ArticlePloS one2015
Phenotype classification of zebrafish embryos by supervised learning.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- A quantitative coordinate system for developmental dynamics.bioRxiv : the preprint server for biology · 2026Article
- Assessment of embryotoxic effects of quinoline yellow using attention-based convolutional neural network and machine learning in zebrafish model.Frontiers in pharmacology · 2025Article
- Deep phenotypic profiling of neuroactive drugs in larval zebrafish.Nature communications · 2024Article
- Automated staging of zebrafish embryos with deep learning.Life science alliance · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species.Biomolecules · 2023Review
- Uncovering developmental time and tempo using deep learning.Nature methods · 2023Article
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- Article
- Effects of Corchorusoside C on NF-κB and PARP-1 Molecular Targets and Toxicity Profile in Zebrafish.International journal of molecular sciences · 2022Article
- Interference with zinc homeostasis and oxidative stress induction as probable mechanisms for cadmium-induced embryo-toxicity in zebrafish.Environmental science and pollution research international · 2022Article
- Automated staging of zebrafish embryos using machine learning.Wellcome open research · 2022Article
- Towards the automation of early-stage human embryo development detection.Biomedical engineering online · 2019Article
- Efficient phenotypic sex classification of zebrafish using machine learning methods.Ecology and evolution · 2019Article
- Automated Morphological Feature Assessment for Zebrafish Embryo Developmental Toxicity Screens.Toxicological sciences : an official journal of the Society of Toxicology · 2019Article
- Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization.NPJ digital medicine · 2019Article
- Article
- Fishing forward and reverse: Advances in zebrafish phenomics.Mechanisms of development · 2018Review
- Zebrafish in Toxicology and Environmental Health.Current topics in developmental biology · 2017Review
- The Need for Careful Data Collection for Pattern Recognition in Digital Pathology.Journal of pathology informatics · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.