{"slug": "neuropilot-an-agent-driven-smart-pipeline-for-processing-quality-control-and", "title": "NeuroPilot: An Agent-Driven Smart Pipeline for Processing, Quality Control, and Managing Neuroimages", "summary": "Researchers introduced NeuroPilot, a multi-agent system that automates neuroimage processing, quality control, and data management, achieving a 100% (201/201) completion rate on QC-validated infant inputs and compressing the traditional 2–3 month timeline into one week. Deployed across 17 cohorts (>123,000 subjects), the LLM-driven agent orchestrates workflows via three skills—dcm2bids-skill, neuroimage-pre-skill, and qc-agent-skill—and is available at https://wanda-cyberbench.com/.", "body_md": "arXiv:2608.07541v1 Announce Type: new\nAbstract: Transforming raw neuroimage archives into analysis-ready derivatives relies on three brittle stages: data standardization, modality-specific preprocessing, and quality control (QC). While individual neuroimaging tools are well developed, their orchestration requires project-specific scripts, environment-adaptive tuning, and labor-intensive manual QC. To address this, we introduce NeuroPilot, a multi-agent system that digitalizes the expertise of neuroimage processing, QC, and data management into three LLM-invocable skills: dcm2bids-skill, neuroimage-pre-skill, and qc-agent-skill. The LLM-driven agent autonomously orchestrates workflows, generalizing various infrastructure settings into a single configuration to achieve the highest scalability. Demonstrating the system's generalizability, we deployed NeuroPilot across 17 cohorts (>123,000 subjects) spanning infant to aging populations and multiple MRI modalities (structural, diffusion, functional). In practice, after standardizing data via the dcm2bids-skill, the agent dynamically routes datasets to the optimal neuroimage-pre-skill based on available modalities and cohort traits (e.g., dispatching T1w and fMRI data to fMRIPrep, or selecting specialized pipelines for infant cohorts). The qc-agent-skill then drives an evidence-based, semi-automated QC via a 3-D browser dashboard, utilizing a multi-tiered verification system to optimize failed cases and escalate complex issues for supervisor inspection. Quantitatively, our QC agent screened 558 production subjects, validating its automated flags against FreeSurfer's topology-defect metrics. The infant processing pipeline achieved a 100% (201/201) completion rate on QC-validated inputs. Importantly, NeuroPilot compresses the traditional 2--3 month timeline for training staff and processing complete datasets into a single week. NeuroPilot is deployed in https://wanda-cyberbench.com/.", "url": "https://wpnews.pro/news/neuropilot-an-agent-driven-smart-pipeline-for-processing-quality-control-and", "canonical_source": "https://arxiv.org/abs/2608.07541", "published_at": "2026-08-11 04:00:00+00:00", "updated_at": "2026-08-11 04:23:46.026246+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-tools", "machine-learning"], "entities": ["NeuroPilot", "dcm2bids-skill", "neuroimage-pre-skill", "qc-agent-skill", "FreeSurfer", "fMRIPrep"], "alternates": {"html": "https://wpnews.pro/news/neuropilot-an-agent-driven-smart-pipeline-for-processing-quality-control-and", "markdown": "https://wpnews.pro/news/neuropilot-an-agent-driven-smart-pipeline-for-processing-quality-control-and.md", "text": "https://wpnews.pro/news/neuropilot-an-agent-driven-smart-pipeline-for-processing-quality-control-and.txt", "jsonld": "https://wpnews.pro/news/neuropilot-an-agent-driven-smart-pipeline-for-processing-quality-control-and.jsonld"}}