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本文引用的文献

1
Selecting the best machine learning algorithm to support the diagnosis of Non-Alcoholic Fatty Liver Disease: A meta learner study.选择最佳机器学习算法支持非酒精性脂肪肝疾病的诊断:元学习者研究。
PLoS One. 2020 Oct 20;15(10):e0240867. doi: 10.1371/journal.pone.0240867. eCollection 2020.
2
Fully automated detection of primary sclerosing cholangitis (PSC)-compatible bile duct changes based on 3D magnetic resonance cholangiopancreatography using machine learning.基于机器学习的 3D 磁共振胰胆管成像对原发性硬化性胆管炎(PSC)相关胆管变化的全自动检测。
Eur Radiol. 2021 Apr;31(4):2482-2489. doi: 10.1007/s00330-020-07323-5. Epub 2020 Sep 24.
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Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma.基于人工智能神经网络模型的肝癌半肝切除术后发生严重肝功能衰竭的术前预测。
Surgery. 2020 Oct;168(4):643-652. doi: 10.1016/j.surg.2020.06.031. Epub 2020 Aug 11.
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Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.使用广义卷积神经网络的肝脏自动CT和MRI分割与生物测量
Radiol Artif Intell. 2019 Mar;1(2). doi: 10.1148/ryai.2019180022. Epub 2019 Mar 27.
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Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD.非酒精性脂肪性肝病肝纤维化的自动量化和结构模式检测。
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Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching.用于预测肝移植结果的机器学习算法:供体-受者匹配问题。
Curr Opin Organ Transplant. 2020 Aug;25(4):406-411. doi: 10.1097/MOT.0000000000000781.
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Artificial neural network and bioavailability of the immunosuppression drug.人工神经网络与免疫抑制药物的生物利用度。
Curr Opin Organ Transplant. 2020 Aug;25(4):435-441. doi: 10.1097/MOT.0000000000000770.
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Radiomics based on artificial intelligence in liver diseases: where we are?基于人工智能的肝脏疾病放射组学:我们目前的进展如何?
Gastroenterol Rep (Oxf). 2020 Apr 7;8(2):90-97. doi: 10.1093/gastro/goaa011. eCollection 2020 Apr.
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Using Artificial Intelligence for Predicting Survival of Individual Grafts in Liver Transplantation: A Systematic Review.使用人工智能预测肝移植中个体移植物的存活率:系统评价。
Liver Transpl. 2020 Jul;26(7):922-934. doi: 10.1002/lt.25772. Epub 2020 Jun 23.
10
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images.肝细胞癌微血管侵犯的术前预测:基于灰阶超声图像的放射组学算法的初步应用
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人工智能在肝病学、肝脏外科和移植领域的新兴应用与前沿研究

Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research.

作者信息

Veerankutty Fadl H, Jayan Govind, Yadav Manish Kumar, Manoj Krishnan Sarojam, Yadav Abhishek, Nair Sindhu Radha Sadasivan, Shabeerali T U, Yeldho Varghese, Sasidharan Madhu, Rather Shiraz Ahmad

机构信息

Comprehensive Liver Care, VPS Lakeshore Hospital, Cochin 682040, Kerala, India.

Hepatobiliary Pancreatic and Liver Transplant Surgery, Kerala Institute of Medical Sciences, Trivandrum 695029, Kerala, India.

出版信息

World J Hepatol. 2021 Dec 27;13(12):1977-1990. doi: 10.4254/wjh.v13.i12.1977.

DOI:10.4254/wjh.v13.i12.1977
PMID:35070002
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8727218/
Abstract

The integration of artificial intelligence (AI) and augmented realities into the medical field is being attempted by various researchers across the globe. As a matter of fact, most of the advanced technologies utilized by medical providers today have been borrowed and extrapolated from other industries. The introduction of AI into the field of hepatology and liver surgery is relatively a recent phenomenon. The purpose of this narrative review is to highlight the different AI concepts which are currently being tried to improve the care of patients with liver diseases. We end with summarizing emerging trends and major challenges in the future development of AI in hepatology and liver surgery.

摘要

全球各地的众多研究人员都在尝试将人工智能(AI)和增强现实技术融入医学领域。事实上,如今医疗服务提供者所使用的大多数先进技术都是从其他行业借鉴并推广而来的。人工智能在肝病学和肝脏外科领域的应用相对较新。本叙述性综述的目的是突出目前正在尝试的不同人工智能概念,以改善肝病患者的护理。最后,我们总结了人工智能在肝病学和肝脏外科未来发展中的新兴趋势和主要挑战。