treatment. In clinical practice, although there are existing technologies …

Phase flow cytometry with coherent modulation imaging

A Sun, X He, Z Jiang, Y Kong, S Wang… - Journal of …, 2023 - Wiley Online Library
Label‐free imaging and identification of fast‐moving cells is a very challenging task. A kind
of phase flow cytometry using coherent modulation imaging was proposed to realize label …