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

Nature Materials volume 18, pages 414–418 (2019)Cite this article

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.

At the same time, the medical community recognizes that disease heterogeneity is a major challenge obscuring accurate diagnosis and effective treatment. Precision medicine, where relatively specific treatments are tailored to each patient based on their characteristics, requires new diagnostics that can classify patients by disease subtype and by response to a given treatment regime. This is particularly relevant for complex illnesses such as autoimmune diseases and cancers, and for chronic diseases that progress over time, such as diabetes and obesity. These illnesses show substantial patient-to-patient variability in terms of symptoms, genetic underpinnings and progression. Thus, a major challenge facing medicine is to develop diagnostics that reveal this heterogeneity: a given therapeutic may only be effective when it ‘matches’ the right patient, or even the right subclone of cells within a given patient, and response must be monitored over time.

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Fig. 1: Valuable information is hidden in label-free images.
Fig. 2: AI-powered point-of-care diagnostics usher in the era of precision medicine.

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Authors and Affiliations

  1. Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Minh Doan & Anne E. Carpenter

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  2. Anne E. Carpenter
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Correspondence to Minh Doan.

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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

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