Highly quantitative, robust, single-cell analyses can help to unravel disease heterogeneity and lead to clinical insights, particularly for complex and chronic diseases. Advances in computer vision and machine learning can empower label-free cell-based diagnostics to capture subtle disease states.
References
Stavrakis, S., Holzner, G., Choo, J. & deMello, A. Curr. Opin. Biotechnol. 55, 36–43 (2018).
Shields, C. W. IV, Reyes, C. D. & López, G. P. Lab Chip 15, 1230–1249 (2015).
Gholizadeh, S. et al. Biosens. Bioelectron. 91, 588–605 (2017).
Reátegui, E. et al. Nat. Commun. 9, 175 (2018).
Djuric, U., Zadeh, G., Aldape, K. & Diamandis, P. npj Precis Oncol. 1, 22 (2017).
Murphy, R. F. Nat. Chem. Biol. 7, 327–330 (2011).
Ching, T. et al. J. R. Soc. Interface 15, 20170387 (2018).
LeCun, Y., Bengio, Y. & Hinton, G. Nature 521, 436–444 (2015).
Blasi, T. et al. Nat. Commun. 7, 10256 (2016).
Hennig, H. et al. Methods 112, 201–210 (2017).
Eulenberg, P. et al. Nat. Commun. 8, 463 (2017).
Coudray, N. et al. Nat. Med. 24, 1559–1567 (2018).
Christiansen, E. M. et al. Cell 173, 792–803 (2018).
Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Nat. Methods 15, 917–920 (2018).
Damond, N. et al. Cell Metab. 29, 755–768 (2019).
Brasko, C. et al. Nat. Commun. 9, 226 (2018).
Ota, S. et al. Science 360, 1246–1251 (2018).
Nitta, N. et al. Cell 175, 266–276 (2018).
June, C. H., O’Connor, R. S., Kawalekar, O. U., Ghassemi, S. & Milone, M. C. Science 359, 1361–1365 (2018).
Snodgrass, R. et al. Nat. Biom. Eng. 2, 657–665 (2018).
Lan, F., Demaree, B., Ahmed, N. & Abate, A. R. Nat. Biotechnol. 35, 640–646 (2017).
Ma, S., Murphy, T. W. & Lu, C. Biomicrofluidics 11, 021501 (2017).
Freudiger, C. W. et al. Science 322, 1857–1861 (2008).
Orringer, D. A. et al. Nat. Biomed. Eng. 1, 0027 (2017).
Lei, C. et al. Nat. Protoc. 13, 1603–1631 (2018).
Chen, C. L. et al. Sci. Rep. 6, 21471 (2016).
Castelvecchi, D. Nature 538, 20–23 (2016).
Bau, D., Zhou, B., Khosla, A., Oliva, A. & Torralba, A. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3319–3327 (IEEE, 2017).
Olah, C., Mordvintsev, A. & Schubert, L. Distill 2, e7 (2017).
Topol, E. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again (Basic Books, 2019).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Doan, M., Carpenter, A.E. Leveraging machine vision in cell-based diagnostics to do more with less. Nat. Mater. 18, 414–418 (2019). https://doi.org/10.1038/s41563-019-0339-y
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41563-019-0339-y
- Springer Nature Limited
This article is cited by
-
Classification and counting of cells in brightfield microscopy images: an application of convolutional neural networks
Scientific Reports (2024)
-
Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data
Scientific Reports (2024)
-
Moral exemplars for the virtuous machine: the clinician’s role in ethical artificial intelligence for healthcare
AI and Ethics (2022)
-
Cell morphology-based machine learning models for human cell state classification
npj Systems Biology and Applications (2021)
-
Deep learning of HIV field-based rapid tests
Nature Medicine (2021)