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| Item Type: | Preprint |
|---|---|
| Title: | A generative AI framework for disease-specific lung microtissue bioengineering |
| Creators Name: | Bahry, Ella, Pestoni, Jeanine C., Hirzel, Kai, Savchyn, Taras, Porras-Gonzalez, Diana, Getmanchuk-Zaporoshchenko, Vera, Gregor, Martin, Conlon, Thomas M, Önder Yildirim, Ali, Harrington, Kyle, Schmidt, Deborah, Burgstaller, Gerald and Heymann, Michael |
| Abstract: | Generative Lung Architecture Modeling (GLAM) is an integrated bioengineering framework that couples high-resolution three-dimensional tissue imaging with generative artificial intelligence to de novo design and 3D-bioprint anatomically detailed lung microtissue models. Native extracellular 3D matrix architectures of pulmonary parenchyma were extracted from healthy, fibrotic, and emphysematous in vivo mouse disease models and processed through a computational pipeline containing pre-trained image segmentation and 3D mesh generation. The resulting datasets were used to train a U-Net generative diffusion model with attention layers capable of synthesizing healthy and diseased lung tissue architectures. Microtissue cubes of about 200 - 300 µm edge length of native and synthetic datasets were fabricated through high-resolution two-photon stereolithography with gelatin-methacryloyl biomaterial ink and successfully seeded with cells, demonstrating biological compatibility. In closing the loop between biological imaging, generative modeling, and high-resolution biofabrication, this integrated framework establishes generative AI as a functional design layer for tissue engineering. The resulting lung microtissues retained architectural features of the native and original tissues, making them an application-ready platform for customizable and scalable fabrication of biological tissue surrogates for preclinical modeling, drug testing, and precision regenerative bioengineering. |
| Source: | bioRxiv |
| Publisher: | Cold Spring Harbor Laboratory Press |
| Article Number: | 2026.04.15.718723 |
| Date: | 16 April 2026 |
| Official Publication: | https://doi.org/10.64898/2026.04.15.718723 |
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