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Review Article

人工智能在胃肠道、肝、胰腺疾病检测中的最新应用

卷 29, 期 1, 2022

发表于: 05 April, 2021

页: [66 - 85] 页: 20

弟呕挨: 10.2174/0929867328666210405114938

价格: $65

摘要

在过去的二十年中，人工智能（AI）算法及其医学科学应用取得了实质性进展。人工智能辅助程序已经建立起来，用于使用传感器和智能手机进行远程健康监测。各种基于AI的预测模型用于无线胶囊内窥镜检查的胃肠道，炎症，非恶性疾病和肠道出血，使用电子病历的肝炎相关纤维化以及使用内窥镜超声检查的胰腺癌。基于AI的模型，可能对医疗保健专业人员使用内窥镜图像进行识别，分析和决策支持产生巨大帮助，通过多种因素建立患者治疗的预后和风险评估。在医疗监管机构批准人工智能算法辅助和非基于人工智能的治疗之前，有足够的随机临床试验来确定这些技术的疗效。本文综述了用于检测胃肠道、肝脏和胰腺疾病的可用 AI 方法和基于 AI 的预测模型。讨论了AI技术在此类疾病预后，风险评估和决策支持方面的局限性。

关键词: 人工智能，深度学习，机器学习，胃肠病学，肝病，胰腺癌。

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[1]
Colom, R.; Karama, S.; Jung, R.E.; Haier, R.J. Human intelligence and brain networks. Dialogues Clin. Neurosci., 2010, 12(4), 489-501.
[http://dx.doi.org/10.31887/DCNS.2010.12.4/rcolom] [PMID: 21319494]
[2]
Noorbakhsh-Sabet, N.; Zand, R.; Zhang, Y.; Abedi, V. Artificial Intelligence Transforms the Future of Health Care. Am. J. Med., 2019, 132(7), 795-801.
[http://dx.doi.org/10.1016/j.amjmed.2019.01.017] [PMID: 30710543]
[3]
Ponn, T.; Kröger, T.; Diermeyer, F. Performance Analysis of Camera-based Object Detection for Automated Vehicles. Sensors (Basel), 2020, 20(13), 3699.
[http://dx.doi.org/10.3390/s20133699] [PMID: 32630350]
[4]
Dias, R.; Torkamani, A. Artificial intelligence in clinical and genomic diagnostics. Genome Med., 2019, 11(1), 70.
[http://dx.doi.org/10.1186/s13073-019-0689-8] [PMID: 31744524]
[5]
Nichols, J.A.; Herbert Chan, H.W.; Baker, M.A.B. Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophys. Rev., 2019, 11(1), 111-118.
[http://dx.doi.org/10.1007/s12551-018-0449-9] [PMID: 30182201]
[6]
Dipaola, F.; Gatti, M.; Pacetti, V.; Bottaccioli, A.G.; Shiffer, D.; Minonzio, M.; Menè, R.; Giaj Levra, A.; Solbiati, M.; Costantino, G.; Anastasio, M.; Sini, E.; Barbic, F.; Brunetta, E.; Furlan, R. Artificial Intelligence Algorithms and Natural Language Processing for the Recognition of Syncope Patients on Emergency Department Medical Records. J. Clin. Med., 2019, 8(10), 1677.
[http://dx.doi.org/10.3390/jcm8101677] [PMID: 31614982]
[7]
Wu, J.T.; Dernoncourt, F.; Gehrmann, S.; Tyler, P.D.; Moseley, E.T.; Carlson, E.T.; Grant, D.W.; Li, Y.; Welt, J.; Celi, L.A. Behind the scenes: A medical natural language processing project. Int. J. Med. Inform., 2018, 112, 68-73.
[http://dx.doi.org/10.1016/j.ijmedinf.2017.12.003] [PMID: 29500024]
[8]
Baclic, O.; Tunis, M.; Young, K.; Doan, C.; Swerdfeger, H.; Schonfeld, J. Challenges and opportunities for public health made possible by advances in natural language processing. Can. Commun. Dis. Rep., 2020, 46(6), 161-168.
[http://dx.doi.org/10.14745/ccdr.v46i06a02] [PMID: 32673380]
[9]
Agrebi, S.; Larbi, A. Use of artificial intelligence in infectious diseases. Artificial Intelligence in Precision Health; Barh, D., Ed.; Academic Press, 2020, Vol. 1, pp. 415-438.
[http://dx.doi.org/10.1016/B978-0-12-817133-2.00018-5]
[10]
Jiang, F.; Jiang, Y.; Zhi, H.; Dong, Y.; Li, H.; Ma, S.; Wang, Y.; Dong, Q.; Shen, H.; Wang, Y. Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol., 2017, 2(4), 230-243.
[http://dx.doi.org/10.1136/svn-2017-000101] [PMID: 29507784]
[11]
Davenport, T.; Kalakota, R. The potential for artificial intelligence in healthcare. Future Healthc J, 2019, 6(2), 94-98.
[http://dx.doi.org/10.7861/futurehosp.6-2-94] [PMID: 31363513]
[12]
Lalmuanawma, S.; Hussain, J.; Chhakchhuak, L. Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos Solitons Fractals, 2020, 139, 110059.
[http://dx.doi.org/10.1016/j.chaos.2020.110059] [PMID: 32834612]
[13]
Yang, X.; Wang, Y.; Byrne, R.; Schneider, G.; Yang, S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem. Rev., 2019, 119(18), 10520-10594.
[http://dx.doi.org/10.1021/acs.chemrev.8b00728] [PMID: 31294972]
[14]
Hessler, G.; Baringhaus, K.H. Artificial Intelligence in Drug Design. Molecules, 2018, 23(10), 2520.
[http://dx.doi.org/10.3390/molecules23102520] [PMID: 30279331]
[15]
Kumar, R.; Sharma, A.; Siddiqui, M.H.; Tiwari, R.K. Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques. Curr. Drug Discov. Technol., 2017, 14(4), 244-254.
[http://dx.doi.org/10.2174/1570163814666170404160911] [PMID: 28382857]
[16]
Kumar, R.; Sharma, A.; Siddiqui, M.H.; Tiwari, R.K. Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms. Comb. Chem. High Throughput Screen., 2018, 21(1), 57-64.
[http://dx.doi.org/10.2174/1386207321666171218121557] [PMID: 29256344]
[17]
Amisha; Malik, P.; Pathania, M.; Rathaur, V.K. Overview of artificial intelligence in medicine. J. Family Med. Prim. Care, 2019, 8, 2328-2331.
[http://dx.doi.org/10.4103/jfmpc.jfmpc_440_19]
[18]
Saxena, D.; Siddiqui, M.H.; Kumar, R. Deep Learning in Disease Diagnosis: Models and Datasets. Curr. Bioinform., 2020, 15, 1.
[http://dx.doi.org/10.2174/1574893615999201002124021]
[19]
Sharma, A.; Kumar, R.; Semwal, R.; Aier, I.; Tyagi, P.; Varadwaj, P. DeepOlf: Deep neural network based architecture for predicting odorants and their interacting Olfactory Receptors. IEEE/ACM Trans. Comput. Biol. Bioinform, 2020. [Epub Ahead of Print]
[http://dx.doi.org/10.1109/TCBB.2020.3002154] [PMID: 32750862]
[20]
Kumar, R.; Sharma, A.; Siddiqui, M.H.; Tiwari, R.K. Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached? Curr. Drug Metab., 2016, 17(2), 129-141.
[http://dx.doi.org/10.2174/1389200216666151103121352] [PMID: 26526829]
[21]
Kumar, R.; Sharma, A.; Siddiqui, M.H.; Tiwari, R.K. Promises of Machine Learning Approaches in Prediction of Absorption of Compounds. Mini Rev. Med. Chem., 2018, 18(3), 196-207.
[http://dx.doi.org/10.2174/1389557517666170315150116] [PMID: 28302041]
[22]
Parekh, V.S.; Jacobs, M.A. Deep learning and radiomics in precision medicine. Expert Rev. Precis. Med. Drug Dev., 2019, 4(2), 59-72.
[http://dx.doi.org/10.1080/23808993.2019.1585805] [PMID: 31080889]
[23]
Krittanawong, C.; Zhang, H.; Wang, Z.; Aydar, M.; Kitai, T. Artificial Intelligence in Precision Cardiovascular Medicine. J. Am. Coll. Cardiol., 2017, 69(21), 2657-2664.
