Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently
begun but it has already become obvious that intelligent systems can dramatically
improve the management of liver diseases. Big data made it possible to envisage transformative
developments of the use of AI for diagnosing, predicting prognosis and treating liver
diseases, but there is still a lot of work to do.
If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international
rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading
results is essential for the effective use of AI. A crucial question is whether it
is possible to sustain, technically and morally, the process of integration between
man and machine.
We present a systematic review on the applications of AI to hepatology, highlighting
the current challenges and crucial issues related to the use of such technologies.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Digestive and Liver DiseaseAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- High-performance medicine: the convergence of human and artificial intelligence.Nat Med. 2019; 25: 44-56https://doi.org/10.1038/s41591-018-0300-7
- From big data to precision medicine.Front Med. 2019; 6: 34https://doi.org/10.3389/fmed.2019.00034
- Artificial intelligence in primary health care: perceptions, issues, and challenges.Yearb Med Inform. 2019; 28: 41-46https://doi.org/10.1055/s-0039-1677901
- Artificial intelligence in healthcare.Nat Biomed Eng. 2018; 2: 719-731https://doi.org/10.1038/s41551-018-0305-z
- Machine learning-based genome-wide interrogation of somatic copy number aberrations in circulating tumor DNA for early detection of hepatocellular carcinoma.EBioMedicine. 2020; 56102811https://doi.org/10.1016/j.ebiom.2020.102811
- Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma.Annu Int Conf IEEE Eng Med Biol Soc. 2020; 2020: 6095-6098https://doi.org/10.1109/EMBC44109.2020.9175293
- Uncovering the potential differentially expressed miRNAs as diagnostic biomarkers for hepatocellular carcinoma based on machine learning in The Cancer Genome Atlas database.Oncol Rep. 2020; 43: 1771-1784https://doi.org/10.3892/or.2020.7551
- Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases.Sci Rep. 2020; 10: 4435https://doi.org/10.1038/s41598-020-61298-3
- Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles.PLoS One. 2019; 14e0221476https://doi.org/10.1371/journal.pone.0221476
- Random gene sets in predicting survival of patients with hepatocellular carcinoma.J Mol Med (Berl). 2019; 97: 879-888https://doi.org/10.1007/s00109-019-01764-2
- Predicting overall survival of patients with hepatocellular carcinoma using a three-category method based on DNA methylation and machine learning.J Cell Mol Med. 2019; 23: 3369-3374https://doi.org/10.1111/jcmm.14231
- Deep learning-based multi-omics integration robustly predicts survival in liver cancer.Clin Cancer Res. 2018; 24: 1248-1259https://doi.org/10.1158/1078-0432.CCR-17-0853
- Gene signature associated with upregulation of the Wnt/β-catenin signaling pathway predicts tumor response to transarterial embolization.J Vasc Interv Radiol. 2017; 28 (e1. doi:): 349-355https://doi.org/10.1016/j.jvir.2016.11.004
- Targeted proteomics predicts a sustained complete-response after transarterial chemoembolization and clinical outcomes in patients with hepatocellular carcinoma: a prospective cohort study.J Proteome Res. 2017; 16: 1239-1248https://doi.org/10.1021/acs.jproteome.6b00833
- Identification of hepatocellular carcinoma-related genes with a machine learning and network analysis.J Comput Biol. Jan 2015; 22: 63-71https://doi.org/10.1089/cmb.2014.0122
- Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software.Acta Radiol. 2021; 62: 291-301https://doi.org/10.1177/0284185120922822
- Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning.J Cancer Res Clin Oncol. 2021; 147: 821-833https://doi.org/10.1007/s00432-020-03366-9
- An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images.Annu Int Conf IEEE Eng Med Biol Soc. 2020; 2020: 1339-1342https://doi.org/10.1109/EMBC44109.2020.9176627
- Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics.Eur Radiol. 2020; 30: 6924-6932https://doi.org/10.1007/s00330-020-07056-5
- Detection of hepatocellular carcinoma in contrast-enhanced magnetic resonance imaging using deep learning classifier: a multi-center retrospective study.Sci Rep. 2020; 10: 9458https://doi.org/10.1038/s41598-020-65875-4
- Comparison of deep-learning and conventional machine-learning methods for the automatic recognition of the hepatocellular carcinoma areas from ultrasound images.Sensors (Basel). 2020; 20: 3085https://doi.org/10.3390/s20113085
- Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study.EBioMedicine. 2020; 56102777https://doi.org/10.1016/j.ebiom.2020.102777
- Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation.Eur J Radiol. 2020; 126108918https://doi.org/10.1016/j.ejrad.2020.108918
- Lesion Image Synthesis Using DCGANs for Metastatic Liver Cancer Detection.Adv Exp Med Biol. 2020; 1213: 95-106https://doi.org/10.1007/978-3-030-33128-3_6
- Liver contrast-enhanced sonography: computer-assisted differentiation between focal nodular hyperplasia and inflammatory hepatocellular adenoma by reference to microbubble transport patterns.Eur Radiol. 2020; 30: 2995-3003https://doi.org/10.1007/s00330-019-06566-1
- Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.Eur Radiol. 2020; 30: 2365-2376https://doi.org/10.1007/s00330-019-06553-6
- Utilizing machine learning for pre- and postoperative assessment of patients undergoing resection for BCLC-0, A and B hepatocellular carcinoma: implications for resection beyond the BCLC guidelines.Ann Surg Oncol. 2020; 27: 866-874https://doi.org/10.1245/s10434-019-08025-z
- Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation.Med Hypotheses. 2020; 134109431https://doi.org/10.1016/j.mehy.2019.109431
- Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules.Eur Radiol. 2020; 30: 558-570https://doi.org/10.1007/s00330-019-06347-w
- Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging.Eur Radiol. 2020; 30: 413-424https://doi.org/10.1007/s00330-019-06318-1
- Deep learning-based radiomics models for early recurrence prediction of hepatocellular carcinoma with multi-phase CT images and clinical data.Annu Int Conf IEEE Eng Med Biol Soc. 2019; 2019: 4881-4884https://doi.org/10.1109/EMBC.2019.8856356
- Improving the malignancy characterization of hepatocellular carcinoma using deeply supervised cross modal transfer learning for non-enhanced MR.Annu Int Conf IEEE Eng Med Biol Soc. 2019; 2019: 853-856https://doi.org/10.1109/EMBC.2019.8857467
- Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.EBioMedicine. 2019; 50: 156-165https://doi.org/10.1016/j.ebiom.2019.10.057
- Predicting hepatocellular carcinoma recurrences: A data-driven multiclass classification method incorporating latent variables.Biomed Inform. 2019; 96103237https://doi.org/10.1016/j.jbi.2019.103237
- Noninvasive evaluation of the pathologic grade of hepatocellular carcinoma using MCF-3DCNN: a pilot study.Biomed Res Int. 2019; 20199783106https://doi.org/10.1155/2019/9783106
- Machine-learning approach for the development of a novel predictive model for the diagnosis of hepatocellular carcinoma.Sci Rep. 2019; 9: 7704https://doi.org/10.1038/s41598-019-44022-8
- Automatic classification of focal liver lesions based on MRI and risk factors.PLoS One. 2019; 14e0217053https://doi.org/10.1371/journal.pone.0217053
- Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features.Eur Radiol. 2019; 29: 3348-3357https://doi.org/10.1007/s00330-019-06214-8
- Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT.Int J Comput Assist Radiol Surg. 2019; 14: 1341-1352https://doi.org/10.1007/s11548-019-01991-5
- Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?.Int J Comput Assist Radiol Surg. 2019; 14: 1295-1301https://doi.org/10.1007/s11548-019-01987-1
- Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.Eur Radiol. 2019; 29: 3338-3347https://doi.org/10.1007/s00330-019-06205-9
