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Open AccessArticle

ZF-AutoML: An Easy Machine-Learning-Based Method to Detect Anomalies in Fluorescent-Labelled Zebrafish

1
Department of Integrative Pharmacology, Mie University Graduate School of Medicine, Tsu, Mie 514-8507, Japan
2
RT Corporation, Chiyoda ku, Tokyo 101-0021, Japan
3
Graduate School of Regional Innovation Studies, Mie University, Tsu, Mie 514-8507, Japan
4
Mie University Zebrafish Drug Screening Center, Tsu, Mie 514-8507, Japan
5
Department of Bioinformatics, Mie University Advanced Science Research Promotion Center, Tsu, Mie 514-8507, Japan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Inventions 2019, 4(4), 72; https://doi.org/10.3390/inventions4040072
Received: 6 November 2019 / Revised: 10 December 2019 / Accepted: 12 December 2019 / Published: 17 December 2019
Background: Zebrafish are efficient animal models for conducting whole organism drug testing and toxicological evaluation of chemicals. They are frequently used for high-throughput screening owing to their high fecundity. Peripheral experimental equipment and analytical software are required for zebrafish screening, which need to be further developed. Machine learning has emerged as a powerful tool for large-scale image analysis and has been applied in zebrafish research as well. However, its use by individual researchers is restricted due to the cost and the procedure of machine learning for specific research purposes. Methods: We developed a simple and easy method for zebrafish image analysis, particularly fluorescent labelled ones, using the free machine learning program Google AutoML. We performed machine learning using vascular- and macrophage-Enhanced Green Fluorescent Protein (EGFP) fishes under normal and abnormal conditions (treated with anti-angiogenesis drugs or by wounding the caudal fin). Then, we tested the system using a new set of zebrafish images. Results: While machine learning can detect abnormalities in the fish in both strains with more than 95% accuracy, the learning procedure needs image pre-processing for the images of the macrophage-EGFP fishes. In addition, we developed a batch uploading software, ZF-ImageR, for Windows (.exe) and MacOS (.app) to enable high-throughput analysis using AutoML. Conclusions: We established a protocol to utilize conventional machine learning platforms for analyzing zebrafish phenotypes, which enables fluorescence-based, phenotype-driven zebrafish screening. View Full-Text
Keywords: artificial intelligence; fluorophores; in vivo screening artificial intelligence; fluorophores; in vivo screening
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Sawaki, R.; Sato, D.; Nakayama, H.; Nakagawa, Y.; Shimada, Y. ZF-AutoML: An Easy Machine-Learning-Based Method to Detect Anomalies in Fluorescent-Labelled Zebrafish. Inventions 2019, 4, 72.

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