NeuroFAIR curator: an AI-assisted framework for metadata enrichment, quality screening, curation prioritization, and FAIR readiness of brain tumor MRI data.

Purpose: Public brain tumor MRI datasets are widely reused for classification and segmentation, but their metadata structure, technical annotation consistency, image quality, and FAIR readiness are seldom evaluated within a unified workflow. This study proposes NeuroFAIR Curator, an AI-assisted framework for metadata enrichment, computational quality screening, sample prioritization, and FAIR readiness assessment. The framework is intended for data stewardship and does not determine the clinical correctness of tumor annotations. Methods: NeuroFAIR Curator was evaluated on BRISC 2025, a two-dimensional contrast-enhanced T1-weighted MRI dataset distributed as JPEG images and PNG masks. A unified curation manifest combined images, masks, labels, split information, inferred anatomical planes, sequence descriptors, technical consistency checks, and quality indicators. A ResCNNClassifier generated label-confidence signals, while a UNet generated segmentation-derived uncertainty and agreement measures. Grad-CAM with deletion area under the curve (DAUC) supported classifier interpretation. Class- and plane-specific 1.5 × IQR limits were used for exploratory mask-area morphology screening. Seven normalized curation signals were fused into a Curation Uncertainty Index (CUI), and FAIR readiness was evaluated using an 18-sub-criterion rubric. Results: The framework generated 6,000 sample-level records linked with 15,586 external manifest entries and processed 4,793 image-mask pairs. Classification achieved 98.30% accuracy, 98.53% macro F1, and 99.79% macro AUC. Segmentation achieved Dice 82.30%, IoU 72.69%, precision 83.64%, recall 84.61%, Hausdorff distance 7.84 pixels, and HD95 4.11 pixels at a validation-selected threshold of 0.95. Exploratory morphology screening flagged 181 of 4,793 masks (3.78%) as statistically atypical; these flags were not interpreted as confirmed annotation errors. The mean Grad-CAM DAUC was 0.2996. CUI ranking captured 49 flagged samples at a 1% review budget. Findability improved from 87.50 to 100.00 and Interoperability from 36.00 to 91.98, while Accessibility and Reusability remained unchanged under the declared rubric. Conclusion: NeuroFAIR Curator converts a public brain tumor MRI corpus into a metadata-enriched, technically checked, quality-screened, review-prioritized, and FAIR-aligned neuroinformatics resource. Its morphology flags are exploratory computational signals for subsequent assessment rather than clinical judgments of mask correctness.