Neuroscience-guided bilateral thalamic MRI deep learning for obstructive sleep apnea diagnosis: a dual-center translational study
Chong Tao, Saiyue Yu, Lin Han, Qiyuan Cai, Lingzi Meng, Yan Jiang, Lin Wang · 2026
Read Neuroscience-guided bilateral thalamic MRI deep learning for obstructive sleep apnea diagnosis: a dual-center translational study by Chong Tao, Saiyue Yu, Lin Han, Qiyuan Cai, Lingzi Meng, Yan Jiang, Lin Wang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13560332.txt.
Description
Tao and colleagues built an MRI deep learning model in 2026 to help diagnose obstructive sleep apnea, guided by mouse experiments on the thalamus. The apnea condition is widely missed, and its repeated oxygen dips may alter the thalamus, a relay station in the brain. Mice exposed to intermittent low oxygen showed strong thalamic activation, strongest in one small nucleus, which justified focusing the human model on both thalami.
The team segmented the thalamus in brain scans from 760 adults across two centers and trained a dual-branch three-dimensional network with a transformer stage to fuse the two sides. The model scored high discrimination in training, validation, and an outside test center, and clearly beat single-side models. The authors present the biology-first design, where animal findings choose the imaging target, as a template for translational imaging tools that carry their reasoning with them.
Catalog record
| CREATOR | Chong Tao, Saiyue Yu, Lin Han, Qiyuan Cai, Lingzi Meng, Yan Jiang, Lin Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 60 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |