The AFIDs-Validator is an open-access, browser-based platform designed to teach and quantitatively assess placement of anatomical landmarks in neuroimaging. The tool addresses a gap in training resources: neuroanatomy and landmarking are often learned informally via mentorship and require access to desktop software and local datasets. AFIDs-Validator provides guided instruction and automated, interpretable feedback without software installation or local data requirements.
The platform pairs an interactive MRI viewer with a language-model–driven neuroanatomy tutor operating inside the viewer. In learning mode, the tutor delivers anatomy-first instruction that responds to the learner’s current image slice, orientation, and cursor position. This integration allows contextualized guidance tied to the exact view the learner is using, providing instruction that adapts to slice location and viewer orientation.
AFIDs-Validator includes a validation engine that accepts a learner’s landmark file and computes per-landmark Euclidean error against expert-annotated reference landmarks. Reference annotations span 21 brain templates, enabling comparisons across multiple standard anatomical volumes. The engine returns quantitative error values for each landmark, allowing objective assessment of placement accuracy relative to expert references.
To make feedback interpretable and fair, the authors analyzed a large empirical dataset of landmark placements. They compiled 15,000 landmark annotations collected across 132 human subjects. Analysis of these annotations showed substantial variability in landmark difficulty and error magnitude:
These empirical results demonstrate that landmark-specific variability is inherent even among trained raters and that a single arbitrary error threshold would poorly reflect typical performance across landmarks.
Rather than scoring learners against a single arbitrary distance cutoff, AFIDs-Validator uses the empirically derived error distributions to create per-landmark reliability priors. Learners are scored relative to the observed spread of trained raters for each specific landmark. This approach aims to:
The platform therefore emphasizes context-aware assessment: performance is evaluated against behavioral norms derived from expert annotations rather than a uniform standard applied across heterogeneous landmarks.
AFIDs-Validator is delivered as a browser-based resource that requires no installation and no licensed software. It does not require users to host local imaging data. All code, reference data, and the tutor design have been openly released by the authors. The preprint reports no competing interests, and the work is made available under a CC-BY 4.0 license.
AFIDs-Validator provides a scalable, accessible option for learning and assessing neuroanatomical landmark placement. By combining an embedded language-model tutor with a validation engine grounded in empirical annotation data, the platform seeks to standardize instruction and deliver quantitatively meaningful feedback. The use of per-landmark reliability priors acknowledges intrinsic differences in landmark difficulty and aligns learner assessment to the observed performance distribution of trained raters. The source preprint describes the platform, the underlying empirical analysis of 15,000 annotations across 132 subjects, reference coverage of 21 brain templates, and the open release of code and data. Details beyond the abstract (for example, implementation specifics, user outcomes, or longitudinal evaluation) were not reported in the source.