- Diagnosis of focal liver lesions from ultrasound using deep learning.Diagn Interv Imaging. 2019; 100: 227-233https://doi.org/10.1016/j.diii.2019.02.009
- Natural language processing of radiology reports in patients with hepatocellular carcinoma to predict radiology resource utilization.J Am Coll Radiol. 2019; 16: 840-844https://doi.org/10.1016/j.jacr.2018.12.004
- Extending 2-D convolutional neural networks to 3-D for advancing deep learning cancer classification with application to mri liver tumor differentiation.IEEE J Biomed Health Inform. 2019; 23: 923-930https://doi.org/10.1109/JBHI.2018.2886276
- Predicting treatment response to image-guided therapies using machine learning: an example for trans-arterial treatment of hepatocellular carcinoma.J Vis Exp. 2018; : 58382https://doi.org/10.3791/58382
- Registration-based organ positioning and joint segmentation method for liver and tumor segmentation.Biomed Res Int. 2018; 20188536854https://doi.org/10.1155/2018/8536854
- H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes.IEEE Trans Med Imaging. 2018; 37: 2663-2674https://doi.org/10.1109/TMI.2018.2845918
- A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images.Clin Hemorheol Microcirc. 2018; 69: 343-354https://doi.org/10.3233/CH-170275
- Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies.Med Biol Eng Comput. 2018; 56: 1699-1713https://doi.org/10.1007/s11517-018-1803-6
- Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features.Comput Biol Med. 2018; 94: 11-18https://doi.org/10.1016/j.compbiomed.2017.12.024
- Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study.Radiology. 2018; 286: 887-896https://doi.org/10.1148/radiol.2017170706
- CT image-based decision support system for categorization of liver metastases into primary cancer sites: initial results.Acad Radiol. 2017; 24: 1501-1509https://doi.org/10.1016/j.acra.2017.06.008
- Metastatic liver tumour segmentation with a neural network-guided 3D deformable model.Med Biol Eng Comput. 2017; 55: 127-139https://doi.org/10.1007/s11517-016-1495-8
- Tumor reference resolution and characteristic extraction in radiology reports for liver cancer stage prediction.J Biomed Inform. 2016; 64: 179-191https://doi.org/10.1016/j.jbi.2016.10.005
- Automatic liver segmentation from CT images using single-block linear detection.Biomed Res Int. 2016; 20169420148https://doi.org/10.1155/2016/9420148
- Liver tumor segmentation from MR images using 3D fast marching algorithm and single hidden layer feedforward neural network.Biomed Res Int. 2016; 20163219068https://doi.org/10.1155/2016/3219068
- Comparison of knowledge-based iterative model reconstruction and hybrid reconstruction techniques for liver CT evaluation of hypervascular hepatocellular carcinoma.J Comput Assist Tomogr. 2016; 40: 863-871https://doi.org/10.1097/RCT.0000000000000455
- Unsupervised detection of liver lesions in CT images.Annu Int Conf IEEE Eng Med Biol Soc. 2015; 2015: 2411-2414https://doi.org/10.1109/EMBC.2015.7318880
- Semiautomatic segmentation of liver metastases on volumetric CT images.Med Phys. 2015; 42: 6283-6293
- Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network.Biomed Mater Eng. 2015; 26 (Suppl): S1599-S1611https://doi.org/10.3233/BME-151459
- Metastatic liver tumour segmentation from discriminant Grassmannian manifolds.Phys Med Biol. 2015; 60: 6459-6478https://doi.org/10.1088/0031-9155/60/16/6459
- Random feature subspace ensemble based extreme learning machine for liver tumor detection and segmentation.Annu Int Conf IEEE Eng Med Biol Soc. 2014; 2014: 4675-4678https://doi.org/10.1109/EMBC.2014.6944667
- A novel multiinstance learning approach for liver cancer recognition on abdominal CT images based on CPSO-SVM and IO.Comput Math Methods Med. 2013; 2013434969https://doi.org/10.1155/2013/434969
- Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma.Am J Gastroenterol. 2013; 108: 1723-1730https://doi.org/10.1038/ajg.2013.332
