Image recognition based on deep learning in Haemonchus contortus motility assays
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11160%2F22%3A10450813" target="_blank" >RIV/00216208:11160/22:10450813 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=kDJ8L4IKjP" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=kDJ8L4IKjP</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.csbj.2022.05.014" target="_blank" >10.1016/j.csbj.2022.05.014</a>
Alternative languages
Result language
angličtina
Original language name
Image recognition based on deep learning in Haemonchus contortus motility assays
Original language description
Poor efficacy of some anthelmintics and rising concerns about the widespread drug resistance have highlighted the need for new drug discovery. The parasitic nematode Haemonchus contortus is an important model organism widely used for studies of drug resistance and drug screening with the current gold standard being the motility assay. We applied a deep learning approach Mask R-CNN for analysing motility videos containing varying rates of motile worms and compared it to other commonly used algorithms with different levels of complexity, namely the Wiggle Index and the Wide Field-of-View Nematode Tracking Platform. Mask R-CNN consistently outperformed the other algorithms in terms of the detection of worms as well as the precision of motility forecasts, having a mean absolute percentage error of 7.6% and a mean absolute error of 5.6% for the detection and motility forecasts, respectively. Using Mask R-CNN for motility assays confirmed the common problem with algorithms that use non-maximum suppression in detecting overlapping objects, which negatively impacts the overall precision. The use of intersect over union as a measure of the classification of motile / non-motile instances had an overall accuracy of 89%, indicating that it is a viable alternative to previously used methods based on movement characteristics, such as body bends. In comparison to the existing methods evaluated here, Mask R-CNN performed better and we anticipate that this method will broaden the number of possible approaches to video analysis of worm motility.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30104 - Pharmacology and pharmacy
Result continuities
Project
<a href="/en/project/EF16_019%2F0000841" target="_blank" >EF16_019/0000841: Efficiency and safety improvement of current drugs and nutraceuticals: advanced methods - new challenges</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computational and Structural Biotechnology Journal
ISSN
2001-0370
e-ISSN
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Volume of the periodical
20
Issue of the periodical within the volume
May
Country of publishing house
SE - SWEDEN
Number of pages
9
Pages from-to
2372-2380
UT code for WoS article
000805642000003
EID of the result in the Scopus database
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