Artificial intelligence predicts sex-specific risk of metabolic dysfunction-associated steatotic liver disease
Mohamad Jamalinia, Giovanni Targher, Amedeo Lonardo, Seyed Taghi Heydari, Kamran Bagheri Lankarani · 2026
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Description
Mohamad Jamalinia and co-authors report a 2026 study on AI forecasts of fatty liver disease split by sex. The disease, tied to metabolic faults, shows clear sex gaps in risk traits and outcomes. The team asked whether models trained apart for men and women learn unlike patterns and score better.
The study enrolled 446 adults at a heart referral center. Data split into training and test sets by sex. Eight model types were trained on all data and again apart for each sex, to flag disease presence and grade of fat build up by ultrasound. Disease was present in about 64 percent of the group. Sex split training lifted scores in men but stayed weak in women.
Catalog record
| CREATOR | Mohamad Jamalinia, Giovanni Targher, Amedeo Lonardo, Seyed Taghi Heydari, Kamran Bagheri Lankarani |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |