| Autor: |
Maja Thiele, Esmaeil Nadimi, Aleksander Krag, Victoria Blanes-Vidal, Katrine Prier Lindvig |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Blanes-Vidal, V, Lindvig, K P, Thiele, M, Nadimi, E S & Krag, A 2022, ' Artificial intelligence outperforms standard blood-based scores in identifying liver fibrosis patients in primary care ', Scientific Reports, vol. 12, 2914 . https://doi.org/10.1038/s41598-022-06998-8 |
| DOI: |
10.1038/s41598-022-06998-8 |
| Popis: |
For years, hepatologists have been seeking non-invasive methods able to detect significant liver fibrosis. However, no previous algorithm using routine blood markers has proven to be clinically appropriate in primary care. We present a novel approach based on artificial intelligence, able to predict significant liver fibrosis in low-prevalence populations using routinely available patient data. We built six ensemble learning models (LiverAID) with different complexities using a prospective screening cohort of 3352 asymptomatic subjects. 463 patients were at a significant risk that justified performing a liver biopsy. Using an unseen hold-out dataset, we conducted a head-to-head comparison with conventional methods: standard blood-based indices (FIB-4, Forns and APRI) and transient elastography (TE). LiverAID models appropriately identified patients with significant liver stiffness (> 8 kPa) (AUC of 0.86, 0.89, 0.91, 0.92, 0.92 and 0.94, and NPV ≥ 0.98), and had a significantly superior discriminative ability (p |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
