FibroX: a machine learning model for detecting and prognosticating advanced fibrosis in metabolic dysfunction-associated steatotic liver disease
Basile Njei, Yazan A Al-Ajlouni, Ysabel Ilagan-Ying, Omar Al Ta’ani, Sarpong Boateng, Nelvis Njei, Ulrick Sidney Kanmounye, Joseph Lim, Bubu Banini · 2026
Read FibroX: a machine learning model for detecting and prognosticating advanced fibrosis in metabolic dysfunction-associated steatotic liver disease by Basile Njei, Yazan A Al-Ajlouni, Ysabel Ilagan-Ying, Omar Al Ta’ani, Sarpong Boateng, Nelvis Njei, Ulrick Sidney Kanmounye, Joseph Lim, Bubu Banini (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/pmc-13263986.txt.
Description
Basile Njei and co-authors introduce FibroX, a machine learning model for advanced liver scarring, in a 2026 study of fatty liver disease linked to metabolic problems. Common blood-based scores can miss or misclassify serious fibrosis, which is scarring at stage F3 or higher. FibroX was trained on survey data from nearly fifteen hundred adults and used a boosting algorithm tuned for high specificity.
Testing included cross-validation, a biopsy-confirmed patient group and a long-running survey cohort followed for mortality. FibroX separated advanced fibrosis more accurately than the widely used FIB-4 score in the main dataset. Explanation methods showed which inputs drove each prediction. The work offers a noninvasive option that could help clinicians find high-risk patients earlier.
Catalog record
| CREATOR | Basile Njei, Yazan A Al-Ajlouni, Ysabel Ilagan-Ying, Omar Al Ta’ani, Sarpong Boateng, Nelvis Njei, Ulrick Sidney Kanmounye, Joseph Lim, Bubu Banini |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 40 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |