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

Summary

This methods article presents a reproducible, quality-controlled workflow for artificial intelligence-assisted enhancement of digitized historical neuroanatomical illustrations. The protocol defines source selection, extraction, file standardization, local super-resolution processing, paired original-enhanced archiving, technical quality-control review, and documentation of both acceptable and suboptimal outputs.

Abstract

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.

Introduction

Historical neuroanatomical illustrations have played an important role in the visual documentation, teaching, and interpretation of the nervous system. Before the development of modern neuroimaging, anatomical plates served as the primary medium for communicating observations of the brain, cerebral ventricles, cranial nerves, vascular structures, and brainstem anatomy^1,2,3. Many of these works are now accessible through digitized books, scanned archival collections, and online repositories. However, the direct use of digitized historical illustrations in modern teaching, pres....

Protocol

This protocol was applied only to digitized historical neuroanatomical illustrations and did not involve human participants, patient data, animal subjects, biological samples, or identifiable personal information; therefore, institutional human-subjects research approval was not applicable. All images were obtained from public-domain, permission-cleared, or appropriately licensed historical sources.

1. Source-use considerations

1. Verify the provenance of each image before processing, including the historical author, source title, approximate publication year, and digital source.
2. Interpret enhanced output....

Results

Representative original and enhanced image comparisons are provided in the manuscript figures. The publication date, source provenance, repository information, and reuse status were verified and documented for each of the 40 historical illustrations included in this study.

Image dataset and source characteristics
The protocol was applied to 40 digitized historical neuroanatomical illustrations. The publication date, provenance, and reuse status of every illustration were i.......

Discussion

This methods article presents a reproducible workflow for AI-assisted enhancement of digitized historical neuroanatomical illustrations. Historical anatomical images have long served as important tools for anatomical documentation, teaching, and scientific communication, and many of these works are now accessible through digitized books, scanned archives, and online repositories. However, their use in modern teaching, presentations, and digital archiving is often limited by low apparent resolution, faded line work, backg.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Adobe Acrobat	Adobe	Version not recorded	PDF-based image extraction
Computer workstation	Not reported in submitted files	Configuration not reported	Hardware used for local AI-assisted image enhancement
Display/screenshot settings	Windows screen capture tool or equivalent	100% or 200% display magnification; monitor scaling recorded	Standardized screenshot-based extraction when direct PDF export was not feasible
Gallica	Bibliothèque nationale de France	Online repository	Source of digitized historical works
General Photo Real-ESRGAN preset	Upscayl / Real-ESRGAN-based model	16x setting	Super-resolution enhancement of digitized illustrations
Google Books	Google	Online repository	Source of digitized historical books
Graphics processing unit	Not reported in submitted files	Model and acceleration status not reported	GPU acceleration for image upscaling when enabled
Internet Archive	Internet Archive	Online repository	Source of public-domain digitized historical works
Local storage drive	User-specified workstation	Sufficient free storage space	Storage of original, enhanced, and figure-ready image files
PDF export settings	Adobe Acrobat or equivalent PDF reader/export software	Original embedded resolution when available	Extraction of images from digitized PDF sources without intentional downsampling
PhotoScape X	MOOII Tech	v4.1.1	Conservative cropping and figure preparation
PNG image format	—	—	Output format for enhanced images
Processing log spreadsheet	Microsoft Excel or equivalent spreadsheet software	Version not critical	Documentation of source, extraction method, enhancement setting, processing time, output file name, and QC classification
Quality-control checklist	Author-generated checklist	Not applicable	Structured technical review for artificial texture, excessive sharpening, label distortion, line thickening, and unsupported visual changes
System memory	Not reported in submitted files	RAM capacity not reported	Memory available for high-scale image enhancement
Upscayl	Upscayl	v2.11.5	Artificial intelligence-assisted image upscaling
Wellcome Collection	Wellcome Collection	Online repository	Source of public-domain digitized historical works
Wikimedia Commons	Wikimedia Foundation	Online repository	Source of public-domain or reuse-compatible images
Windows operating system	Microsoft	Windows 10	Operating system used for image extraction and processing
Windows screen capture tool	Microsoft	Windows 10 built-in tool	Screenshot-based image capture when PDF export was not feasible