AIMC Topic: Liver Transplantation
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Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study.
Scientific reports
Aug 28, 2024
Titrating tacrolimus concentration in liver transplantation recipients remains a challenge in the early post-transplant period. This multicenter retrospective cohort study aimed to develop and validate a machine-learning algorithm to predict tacrolim...
Informatics-driven solutions for optimal care delivery in liver transplantation.
Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
Aug 26, 2024
Clinical informatics, which combines health information technology and clinical expertise, aims to improve health care delivery and outcomes. For candidates and recipients of liver transplants, the complexities of their management are vast. Care ofte...
Artificial intelligence-based model for the recurrence of hepatocellular carcinoma after liver transplantation.
Surgery
Aug 23, 2024
BACKGROUND: Artificial intelligence-based models might improve patient selection for liver transplantation in hepatocellular carcinoma. The objective of the current study was to develop artificial intelligence-based deep learning models and determine...
Optimizing post-transplantation outcomes: the role of multi-omics, artificial intelligence, and animal models in addressing immunosuppression-associated hepatotoxicity.
International journal of surgery (London, England)
Aug 1, 2024
Why your doctor is not an algorithm: Exploring logical principles of different clinical inference methods using liver transplantation as a model.
Gastroenterologia y hepatologia
Jun 7, 2024
The development of machine learning (ML) tools in many different medical settings is largely increasing. However, the use of the resulting algorithms in daily medical practice is still an unsolved challenge. We propose an epistemological approach (i....
Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.
Human genomics
Jun 5, 2024
BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with rec...
Liver Transplantation
Gene Expression Regulation, Neoplastic
Signal Transduction
Neoplasm Recurrence, Local
Gene Expression Profiling
Transcriptome
Deep Learning
Liver Neoplasms
Carcinoma, Hepatocellular
Protein Interaction Maps
Gene Regulatory Networks
Female
Biomarkers, Tumor
Humans
Male
Middle Aged
Deep learning for 3D biliary anatomy for living liver donor hepatectomy planning.
International journal of surgery (London, England)
Jun 1, 2024
Impact of an artificial intelligence based model to predict non-transplantable recurrence among patients with hepatocellular carcinoma.
HPB : the official journal of the International Hepato Pancreato Biliary Association
May 16, 2024
OBJECTIVE: We sought to develop Artificial Intelligence (AI) based models to predict non-transplantable recurrence (NTR) of hepatocellular carcinoma (HCC) following hepatic resection (HR).
3D auto-segmentation of biliary structure of living liver donors using magnetic resonance cholangiopancreatography for enhanced preoperative planning.
International journal of surgery (London, England)
Apr 1, 2024
BACKGROUND: This study aimed to develop an automated segmentation system for biliary structures using a deep learning model, based on data from magnetic resonance cholangiopancreatography (MRCP).
Integrated Bioinformatics and Validation Reveal and Its Related Molecules as Potential Identifying Genes in Liver Cirrhosis.
Biomolecules
Dec 21, 2023
Liver cirrhosis remains a significant global public health concern, with liver transplantation standing as the foremost effective treatment currently available. Therefore, investigating the pathogenesis of liver cirrhosis and developing novel therapi...
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