- Liver tumor detection and segmentation using kernel-based Extreme Learning Machine.Annu Int Conf IEEE Eng Med Biol Soc. 2013; 2013: 3662-3665https://doi.org/10.1109/EMBC.2013.6610337
- Segmentation of hepatic tumor from abdominal CT data using an improved support vector machine framework.Annu Int Conf IEEE Eng Med Biol Soc. 2013; 2013: 3347-3350https://doi.org/10.1109/EMBC.2013.6610258
- Content-based retrieval of focal liver lesions using bag-of-visual-words representations of single- and multiphase contrast-enhanced CT images.J Digit Imaging. 2012; 25: 708-719https://doi.org/10.1007/s10278-012-9495-1
- Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning.J Biophotonics. 2019; 12e201800435https://doi.org/10.1002/jbio.201800435
- Sparse Contribution Feature Selection and Classifiers Optimized by Concave-Convex Variation for HCC Image Recognition.Biomed Res Int. 2017; 20179718386https://doi.org/10.1155/2017/9718386
- Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.Comput Biol Med. 2017; 84: 156-167https://doi.org/10.1016/j.compbiomed.2017.03.017
- Selecting the best machine learning algorithm to support the diagnosis of non-alcoholic fatty liver disease: a meta learner study.PLoS One. 2020; 15e0240867https://doi.org/10.1371/journal.pone.0240867
- Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools.Food Chem Toxicol. 2020; 142111494https://doi.org/10.1016/j.fct.2020.111494
- Relevant Features in Nonalcoholic Steatohepatitis Determined Using Machine Learning for Feature Selection.Metab Syndr Relat Disord. 2019; 17: 444-451https://doi.org/10.1089/met.2019.0052
- Prediction of fatty liver disease using machine learning algorithms.Comput Methods Programs Biomed. 2019; 170: 23-29https://doi.org/10.1016/j.cmpb.2018.12.032
- Application of machine learning methods to predict non-alcoholic steatohepatitis (NASH) in non-alcoholic fatty liver (NAFL) patients.AMIA Annu Symp Proc. 2018; 2018: 430-439
- Application of machine learning techniques for clinical predictive modeling: a cross-sectional study on nonalcoholic fatty liver disease in China.Biomed Res Int. 2018; 20184304376https://doi.org/10.1155/2018/4304376
- Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population.Aliment Pharmacol Ther. 2017; 46: 447-456https://doi.org/10.1111/apt.14172
- Predicting and elucidating the etiology of fatty liver disease: a machine learning modeling and validation study in the IMI DIRECT cohorts.PLoS Med. 2020; 17e1003149https://doi.org/10.1371/journal.pmed.1003149
- Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: a proof of concept study.Metabolism. 2019; 101154005https://doi.org/10.1016/j.metabol.2019.154005
- Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks.Radiology. 2020; 295: 342-350https://doi.org/10.1148/radiol.2020191160
- Ultrasonic liver steatosis quantification by a learning-based acoustic model from a novel shear wave sequence.Biomed Eng Online. 2019; 18: 121https://doi.org/10.1186/s12938-019-0742-2
- Automated liver fat quantification at nonenhanced abdominal CT for population-based steatosis assessment.Radiology. 2019; 293: 334-342https://doi.org/10.1148/radiol.2019190512
- Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations.Med Phys. 2019; 46: 3508-3519https://doi.org/10.1002/mp.13675
- Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images.Int J Comput Assist Radiol Surg. 2018; 13: 1895-1903https://doi.org/10.1007/s11548-018-1843-2
- Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm.Comput Methods Programs Biomed. 2018; 155: 165-177https://doi.org/10.1016/j.cmpb.2017.12.016
- Accurate identification of fatty liver disease in data warehouse utilizing natural language processing.Dig Dis Sci. 2017; 62: 2713-2718https://doi.org/10.1007/s10620-017-4721-9
- Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization.J Med Syst. 2017; 41 (Erratum in: J Med Syst. 2017;42(1):18): 152https://doi.org/10.1007/s10916-017-0797-1
- Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm.Med Phys. 2012; 39: 4255-4264https://doi.org/10.1118/1.4725759
- Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.Lab Invest. 2020; 100: 1367-1383https://doi.org/10.1038/s41374-020-0463-y
- Automatic classification of white regions in liver biopsies by supervised machine learning.Hum Pathol. 2014; 45: 785-792https://doi.org/10.1016/j.humpath.2013.11.011
- Development, validation, and evaluation of a simple machine learning model to predict cirrhosis mortality.JAMA Netw Open. 2020; 3e2023780https://doi.org/10.1001/jamanetworkopen.2020.23780
- Assisting the non-invasive diagnosis of liver fibrosis stages using machine learning methods.Annu Int Conf IEEE Eng Med Biol Soc. 2020; 2020: 5382-5387https://doi.org/10.1109/EMBC44109.2020.9176542
- Deep residual nets model for staging liver fibrosis on plain CT images.Int J Comput Assist Radiol Surg. 2020; 15: 1399-1406https://doi.org/10.1007/s11548-020-02206-y
- Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology.Eur Radiol. 2020; 30: 4675-4685https://doi.org/10.1007/s00330-020-06831-8
- Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.Eur Radiol. 2020; 30: 2973-2983https://doi.org/10.1007/s00330-019-06595-w
- Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network.Eur Radiol. 2020; 30: 1264-1273https://doi.org/10.1007/s00330-019-06407-1
- Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data.AJR Am J Roentgenol. 2019; 213: 592-601https://doi.org/10.2214/AJR.19.21082
- Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment.Med Phys. 2019; 46: 2298-2309https://doi.org/10.1002/mp.13521
- Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis.Eur Radiol. 2019; 29: 1496-1506https://doi.org/10.1007/s00330-018-5680-z
- Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced ct images in the liver.Radiology. 2018; 289: 688-697https://doi.org/10.1148/radiol.2018180763
- Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning.EBioMedicine. 2018; 35: 124-132https://doi.org/10.1016/j.ebiom.2018.07.041
- Deep learning for staging liver fibrosis on CT: a pilot study.Eur Radiol. Nov 2018; 28: 4578-4585https://doi.org/10.1007/s00330-018-5499-7
- Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study.Gut. 2019; 68: 729-741https://doi.org/10.1136/gutjnl-2018-316204
- Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C.PLoS One. 2017; 12e0187344https://doi.org/10.1371/journal.pone.0187344
- Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B.Comput Biol Med. 2017; 89: 18-23https://doi.org/10.1016/j.compbiomed.2017.07.012
- Comparison of machine learning approaches for prediction of advanced liver fibrosis in chronic hepatitis C patients.IEEE/ACM Trans Comput Biol Bioinform. 2018; 15: 861-868https://doi.org/10.1109/TCBB.2017.2690848
- Machine learning models to predict disease progression among veterans with hepatitis C virus.PLoS One. 2019; 14e0208141https://doi.org/10.1371/journal.pone.0208141
- Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification.Sensors (Basel). 2017; 17: 149https://doi.org/10.3390/s17010149
- Computational models of liver fibrosis progression for hepatitis C virus chronic infection.BMC Bioinformatics. 2014; (15 Suppl 8Suppl): S5https://doi.org/10.1186/1471-2105-15-S8-S5
- Computer-aided diagnosis and quantification of cirrhotic livers based on morphological analysis and machine learning.Comput Math Methods Med. 2013; 2013264809https://doi.org/10.1155/2013/264809
- Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C.Artif Intell Med. 2011; 51: 53-65https://doi.org/10.1016/j.artmed.2010.06.002
- The promise of AI for DILI prediction.Front Artif Intell. 2021; 4638410https://doi.org/10.3389/frai.2021.638410
- Machine learning in liver transplantation: a tool for some unsolved questions?.Transpl Int. 2021; 34: 398-411https://doi.org/10.1111/tri.13818
- Textbook of epidemiology. Netherlands: Bohn Stafleu van Loghum.2018
- Digital epidemiology: what is it, and where is it going?.Life Sci Soc Policy. 2018; 14: 1https://doi.org/10.1186/s40504-017-0065-7
