Artificial intelligence-based evaluation of prognosis in cirrhosis

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Abstract
Cirrhosis represents a significant global health challenge, characterized by high morbidity and mortality rates that severely impact human health. Timely and precise prognostic assessments of liver cirrhosis are crucial for improving patient outcomes and reducing mortality rates as they enable physicians to identify high-risk patients and implement early interventions. This paper features a thorough literature review on the prognostic assessment of liver cirrhosis, aiming to summarize and delineate the present status and constraints associated with the application of traditional prognostic tools in clinical settings. Among these tools, the Child–Pugh and Model for End-Stage Liver Disease (MELD) scoring systems are predominantly utilized. However, their accuracy varies significantly. These systems are generally suitable for broad assessments but lack condition-specific applicability and fail to capture the risks associated with dynamic changes in patient conditions. Future research in this field is poised for deep exploration into the integration of artificial intelligence (AI) with routine clinical and multi-omics data in patients with cirrhosis. The goal is to transition from static, unimodal assessment models to dynamic, multimodal frameworks. Such advancements will not only improve the precision of prognostic tools but also facilitate personalized medicine approaches, potentially revolutionizing clinical outcomes.
Funding Information
  • The Ministry of Science and Technology of the People's Republic of China (2021ZD0201900)
  • National Natural Science Foundation of China (12090052, 82272204)
  • Natural Science Foundation of Liaoning Province (2023-MS-288)
  • Science and Technology Bureau Project of Wenzhou (Y2023729, Y20220505)
  • Fundamental Research Funds for the Liaoning Universities
  • 5G Network-based Platform for Precision Emergency Medical Care in Regional Hospital Clusters funded by the Ministry of Industry and Information Technology of the People's Republic of China (2020-78)
  • The Key Clinical Specialty Program of the Zhejiang Province of Critical Care Medicine (Y2022)
  • “Pioneer” and “Leading Goose” R&D Program of Zhejiang (2023C03084)

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