A Reproducible Protocol for Artificial Intelligence-Assisted Enhancement of Digitized Historical Neuroanatomical Illustrations

Le résumé fourni par la source

Digitized historical neuroanatomical illustrations are valuable resources for anatomical education, historical scholarship, and digital archiving. However, their practical use in teaching material and scholarly presentation is often limited by low apparent resolution, fading, paper discoloration, compression artifacts, and reduced legibility of fine anatomical line work or labels. Artificial intelligence-assisted super-resolution may improve the visual usability of these digitized reproductions, but uncontrolled enhancement may also introduce artificial texture, altered line continuity, distorted labels, or visually plausible details not supported by the source image. Therefore, standardized and transparent procedures are required when applying artificial intelligence-assisted enhancement to historical scientific illustrations. This article describes a step-by-step workflow for the controlled enhancement of digitized historical neuroanatomical illustrations. The protocol includes source-image selection, documentation of provenance and reuse status, image extraction, conservative preprocessing, local super-resolution enhancement using fixed settings, paired original-enhanced archiving, region-of-interest-based inspection, and structured technical quality-control documentation. The workflow is intended to improve visual accessibility for teaching, presentation, and preliminary visual inspection while maintaining a direct interpretive link between every enhanced output and its original digitized source. The method should be regarded as auxiliary enhancement of scanned digital reproductions, not restoration of original artwork, authentication of historical sources, or recovery of lost anatomical information. Original and enhanced versions must be archived together, and enhanced outputs should be reviewed for possible artifacts before use. This protocol provides a practical, low-cost, and reproducible approach for preparing historical neuroanatomical images as paired companion visualizations while preserving caution regarding anatomical and historical integrity.

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