- The epidemiology of (mis)information.Am J Med. 2002; 113: 763-765https://doi.org/10.1016/S0002-9343(02)01473-0
- Association of search query interest in gastrointestinal symptoms with COVID-19 Diagnosis in the United States: infodemiology study.JMIR Public Heal Surveill. 2020; 6: e19354https://doi.org/10.2196/19354
- Silver lining of COVID-19: Heightened global interest in pneumococcal and influenza vaccines, an infodemiology study.Vaccine. 2020; 38: 5430-5435https://doi.org/10.1016/j.vaccine.2020.06.069
- Infodemiology: tracking flu-related searches on the web for syndromic surveillance.AMIA Annu Symp Proc. 2006; 2006: 244-248
- Is there a duty to participate in digital epidemiology?.Life Sci Soc Policy. 2018; 14: 9https://doi.org/10.1186/s40504-018-0074-1
- Trends of infodemiology studies: a scoping review.Health Info Libr J. 2018; 35: 91-120https://doi.org/10.1111/hir.12216
- Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease.Hepatology. 2018; 67: 123-133https://doi.org/10.1002/hep.29466
- Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016–2030.J Hepatol. 2018; 69: 896-904https://doi.org/10.1016/j.jhep.2018.05.036
- Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis.BMC Gastroenterol. 2021; 21: 10https://doi.org/10.1186/s12876-020-01585-5
- Support of precision medicine through risk-stratification in autoimmune liver diseases – histology, scoring systems, and non-invasive markers.Autoimmun Rev. 2018; 17: 854-865https://doi.org/10.1016/j.autrev.2018.02.013
- Risk stratification in primary sclerosing cholangitis.Minerva Gastroenterol Dietol. 2020; https://doi.org/10.23736/S1121-421X.20.02821-4
- Primary sclerosing cholangitis risk estimate tool (PREsTo) predicts outcomes of the disease: a derivation and validation study using machine learning.Hepatology. 2020; 71: 214-224https://doi.org/10.1002/hep.30085
- Bile acid profiles in primary sclerosing cholangitis and their ability to predict hepatic decompensation.Hepatology. 2020; https://doi.org/10.1002/hep.31652
- Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantation.Am J Transplant. 2019; 19: 1109-1118https://doi.org/10.1111/ajt.15172
- Machine-learning algorithms predict graft failure after liver transplantation.Transplantation. 2017; 101: e125-e132https://doi.org/10.1097/TP.0000000000001600
- Survival outcomes following pediatric liver transplantation (Pedi-SOFT) score: a novel predictive index.Am J Transplant. 2015; 15: 1855-1863https://doi.org/10.1111/ajt.13190
- Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study.J Hepatol. 2014; 61: 1020-1028https://doi.org/10.1016/j.jhep.2014.05.039
- Are there better guidelines for allocation in liver transplantation?: A novel score targeting justice and utility in the model for end-stage liver disease era.Ann Surg. 2011; 254: 745-753https://doi.org/10.1097/SLA.0b013e3182365081
- Impact of MELD-based allocation on end-stage renal disease after liver transplantation.Am J Transplant. 2011; 11: 2372-2378https://doi.org/10.1111/j.1600-6143.2011.03703.x
- Machine learning in medicine.N Engl J Med. 2019; 380: 1347-1358https://doi.org/10.1056/NEJMra1814259
- Omics-derived hepatocellular carcinoma risk biomarkers for precision care of chronic liver diseases.Hepatol Res. 2020; 50: 817-830https://doi.org/10.1111/hepr.13506
- The role of omics in the pathophysiology, diagnosis and treatment of non-alcoholic fatty liver disease.Metabolism. 2020; 111S154320https://doi.org/10.1016/j.metabol.2020.154320
- Bioinformatics and database resources in hepatology.J Hepatol. 2015; 62: 712-719https://doi.org/10.1016/j.jhep.2014.10.036
- Multi-omics approaches to disease.Genome Biol. 2017; 18: 83https://doi.org/10.1186/s13059-017-1215-1
Vujkovic M, Ramdas S, Lorenz KM, et al. A genome-wide association study for nonalcoholic fatty liver disease 1 identifies novel genetic loci and trait-relevant candidate genes in the 2 Million Veteran Program. 3. MedRxiv 2021. https://doi.org/10.1101/2020.12.26.20248491.
- Genetic contributions to NAFLD: leveraging shared genetics to uncover systems biology.Nat Rev Gastroenterol Hepatol. 2020; 17: 40-52https://doi.org/10.1038/s41575-019-0212-0
- Recent advances of microbiome-associated metabolomics profiling in liver disease: principles, mechanisms, and applications.Int J Mol Sci. 2021; 22: 1-18https://doi.org/10.3390/ijms22031160
- Taking it Personally: Personalized Utilization of the Human Microbiome in Health and Disease.Cell Host Microbe. 2016; 19: 12-20https://doi.org/10.1016/j.chom.2015.12.016
- Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses.Cell Metab. 2017; 25: 1243-1253https://doi.org/10.1016/j.cmet.2017.05.002
- Personalized Nutrition by Prediction of Glycemic Responses.Cell. 2015; 163: 1079-1094https://doi.org/10.1016/j.cell.2015.11.001
- Gut Microbiome-Based Metagenomic Signature for Non-invasive Detection of Advanced Fibrosis in Human Nonalcoholic Fatty Liver Disease.Cell Metab. 2017; 25 (e5): 1054-1062https://doi.org/10.1016/j.cmet.2017.04.001
- A gut microbiome signature for cirrhosis due to nonalcoholic fatty liver disease.Nat Commun. 2019; 10: 1406https://doi.org/10.1038/s41467-019-09455-9
- Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers.Sci Rep. 2017; 7: 16954https://doi.org/10.1038/s41598-017-17031-8
- Genomics and Liver Transplantation: Genomic Biomarkers for the Diagnosis of Acute Cellular Rejection.Liver Transplant. 2020; 26: 1337-1350https://doi.org/10.1002/lt.25812
- Artificial intelligence in medical imaging of the liver.World J Gastroenterol. 2019; 25: 672-682https://doi.org/10.3748/wjg.v25.i6.672
- CT perfusion of the liver: Principles and applications in oncology.Radiology. 2014; 272: 322-344https://doi.org/10.1148/radiol.14130091
- Digital liver biopsy: bio-imaging of fatty liver for translational and clinical research.World J Hepatol. 2018; 10: 231-245https://doi.org/10.4254/wjh.v10.i2.231
- Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure.Nat Protoc. 2012; 7: 654-669https://doi.org/10.1038/nprot.2012.009
- Deep learning in medical image analysis.Annu Rev Biomed Eng. 2017; 19: 221-248https://doi.org/10.1146/annurev-bioeng-071516-044442
- Application of artificial intelligence technology in oncology: towards the establishment of precision medicine.Cancers (Basel). 2020; 12: 3532https://doi.org/10.3390/cancers12123532
- European society of radiology (ESR).Insights Imaging. 2010; 1 (White paper on imaging biomarkers): 42-45https://doi.org/10.1007/s13244-010-0025-8
- A new initiative on precision medicine.N Engl J Med. 2015; 372: 793-795https://doi.org/10.1056/nejmp1500523
- Radiomics and imaging genomics in precision medicine.Precis Futur Med. 2017; 1: 10-31https://doi.org/10.23838/pfm.2017.00101
- A phase 2 multicenter study of stereotactic body radiotherapy for hepatocellular carcinoma: Safety and efficacy.Cancer. 2020; 126: 363-372https://doi.org/10.1002/cncr.32502
- Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT.Med Phys. 2018; 45: 4763-4774https://doi.org/10.1002/mp.13122
- Preoperative diagnosis and prediction of hepatocellular carcinoma: radiomics analysis based on multi-modal ultrasound images.BMC Cancer. 2018; 18: 1089https://doi.org/10.1186/s12885-018-5003-4
- A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma.Surg Oncol. 2019; 28: 78-85https://doi.org/10.1016/j.suronc.2018.11.013
- Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI.Eur Radiol. 2019; 29: 4648-4659https://doi.org/10.1007/s00330-018-5935-8
- Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma.Eur Radiol. 2019; 29: 2890-2901https://doi.org/10.1007/s00330-018-5797-0
- Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images.BMC Cancer. 2018; 18: 1089https://doi.org/10.1186/s12885-018-5003-4
- Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest.Diagn Interv Imaging. 2018; 99: 643-651https://doi.org/10.1016/j.diii.2018.05.008
- Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy.BMC Cancer. 2017; 17: 829https://doi.org/10.1186/s12885-017-3847-7
- Signature of survival: A 18F-FDG PET based whole-liver radiomic analysis predicts survival after 90Y-TARE for hepatocellular carcinoma.Oncotarget. 2018; 9: 4549-4558https://doi.org/10.18632/oncotarget.23423
- A systematic review of techniques and sources of big data in the healthcare sector.J Med Syst. 2017; 41: 183https://doi.org/10.1007/s10916-017-0832-2
- Big data in nephrology: Friend or foe?.Blood Purif. 2014; 36: 160-164https://doi.org/10.1159/000356751
- Big data and biomedical informatics: a challenging opportunity.Yearb Med Inform. 2014; 9: 8-13https://doi.org/10.15265/IY-2014-0024
- Big data analytics in healthcare: promise and potential.Heal Inf Sci Syst. 2014; 2: 3https://doi.org/10.1186/2047-2501-2-3
- Deep learning and process understanding for data-driven Earth system science.Nature. 2019; 566: 195-204https://doi.org/10.1038/s41586-019-0912-1
- Learning from the Melbourne experience: how reliable are cancer registry data for hepatocellular carcinoma?.Hepatology. 2016; 63: 1078-1079https://doi.org/10.1002/hep.28452
- Identifying candidates with favorable prognosis following liver transplantation for hepatocellular carcinoma: Data mining analysis.J Surg Oncol. 2015; 112: 72-79https://doi.org/10.1002/jso.23944
- Making sense of big data in health research: Towards an EU action plan.Genome Med. 2016; 8: 71https://doi.org/10.1186/s13073-016-0323-y
- Big data: are we making a big mistake? | Financial Times.Financ Times. 2014;
- Small data vs. big data : back to the basics.Datafloq, 2014
- Defining the scientific method.Nat Methods. 2009; 6: 237https://doi.org/10.1038/nmeth0409-237
- Lost in Thought - The Limits of the Human Mind and the Future of Medicine.N Engl J Med. 2017; 377 (PMID: 28953443; PMCID: PMC5754014): 1209-1211https://doi.org/10.1056/NEJMp1705348
- Why digital medicine depends on interoperability.Npj Digit Med. 2019; 2: 79https://doi.org/10.1038/s41746-019-0158-1
- The dark secret at the heart of AI.Technol Rev. 2017;
- Can we open the black box of AI?.Nature. 2016; 538: 20-23https://doi.org/10.1038/538020a
- Beyond the hype of big data and artificial intelligence: Building foundations for knowledge and wisdom.BMC Med. 2019; 17: 143https://doi.org/10.1186/s12916-019-1382-x
- Data Storage Mechanism Based on Blockchain with Privacy Protection in Wireless Body Area Network.Sensors (Basel). 2019; 19: 2395https://doi.org/10.3390/s19102395
- Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements.Psychol Sci Public Interes. 2019; 20: 1-68https://doi.org/10.1177/1529100619832930
- White paper- industrial data space - digital sovereignty over data.Fraunhofer-Gesellschaft, München, Germany2016
- Brain drain from developing countries: How can brain drain be converted into wisdom gain?.J R Soc Med. 2005; 98: 487-491https://doi.org/10.1258/jrsm.98.11.487
- The state of AI 2019.MMC Ventures, London, United Kingdom2019
- Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms.Int J Biomed Imaging. 2013; 2013323268https://doi.org/10.1155/2013/323268
- Unintended consequences of machine learning in medicine.JAMA - J Am Med Assoc. 2017; 318: 517-518https://doi.org/10.1001/jama.2017.7797
Article info
Publication history
Published online: July 12, 2021
Accepted:
June 7,
2021
Received:
March 2,
2021
Identification
Copyright
© 2021 Published by Elsevier Ltd on behalf of Editrice Gastroenterologica Italiana S.r.l.