97 studies published between 2015 and May 2025 were systematically reviewed to map the application of artificial intelligence in behavioral analysis of invertebrate and larval model organisms. The review, authored by Zuzanna Stępnicka and published on figshare, analyzes model organisms, AI methods, input data characteristics, preprocessing pipelines, model architectures, and evaluation metrics. It proposes a standardized reporting framework to enhance transparency and reproducibility in the field.
Use Cases
- Benchmarking AI methods for behavioral analysis based on the review of 97 studies.
- Identifying trends in model organism usage and AI techniques based on the systematic mapping.
- Developing standardized reporting frameworks for AI-driven behavioral studies based on identified gaps in reproducibility.
- Selecting appropriate preprocessing pipelines and model architectures based on the reviewed literature.
Strengths
- Follows PRISMA 2020 guidelines for systematic review methodology.
- Analyzes 97 eligible studies, showing a steep increase from 2 in 2015 to 97 by mid-2025.
- Identifies dominant AI techniques such as convolutional neural networks and pose estimation frameworks like DeepLabCut.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- The dataset is 46.1 KB, indicating a limited scope likely containing summary tables rather than raw study data.
Provenance
- Source
- figshare
- Collection Method
- Systematic literature review following PRISMA 2020 guidelines.
- Time Range
- 2015 to May 2025
- Freshness
- Last updated 2026-05-19 05:41:11; freshness should be verified.