Journal
SMALL
Volume 16, Issue 36, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/smll.202001080
Keywords
deep learning; hazard assessment; image analysis; machine learning; nanoinformatics
Categories
Funding
- European Union's Horizon 2020 research and innovation programme via NanoSolveIT Project [814572, NE/N006569/1]
- NERC [NE/N006569/1] Funding Source: UKRI
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This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images ofDaphnia magnais proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on externalD.magnaimages and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time-consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long-term reproductive toxicity assays over multiple generations.
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