[http://dx.doi.org/10.1016/j.jacc.2017.03.571] [PMID: 28545640]
[24]
Schork, N.J. Artificial Intelligence and Personalized Medicine. Cancer Treat. Res, 2019, 178, 265-283.
[http://dx.doi.org/10.1007/978-3-030-16391-4_11] [PMID: 31209850]
[25]
Magrabi, F.; Ammenwerth, E.; McNair, J.B.; De Keizer, N.F.; Hyppönen, H.; Nykänen, P.; Rigby, M.; Scott, P.J.; Vehko, T.; Wong, Z.S.; Georgiou, A. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications. Yearb. Med. Inform., 2019, 28(1), 128-134.
[http://dx.doi.org/10.1055/s-0039-1677903] [PMID: 31022752]
[26]
Montani, S.; Striani, M. Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey. Yearb. Med. Inform., 2019, 28(1), 120-127.
[http://dx.doi.org/10.1055/s-0039-1677911] [PMID: 31419824]
[27]
Spänig, S.; Emberger-Klein, A.; Sowa, J.P.; Canbay, A.; Menrad, K.; Heider, D. The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes. Artif. Intell. Med., 2019, 100, 101706.
[http://dx.doi.org/10.1016/j.artmed.2019.101706] [PMID: 31607340]
[28]
Bi, W.L.; Hosny, A.; Schabath, M.B.; Giger, M.L.; Birkbak, N.J.; Mehrtash, A.; Allison, T.; Arnaout, O.; Abbosh, C.; Dunn, I.F.; Mak, R.H.; Tamimi, R.M.; Tempany, C.M.; Swanton, C.; Hoffmann, U.; Schwartz, L.H.; Gillies, R.J.; Huang, R.Y.; Aerts, H.J.W.L. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J. Clin., 2019, 69(2), 127-157.
[http://dx.doi.org/10.3322/caac.21552] [PMID: 30720861]
[29]
Thompson, R.F.; Valdes, G.; Fuller, C.D.; Carpenter, C.M.; Morin, O.; Aneja, S.; Lindsay, W.D.; Aerts, H.J.W.L.; Agrimson, B.; Deville, C., Jr; Rosenthal, S.A.; Yu, J.B.; Thomas, C.R., Jr Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiother. Oncol., 2018, 129(3), 421-426.
[http://dx.doi.org/10.1016/j.radonc.2018.05.030] [PMID: 29907338]
[30]
Chassagnon, G.; Vakalopoulou, M.; Paragios, N.; Revel, M.P. Artificial intelligence applications for thoracic imaging. Eur. J. Radiol., 2020, 123, 108774.
[http://dx.doi.org/10.1016/j.ejrad.2019.108774] [PMID: 31841881]
[31]
Mayro, E.L.; Wang, M.; Elze, T.; Pasquale, L.R. The impact of artificial intelligence in the diagnosis and management of glaucoma. Eye (Lond.), 2020, 34(1), 1-11.
[http://dx.doi.org/10.1038/s41433-019-0577-x] [PMID: 31541215]
[32]
Saxena, D.; Sharma, A.; Siddiqui, M.H.; Kumar, R. Blood Brain Barrier Permeability Prediction Using Machine Learning Techniques: An Update. Curr. Pharm. Biotechnol., 2019, 20(14), 1163-1171.
[http://dx.doi.org/10.2174/1389201020666190821145346] [PMID: 31433750]
[33]
Kumar, R.; Sharma, A.; Tiwari, R.K. Can we predict blood brain barrier permeability of ligands using computational approaches? Interdiscip. Sci., 2013, 5(2), 95-101.
[http://dx.doi.org/10.1007/s12539-013-0158-9] [PMID: 23740390]
[34]
Grote, T.; Berens, P. On the ethics of algorithmic decision-making in healthcare. J. Med. Ethics, 2020, 46(3), 205-211.
[http://dx.doi.org/10.1136/medethics-2019-105586] [PMID: 31748206]
[35]
Wang, Y.; Jia, P.; Liu, L.; Huang, C.; Liu, Z. A systematic review of fuzzing based on machine learning techniques. PLoS One, 2020, 15(8), e0237749.
