NeuroPilot: AI-Driven Multi-Agent System for Neuroimage Processing and QC
NeuroPilot, a multi-agent system detailed in a preprint on arXiv (2608.07541), revolutionizes neuroimage processing, quality control (QC), and data management through three skills: dcm2bids-skill, neuroimage-pre-skill, and qc-agent-skill. This innovative system tackles the fragility of conventional pipelines that depend on specific scripts and manual QC. By autonomously managing workflows, NeuroPilot's LLM-driven agent adapts to various infrastructure environments, ensuring scalability with a unified configuration. It has been implemented across 17 cohorts, involving over 123,000 subjects from infants to the elderly, and includes various MRI modalities, such as structural and diffusion imaging. This advancement is crucial for neuroimaging, as it aims to lessen the burden of manual QC and enhance reproducibility. The preprint's cross-type announcement on arXiv underscores its interdisciplinary significance. NeuroPilot's name reflects its guiding role in neuroimage data processing, focusing on streamlining the three fragile stages of data standardization, modality-specific preprocessing, and QC. Its deployment across diverse cohorts showcases its adaptability and potential for widespread use in both research and clinical environments.
Key facts
- NeuroPilot is a multi-agent system for neuroimage processing and QC.
- It uses three LLM-invocable skills: dcm2bids-skill, neuroimage-pre-skill, and qc-agent-skill.
- The system autonomously orchestrates workflows via an LLM-driven agent.
- Deployed across 17 cohorts with over 123,000 subjects.
- Covers infant to aging populations and multiple MRI modalities (structural, diffusion).
- Addresses three brittle stages: data standardization, preprocessing, and QC.
- Preprint available on arXiv with ID 2608.07541.
- Aims to reduce manual QC and improve scalability.
Entities
Institutions
- arXiv