06 March 2026
Researchers from the National Institutes of Health (NIH) in the US have used artificial intelligence (AI) technology to create a ‘digital twin’ of retinal cells.
Writing in npj Artificial Intelligence, the authors highlighted that they combined AI with mathematical modelling to develop a three-dimensional digital twin of retinal pigment epithelium (RPE) cells.
The researchers added that this technology can be used to discover intracellular defects in diseased RPE.
Dr Kapil Bharti, scientific director at the NIH’s National Eye Institute (NEI), highlighted: “The digital twin approach represents a powerful new tool for age-related macular degeneration (AMD) therapeutic development and could be adapted to study other eye and non-eye diseases and conditions affecting cell polarity.”
Study author and NEI research fellow, Dr Davide Ortolan, highlighted that as well as supporting an understanding of what occurs in AMD, the technology provides a platform to discover how to fix it.
“By combining AI with mathematical modeling, we’ve created a window into cellular processes that were previously hidden from view,” he said.
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Don Williams09 March 2026
This is a very encouraging development. A digital twin is essentially a virtual biological replica that mirrors the structure and behaviour of real cells or tissues, allowing researchers to simulate disease processes and test potential interventions in a far more precise way than was previously possible.
In the context of AMD, creating a digital twin of retinal pigment epithelium cells could be particularly valuable because it may help uncover intracellular dysfunction that is otherwise difficult to observe directly. That is important not only for improving our understanding of disease mechanisms, but also for accelerating the development of more targeted therapies.
The combination of AI with mathematical modelling is especially promising because it moves beyond simple image analysis and towards a deeper functional understanding of pathology. If this approach continues to mature, it could become a very powerful research tool not only for AMD, but potentially for a much wider range of retinal and systemic disease processes.
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