[http://dx.doi.org/10.1371/journal.pone.0237749] [PMID: 32810156]
[36]
Horne, E.; Tibble, H.; Sheikh, A.; Tsanas, A. Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping. JMIR Med. Inform., 2020, 8(5), e16452.
[http://dx.doi.org/10.2196/16452] [PMID: 32463370]
[37]
Henry, D.; Dymnicki, A.B.; Mohatt, N.; Allen, J.; Kelly, J.G. Clustering Methods with Qualitative Data: a Mixed-Methods Approach for Prevention Research with Small Samples. Prev. Sci., 2015, 16(7), 1007-1016.
[http://dx.doi.org/10.1007/s11121-015-0561-z] [PMID: 25946969]
[38]
Rashidi, H.H.; Tran, N.K.; Betts, E.V.; Howell, L.P.; Green, R. Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Acad Pathol, 2019, 6, 2374289519873088.
[http://dx.doi.org/10.1177/2374289519873088] [PMID: 31523704]
[39]
Fabris, F.; Magalhães, J.P.; Freitas, A.A. A review of supervised machine learning applied to ageing research. Biogerontology, 2017, 18(2), 171-188.
[http://dx.doi.org/10.1007/s10522-017-9683-y] [PMID: 28265788]
[40]
Son, Y.J.; Kim, H.G.; Kim, E.H.; Choi, S.; Lee, S.K. Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc. Inform. Res., 2010, 16(4), 253-259.
[http://dx.doi.org/10.4258/hir.2010.16.4.253] [PMID: 21818444]
[41]
Sharma, A.; Kumar, R.; Varadwaj, P.K.; Ahmad, A.; Ashraf, G.M. A comparative study of support vector machine, artificial neural network and bayesian classifier for mutagenicity prediction. Interdiscip. Sci., 2011, 3(3), 232-239.
[http://dx.doi.org/10.1007/s12539-011-0102-9] [PMID: 21956745]
[42]
Kumar, R.; Sharma, A.; Varadwaj, P.; Ahmad, A.; Ashraf, G.M. Classification of oral bioavailability of drugs by machine learning approaches: a comparative study. J. Comp. Int. Sci, 2011, 2, 1-18.
[http://dx.doi.org/10.6062/jcis.2011.02.03.0045]
[43]
Sharma, A.; Kumar, R.; Varadwaj, P. Prediction of mutagenicity of compounds by support vector machine. Online J. Bioinform., 2011, 12, 9-17.
[44]
Kumar, R.; Sharma, A.; Varadwaj, P.K. A prediction model for oral bioavailability of drugs using physicochemical properties by support vector machine. J. Nat. Sci. Biol. Med., 2011, 2(2), 168-173.
[http://dx.doi.org/10.4103/0976-9668.92325] [PMID: 22346230]
[45]
LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature, 2015, 521(7553), 436-444.
[http://dx.doi.org/10.1038/nature14539] [PMID: 26017442]
[46]
Alipanahi, B.; Delong, A.; Weirauch, M.T.; Frey, B.J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol., 2015, 33(8), 831-838.
[http://dx.doi.org/10.1038/nbt.3300] [PMID: 26213851]
[47]
Zhou, J.; Troyanskaya, O.G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods, 2015, 12(10), 931-934.
[http://dx.doi.org/10.1038/nmeth.3547] [PMID: 26301843]
[48]
Tran, S.N.; d’Avila Garcez, A.S. Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks. IEEE Trans. Neural Netw. Learn. Syst., 2018, 29(2), 246-258.
[http://dx.doi.org/10.1109/TNNLS.2016.2603784] [PMID: 27845678]
[49]
Yang, H.; Shen, S.; Yao, X.; Sheng, M.; Wang, C. Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition. Sensors (Basel), 2018, 18(4), 952.
[http://dx.doi.org/10.3390/s18040952] [PMID: 29570642]
[50]
Zhan, T.; Chen, Y.; Hong, X.; Lu, Z.; Chen, Y. Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge. CNS Neurol. Disord. Drug Targets, 2017, 16(2), 129-136.
[http://dx.doi.org/10.2174/1871527316666170113101559] [PMID: 28088902]
[51]
Li, G.; Deng, L.; Xu, Y.; Wen, C.; Wang, W.; Pei, J.; Shi, L. Temperature based Restricted Boltzmann Machines. Sci. Rep., 2016, 6, 19133.
