Superior transplant recipient outcome prediction and pathology assessment using rapid deep learning applied to procurement kidney biopsies
Joseph P Gaut, Jon N Marsh, Ling Chen, Fan Zhang, Xiaobing Yu, Parker Wilson, Ta-Chiang Liu, S Joshua Swamidass · 2025
Read Superior transplant recipient outcome prediction and pathology assessment using rapid deep learning applied to procurement kidney biopsies by Joseph P Gaut, Jon N Marsh, Ling Chen, Fan Zhang, Xiaobing Yu, Parker Wilson, Ta-Chiang Liu, S Joshua Swamidass (2025) 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/pmc-12804817.txt.
Description
Joseph P Gaut and co-authors present a 2025 study on using deep learning to judge donor kidney biopsies. When a deceased donor kidney is offered, pathologists must quickly score scarring in its filters, a slow task with shaky agreement between readers. Earlier work showed software could do this scoring faster and more consistently.
The team applied an updated model to 691 transplanted kidneys, with recipients followed for over four years on average. Each slide was read in about twenty-seven seconds. Both human and software scores tracked later kidney function. But only the software score predicted graft survival once other factors were accounted for. The result supports rapid automated reading at the moment organs are allocated.
Catalog record
| CREATOR | Joseph P Gaut, Jon N Marsh, Ling Chen, Fan Zhang, Xiaobing Yu, Parker Wilson, Ta-Chiang Liu, S Joshua Swamidass |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 39 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |