Performance of Supervised Classifiers for Damage Scoring of Zebrafish Neuromasts

Philip, Rohit C.; Malladi, Sree Ramya S. P.; Niihori, Maki; Jacob, Abraham; Rodríguez, Jeffrey J.

doi:10.1109/ssiai.2018.8470377

Cited by 1 publication

(1 citation statement)

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“…On a cellular and molecular dimension, while certain algorithms have evinced capabilities in identifying specialized cellular populations, such as neuromasts, melanocytes, and retinal cells − (Figure f), extensive manual annotation might still be a prerequisite. Moreover, the Max-pooling convolutional neural network demonstrated efficacy in detecting tyrosine hydroxylase-labeled cells in zebrafish larval brain z-stack imagery, but its sensitivity spectrum might not encapsulate all cell typologies.…”

Section: Ai-based Image Analysismentioning

confidence: 99%

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening

Wang,

Dong,

Qiao

et al. 2024

Environ. Sci. Technol.

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The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.

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