[http://dx.doi.org/10.1038/srep19133] [PMID: 26758235]
[52]
Chu, J.; Wang, H.; Meng, H.; Jin, P.; Li, T. Restricted Boltzmann Machines With Gaussian Visible Units Guided by Pairwise Constraints. IEEE Trans. Cybern., 2019, 49(12), 4321-4334.
[http://dx.doi.org/10.1109/TCYB.2018.2863601] [PMID: 30137021]
[53]
Cao, P.; Liu, X.; Bao, H.; Yang, J.; Zhao, D. Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD. Biomed. Mater. Eng., 2015, 26(Suppl. 1), S1541-S1547.
[http://dx.doi.org/10.3233/BME-151453] [PMID: 26405918]
[54]
Tubiana, J.; Cocco, S.; Monasson, R. Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins. Neural Comput., 2019, 31(8), 1671-1717.
[http://dx.doi.org/10.1162/neco_a_01210] [PMID: 31260391]
[55]
Mehdipour Ghazi, M.; Nielsen, M.; Pai, A.; Cardoso, M.J.; Modat, M.; Ourselin, S.; Sørensen, L. Alzheimer’s Disease Neuroimaging Initiative. Training recurrent neural networks robust to incomplete data: Application to Alzheimer’s disease progression modeling. Med. Image Anal., 2019, 53, 39-46.
[http://dx.doi.org/10.1016/j.media.2019.01.004] [PMID: 30682584]
[56]
Kimmel, J.; Brack, A.; Marshall, W. Deep convolutional and recurrent neural networks for cell motility discrimination and prediction. IEEE/ACM Trans. Comput. Biol. Bioinform, 2021, 18, 562-574.
[http://dx.doi.org/10.1109/TCBB.2019.2919307] [PMID: 31251191]
[57]
Liu, M.; Cheng, D.; Yan, W. Alzheimer’s Disease Neuroimaging Initiative. Classification of Alzheimer’s Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images. Front. Neuroinform., 2018, 12, 35.
[http://dx.doi.org/10.3389/fninf.2018.00035] [PMID: 29970996]
[58]
Szkoła, J.; Pancerz, K.; Warchoł, J. Recurrent neural networks in computer-based clinical decision support for laryngopathies: an experimental study. Comput. Intell. Neurosci., 2011, 2011, 289398.
[http://dx.doi.org/10.1155/2011/289398] [PMID: 22007195]
[59]
Pellicer-Valero, O.J.; Cattinelli, I.; Neri, L.; Mari, F.; Martín-Guerrero, J.D.; Barbieri, C. Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks. Artif. Intell. Med., 2020, 107, 101898.
[http://dx.doi.org/10.1016/j.artmed.2020.101898] [PMID: 32828446]
[60]
Akbar, S.; Peikari, M.; Salama, S.; Nofech-Mozes, S.; Martel, A.L. The transition module: a method for preventing overfitting in convolutional neural networks. Comput. Methods Biomech. Biomed. Eng. Imaging Vis., 2019, 7(3), 260-265.
[http://dx.doi.org/10.1080/21681163.2018.1427148] [PMID: 31192055]
[61]
Wang, L.; Zang, J.; Zhang, Q.; Niu, Z.; Hua, G.; Zheng, N. Action Recognition by an Attention-Aware Temporal Weighted Convolutional Neural Network. Sensors (Basel), 2018, 18(7), 1979.
[http://dx.doi.org/10.3390/s18071979] [PMID: 29933555]
[62]
Shin, H.C.; Roth, H.R.; Gao, M.; Lu, L.; Xu, Z.; Nogues, I.; Yao, J.; Mollura, D.; Summers, R.M. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Trans. Med. Imaging, 2016, 35(5), 1285-1298.
[http://dx.doi.org/10.1109/TMI.2016.2528162] [PMID: 26886976]
[63]
Abdelhafiz, D.; Yang, C.; Ammar, R.; Nabavi, S. Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinformatics, 2019, 20(Suppl. 11), 281.
[http://dx.doi.org/10.1186/s12859-019-2823-4] [PMID: 31167642]
[64]
Brown, J.M.; Campbell, J.P.; Beers, A.; Chang, K.; Ostmo, S.; Chan, R.V.P.; Dy, J.; Erdogmus, D.; Ioannidis, S.; Kalpathy-Cramer, J.; Chiang, M.F. Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks. JAMA Ophthalmol., 2018, 136(7), 803-810.
