ArticleComputers in biology and medicine2021
Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Exploring the Cardioprotective Spectrum-Effect Relationship ofPharmaceuticals (Basel, Switzerland) · 2026Article
- ZeCardioAI: combining zebrafish, AI, and xAI for an in-depth cardiac phenotyping platform.Briefings in bioinformatics · 2026Article
- Zebrafish as a multimodal platforms for anti-inflammatory phytomedicine discovery and translation.Frontiers in pharmacology · 2026Review
- Zebrafish as a vertebrate model for high-throughput drug toxicity screening: Mechanisms, novel techniques, and future perspectives.Journal of pharmaceutical analysis · 2025Review
- Cross-Shaped Heat Tensor Network for Morphometric Analysis Using Zebrafish Larvae Feature Keypoints.Sensors (Basel, Switzerland) · 2024Article
- 4D Light-sheet Imaging of Zebrafish Cardiac Contraction.Journal of visualized experiments : JoVE · 2024Article
- Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization.Medical & biological engineering & computing · 2023Review
- Identification of side effects of COVID-19 drug candidates on embryogenesis using an integrated zebrafish screening platform.Scientific reports · 2023Article
- A novel method for measuring acute thermal tolerance in fish embryos.Conservation physiology · 2023Article
- Using DeepLabCut as a Real-Time and Markerless Tool for Cardiac Physiology Assessment in Zebrafish.Biology · 2022Article
- Article
- An OpenCV-Based Approach for Automated Cardiac Rhythm Measurement in Zebrafish from Video Datasets.Biomolecules · 2021Article
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Authors and funding
11 authors.
Funding
Abstract
Zebrafish is a powerful and widely-used model system for a host of biological investigations, including cardiovascular studies and genetic screening. Zebrafish are readily assessable during developmental stages; however, the current methods for quantifying and monitoring cardiac functions mainly involve tedious manual work and inconsistent estimations. In this paper, we developed and validated a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) based on a U-net deep learning model for automated assessment of cardiovascular indices, such as ejection fraction (EF) and fractional shortening (FS) from microscopic videos of wildtype and cardiomyopathy mutant zebrafish embryos. Our approach yielded favorable performance with accuracy above 90% compared with manual processing. We used only black and white regular microscopic recordings with frame rates of 5-20 frames per second (fps); thus, the framework could be widely applicable with any laboratory resources and infrastructure. Most importantly, the automatic feature holds promise to enable efficient, consistent, and reliable processing and analysis capacity for large amounts of videos, which can be generated by diverse collaborating teams.
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