ArticleeLife2019
DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
76 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations.Frontiers in artificial intelligence · 2026Pooled it
- Beast3D: Animal behavioral analysis and neural encoding from multi-view video via Gaussian splatting.ArXiv · 2026Article
- Whole-body 3D kinematics of freely behavingbioRxiv : the preprint server for biology · 2026Article
- AVATAR: AI Vision Analysis for Three-dimensional Action in Real-time.Experimental neurobiology · 2026Article
- Lightning Pose 3D: an uncertainty-aware framework for data-efficient multi-view animal pose estimation.bioRxiv : the preprint server for biology · 2026Article
- Centralized brain networks controlling antennal grooming coordination.Nature communications · 2026Article
- Non-pixelated in-materia retinomorphic sensor via photocarrier dynamics for precise spatiotemporal perception.Nature communications · 2026Article
- Deep Imputation for Skeleton data (DISK) for behavioral science.Nature methods · 2026Article
- Development of a deep learning-based grid walking test for assessing post-stroke motor function in mice.Frontiers in behavioral neuroscience · 2026Article
- DISSeCT: An unsupervised framework for high-resolution mapping of rodent behavior using inertial sensors.PLoS biology · 2025Article
- A parametric finite element model of leg campaniform sensilla inJournal of the Royal Society, Interface · 2025Article
- Mapping the landscape of social behavior.Cell · 2025Article
- FlyVISTA, an integrated machine learning platform for deep phenotyping of sleep inScience advances · 2025Article
- A real-time, multi-subject three-dimensional pose tracking system for the behavioral analysis of non-human primates.Cell reports methods · 2025Article
- ONIX: a unified open-source platform for multimodal neural recording and perturbation during naturalistic behavior.Nature methods · 2025Article
- NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila.Nature methods · 2024Article
- Miniature linear and split-belt treadmills reveal mechanisms of adaptive motor control in walking Drosophila.Current biology : CB · 2024Article
- Mapping the landscape of social behavior.bioRxiv : the preprint server for biology · 2024Article
- Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools.Nature methods · 2024Article
- FlyVISTA, an Integrated Machine Learning Platform for Deep Phenotyping of Sleep inbioRxiv : the preprint server for biology · 2024Article
16 more citing papers are in PubMed but not listed here.
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
Abstract
Studying how neural circuits orchestrate limbed behaviors requires the precise measurement of the positions of each appendage in three-dimensional (3D) space. Deep neural networks can estimate two-dimensional (2D) pose in freely behaving and tethered animals. However, the unique challenges associated with transforming these 2D measurements into reliable and precise 3D poses have not been addressed for small animals including the fly,
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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.