Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna.
deep learning
hazard assessment
image analysis
machine learning
nanoinformatics
Journal
Small (Weinheim an der Bergstrasse, Germany)
ISSN: 1613-6829
Titre abrégé: Small
Pays: Germany
ID NLM: 101235338
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
20
02
2020
revised:
27
04
2020
accepted:
28
04
2020
pubmed:
18
6
2020
medline:
18
5
2021
entrez:
18
6
2020
Statut:
ppublish
Résumé
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 of Daphnia magna is 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 external D. magna images 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.
Identifiants
pubmed: 32548897
doi: 10.1002/smll.202001080
doi:
Substances chimiques
Water Pollutants, Chemical
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2001080Informations de copyright
© 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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