Scientists have developed a faster method to produce highly detailed 3D models of ants by combining advanced X-ray imaging, robotics and artificial intelligence, enabling researchers to digitally reconstruct hundreds of species in far less time than before.
The study, published on March 5 in the journal Nature Methods, was led by researchers including Evan Economo of the University of Maryland and Thomas van de Kamp of the Karlsruhe Institute of Technology (KIT) in Germany.
For more than a decade, Economo’s laboratory has used micro-CT scanners to study insect morphology — the structure of their bodies. Although the technology produces extremely detailed images, scanning a single specimen can take up to 10 hours.
To speed up the process, the team introduced a high-throughput system that integrates a synchrotron particle accelerator with X-ray imaging, robotics and artificial intelligence. The approach allows thousands of specimens to be scanned quickly and converted into interactive 3D models.
The project, known as Antscan, has produced digital reconstructions of about 800 ant species. Researchers estimate that completing the same task using conventional CT scanners would have required nearly six years of continuous work.
Instead, by using facilities at KIT, scientists scanned about 2,000 ant specimens in just one week.
The ants, preserved in ethanol and sourced from museums and research collections worldwide, were transported to the KIT laboratory where powerful synchrotron-generated X-rays rapidly captured images of the specimens.
A robotic system rotated each specimen during scanning and replaced it with another every 30 seconds. The process generated stacks of two-dimensional images that were later assembled into detailed three-dimensional models.
Many ants initially appeared in distorted positions, but artificial intelligence tools developed by computer science students automatically corrected their posture to create lifelike representations.
The resulting digital models reveal internal structures — including muscles, nervous systems, digestive organs and stingers — at micrometer-level resolution. Researchers say the models can also be animated or used in virtual reality environments for research, education and visual media.
The Antscan database has already supported new scientific discoveries. In a separate study published in Science Advances in December 2025, researchers used the dataset to analyse how ant colonies balance worker size and strength.
By studying more than 500 species, scientists found that colonies investing less in thick exoskeleton armour often maintain larger worker populations, suggesting that reduced protective cuticle may allow colonies to expand more successfully.
Experts say the growing Antscan archive could eventually serve as a digital library of biodiversity and help train machine-learning systems to identify ant species automatically in field research.
The team plans to expand the database further by scanning additional specimens and applying similar AI-driven techniques to other biological datasets.
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