Leveraging machine vision in cell-based diagnostics to do more with less

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.

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Bibliographische Detailangaben
Veröffentlicht in:	Nature materials 2019-05, Vol.18 (5), p.414-418
Hauptverfasser:	Doan, Minh, Carpenter, Anne E.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Automation Biomarkers Biomaterials Biopsy Cellular biology Chemistry and Materials Science Chronic illnesses Comment Computer vision Condensed Matter Physics Cytological Techniques Deep learning Diagnosis, Computer-Assisted Diagnostic Imaging Diagnostic Techniques and Procedures Digital cameras Disease Flow cytometry Heterogeneity Humans Image Processing, Computer-Assisted Machine Learning Machine vision Materials Science Microfluidics Microscopy Nanotechnology Neural Networks (Computer) Ophthalmology Optical and Electronic Materials Pattern recognition Phenotype Precision Medicine - methods Prognosis Reagents Single-Cell Analysis Software Vision systems
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Beschreibung
Zusammenfassung:	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.
ISSN:	1476-1122 1476-4660
DOI:	10.1038/s41563-019-0339-y