ArticleBiomedical optics express2022
Label-free, non-invasive, and repeatable cell viability bioassay using dynamic full-field optical coherence microscopy and supervised machine learning.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 23 citations in OpenAlex.
- Regularized Single-Cell Imaging Enables Generalizable AI Models for Stain-Free Cell Viability Screening.Small methods · 2026Article
- Longitudinal investigation of prostate tumor spheroid proliferation with dynamic line-field optical coherence tomography.Biomedical optics express · 2026Article
- A knowledge-driven deep learning framework for organoid morphological segmentation and characterization.BMC biology · 2025Article
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Review
- Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning.Journal of pharmaceutical sciences · 2024Article
- Line-field dynamic optical coherence tomography platform for volumetric assessment of biological tissues.Biomedical optics express · 2024Article
- Dynamic contrast optical coherence tomography (DyC-OCT) for label-free live cell imaging.Communications biology · 2024Review
- Stem cell-based approaches for developmental neurotoxicity testing.Frontiers in toxicology · 2024Review
- Dynamic full-field optical coherence tomography module adapted to commercial microscopes allows longitudinal in vitro cell culture study.Communications biology · 2023Article
- Dynamic optical coherence tomography for cell analysis [Invited].Biomedical optics express · 2023Review
- Biological Cells as Natural Biophotonic Devices: Fundamental and Applications-introduction to the feature issue.Biomedical optics express · 2022Article
- Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.