ReviewJournal of toxicology2018
Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress.
Review in Journal of toxicology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Tilapia Feeding Behavior Image Dataset: A Benchmark Resource for Automated Feeding Intensity Recognition in Aquaculture.Scientific data · 2026Article
- Comprehensive machine learning assessment of zebrafish behaviour and biochemical markers in response to caffeine exposure.Ecotoxicology (London, England) · 2025Article
- Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies.PLoS computational biology · 2024Article
- A latest progress in the study of fish behavior: cross-generational effects of behavior under pollution pressure and new technologies for behavior monitoring.Environmental science and pollution research international · 2024Review
- Digital Video Acquisition and Optimization Techniques for Effective Animal Tracking in Behavioral Ecotoxicology.Environmental toxicology and chemistry · 2022Article
- Fish Behavior as a Neural Proxy to Reveal Physiological States.Frontiers in physiology · 2022Review
- Parallel Fish School Tracking Based on Multiple Appearance Feature Detection.Sensors (Basel, Switzerland) · 2021Article
- Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.Nanomaterials (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented.
Identifiers
What OpenQuestion holds
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.