Label-free, non-invasive, and repeatable cell viability bioassay using dynamic full-field optical coherence microscopy and supervised machine learning.
Soongho ParkVinay VeluvoluWilliam S MartinThien NguyenJinho ParkDan L SackettClaude BoccaraAmir GandjbakhchePublished in: Biomedical optics express (2022)
We present a novel method that can assay cellular viability in real-time using supervised machine learning and intracellular dynamic activity data that is acquired in a label-free, non-invasive, and non-destructive manner. Cell viability can be an indicator for cytology, treatment, and diagnosis of diseases. We applied four supervised machine learning models on the observed data and compared the results with a trypan blue assay. The cell death assay performance by the four supervised models had a balanced accuracy of 93.92 ± 0.86%. Unlike staining techniques, where criteria for determining viability of cells is unclear, cell viability assessment using machine learning could be clearly quantified.
Keyphrases
- machine learning
- label free
- big data
- cell death
- artificial intelligence
- high throughput
- cell cycle arrest
- electronic health record
- induced apoptosis
- deep learning
- high resolution
- high grade
- high speed
- signaling pathway
- ultrasound guided
- single molecule
- optical coherence tomography
- oxidative stress
- mass spectrometry
- data analysis
- flow cytometry
- clinical evaluation