Feasibility of Artificial Intelligence-Based Image Enhancement Program for Anatomical Dissection Photographs

Anatomical photographs are essential in medical education and research as they document fine details of human anatomy. which may support visualization of dissection material. This study investigated the feasibility of an artificial intelligence (AI)-based image enhancement system for anatomical dissection photographs and explored whether subtle visual differences could be detected under magnification. A dataset of 50 anatomical photographs taken between 2001 and 2024 with four different digital cameras was processed using Upscayl (v2.11.5) with the preset "16× REAL-ESRGAN." Processing was performed on a Casper Excalibur G770 laptop, requiring approximately 3-5 min per image. Original and enhanced images were compared at magnifications of 1×, 5×, 10×, 15×, and 20× on a 55-in. Full HD display. Forty experts, including neuroanatomists and neurosurgeons, qualitatively assessed the images with respect to anatomical accuracy, noise reduction, edge definition, and training value. The visual differences between the original and enhanced images were generally subtle. However, subtle improvements in edge definition and noise reduction became more apparent in deep anatomical regions, such as ventricular cavities, particularly at higher magnification levels. High-resolution images showed limited observable differences, whereas lower-resolution images exhibited slightly more noticeable changes under magnification. The enhancement process did not introduce distortions of anatomical structures. A key limitation was the substantial increase in file size after enhancement. AI-based image enhancement appears feasible for anatomical dissection photographs and may provide modest visual benefits in selected settings, especially for older or lower-resolution images viewed at higher magnification. Further optimization is required to reduce file size and processing time before routine educational or publication use.