Bibliographic Details
| Title: |
Artificial intelligence outperforms standard blood-based scores in identifying liver fibrosis patients in primary care. |
| Authors: |
Blanes-Vidal, Victoria1,2 (AUTHOR) vbv@mmmi.sdu.dk, Lindvig, Katrine P.3,4 (AUTHOR), Thiele, Maja3,4 (AUTHOR), Nadimi, Esmaeil S.1,2 (AUTHOR), Krag, Aleksander3,4 (AUTHOR) |
| Source: |
Scientific Reports. 2/21/2022, Vol. 12 Issue 1, p1-11. 11p. |
| Subject Terms: |
*HEPATIC fibrosis, *ARTIFICIAL intelligence, *PRIMARY care, *PATIENT care, *ASYMPTOMATIC patients, *CHRONIC hepatitis B |
| Abstract: |
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 < 0.01) than conventional blood-based indices (AUC = 0.60–0.76). Compared to TE, LiverAID models showed a good ability to rule out significant biopsy-assessed fibrosis stages. Given the ready availability of the required data and the relatively high performance, our artificial intelligence-based models are valuable screening tools that could be used clinically for early identification of patients with asymptomatic chronic liver diseases in primary care. [ABSTRACT FROM AUTHOR] |
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