Use of machine learning models for the prognostication of liver transplantation: A systematic review

BackgroundHepatocellular carcinoma (HCC) is a common primary liver cancer that requires early diagnosis due to its poor prognosis. Recent advances in artificial intelligence (AI) have facilitated hepatocellular carcinoma detection using multiple AI models; however, their performance is still uncertain.AimThis meta-analysis aimed to compare the diagnostic performance of different AI models with that of clinicians in the detection of hepatocellular carcinoma.MethodsWe searched the PubMed, Scopus, Cochrane Library, and Web of Science databases for eligible studies. The R package was used to synthesize the results. The outcomes of various studies were aggregated using fixed-effect and random-effects models. Statistical heterogeneity was evaluated using I-squared (I2) and chi-square statistics.ResultsWe included seven studies in our meta-analysis;. Both physicians and AI-based models scored an average sensitivity of 93%. Great variation in sensitivity, accuracy, and specificity was observed depending on the model and diagnostic technique used. The region-based convolutional neural network (RCNN) model showed high sensitivity (96%). Physicians had the highest specificity in diagnosing hepatocellular carcinoma(100%); furthermore, models-based convolutional neural networks achieved high sensitivity. Models based on AI-assisted Contrast-enhanced ultrasound (CEUS) showed poor accuracy (69.9%) compared to physicians and other models. The leave-one-out sensitivity revealed high heterogeneity among studies, which represented true differences among the studies.ConclusionModels based on Faster R-CNN excel in image classification and data extraction, while both CNN-based models and models combining contrast-enhanced ultrasound (CEUS) with artificial intelligence (AI) had good sensitivity. Although AI models outperform physicians in diagnosing HCC, they should be utilized as supportive tools to help make more accurate and timely decisions.

show abstract

Palliative care for end-stage liver disease and acute on chronic liver failure: A systematic review

Mafi,

Soldera

2024

World J Methodol

View full text Add to dashboard Cite

BACKGROUND End stage liver disease (ESLD) represents a growing health concern characterized by elevated morbidity and mortality, particularly among individual ineligible for liver transplantation. The demand for palliative care (PC) is pronounced in patients grappling with ESLD and acute on chronic liver failure (ACLF). Unfortunately, the historical underutilization of PC in ESLD patients, despite their substantial needs and those of their family caregivers, underscores the imperative of seamlessly integrating PC principles into routine healthcare practices across the entire disease spectrum. AIM To comprehensively investigate the evidence surrounding the benefits of incorporating PC into the comprehensive care plan for individuals confronting ESLD and/or ACLF. METHODS A systematic search in the Medline (PubMed) database was performed using a predetermined search command, encompassing studies published in English without any restrictions on the publication date. Subsequently, the retrieved studies were manually examined. Simple descriptive analyses were employed to summarize the results. RESULTS The search strategies yielded 721 references. Following the final analysis, 32 full-length references met the inclusion criteria and were consequently incorporated into the study. Meticulous data extraction from these 32 studies was undertaken, leading to the execution of a comprehensive narrative systematic review. The review found that PC provides significant benefits, reducing symptom burden, depressive symptoms, readmission rates, and hospital stays. Yet, barriers like the appeal of transplants and misconceptions about PC hinder optimal utilization. Integrating PC early, upon the diagnosis of ESLD and ACLF, regardless of transplant eligibility and availability, improves the quality of life for these patients. CONCLUSION Despite the substantial suffering and poor prognosis associated with ESLD and ACLF, where liver transplantation stands as the only curative treatment, albeit largely inaccessible, PC services have been overtly provided too late in the course of the illness. A comprehensive understanding of PC's pivotal role in treating ESLD and ACLF is crucial for overcoming these barriers, involving healthcare providers, patients, and caregivers.

show abstract

Use of machine learning models for the prognostication of liver transplantation: A systematic review

Cited by 3 publications

References 118 publications

Machine Learning-Based Analysis and Prediction of Liver Cirrhosis

Machine Learning-Based Analysis and Prediction of Liver Cirrhosis

Diagnostic performance of AI-based models versus physicians among patients with hepatocellular carcinoma: a systematic review and meta-analysis

Palliative care for end-stage liver disease and acute on chronic liver failure: A systematic review

Contact Info

Product

Resources

About