[http://dx.doi.org/10.1001/jamaophthalmol.2018.1934] [PMID: 29801159]
[65]
Zhou, L.Q.; Wang, J.Y.; Yu, S.Y.; Wu, G.G.; Wei, Q.; Deng, Y.B.; Wu, X.L.; Cui, X.W.; Dietrich, C.F. Artificial intelligence in medical imaging of the liver. World J. Gastroenterol., 2019, 25(6), 672-682.
[http://dx.doi.org/10.3748/wjg.v25.i6.672] [PMID: 30783371]
[66]
Basaia, S.; Agosta, F.; Wagner, L.; Canu, E.; Magnani, G.; Santangelo, R.; Filippi, M. Alzheimer’s Disease Neuroimaging Initiative. Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin., 2019, 21, 101645.
[http://dx.doi.org/10.1016/j.nicl.2018.101645] [PMID: 30584016]
[67]
Andersen, J.K.H.; Pedersen, J.S.; Laursen, M.S.; Holtz, K.; Grauslund, J.; Savarimuthu, T.R.; Just, S.A. Neural networks for automatic scoring of arthritis disease activity on ultrasound images. RMD Open, 2019, 5(1), e000891.
[http://dx.doi.org/10.1136/rmdopen-2018-000891] [PMID: 30997154]
[68]
Torng, W.; Altman, R.B. Graph Convolutional Neural Networks for Predicting Drug-Target Interactions. J. Chem. Inf. Model., 2019, 59(10), 4131-4149.
[http://dx.doi.org/10.1021/acs.jcim.9b00628] [PMID: 31580672]
[69]
Berishvili, V.P.; Perkin, V.O.; Voronkov, A.E.; Radchenko, E.V.; Syed, R.; Venkata Ramana Reddy, C.; Pillay, V.; Kumar, P.; Choonara, Y.E.; Kamal, A.; Palyulin, V.A. Time-Domain Analysis of Molecular Dynamics Trajectories Using Deep Neural Networks: Application to Activity Ranking of Tankyrase Inhibitors. J. Chem. Inf. Model., 2019, 59(8), 3519-3532.
[http://dx.doi.org/10.1021/acs.jcim.9b00135] [PMID: 31276400]
[70]
Senior, A.W.; Evans, R.; Jumper, J.; Kirkpatrick, J.; Sifre, L.; Green, T.; Qin, C.; Žídek, A.; Nelson, A.W.R.; Bridgland, A.; Penedones, H.; Petersen, S.; Simonyan, K.; Crossan, S.; Kohli, P.; Jones, D.T.; Silver, D.; Kavukcuoglu, K.; Hassabis, D. Improved protein structure prediction using potentials from deep learning. Nature, 2020, 577(7792), 706-710.
[http://dx.doi.org/10.1038/s41586-019-1923-7] [PMID: 31942072]
[71]
Science Magazine The game has changed. AI triumphs at solving protein structures Available at:, https://www.sciencemag.org/news/2020/11/game-has-changed-ai-triumphs- solving-protein-structures (Accessed February 23, 2021).
[72]
Protein structure prediction center. https://www.predictioncenter.org/index.cgi (Accessed February 23, 2021)
[73]
Kelley, D.R.; Snoek, J.; Rinn, J.L. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res., 2016, 26(7), 990-999.
[http://dx.doi.org/10.1101/gr.200535.115] [PMID: 27197224]
[74]
Chang, P.; Grinband, J.; Weinberg, B.D.; Bardis, M.; Khy, M.; Cadena, G.; Su, M.Y.; Cha, S.; Filippi, C.G.; Bota, D.; Baldi, P.; Poisson, L.M.; Jain, R.; Chow, D. Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas. AJNR Am. J. Neuroradiol., 2018, 39(7), 1201-1207.
[http://dx.doi.org/10.3174/ajnr.A5667] [PMID: 29748206]
[75]
Aoki, G.; Sakakibara, Y. Convolutional neural networks for classification of alignments of non-coding RNA sequences. Bioinformatics, 2018, 34(13), i237-i244.
[http://dx.doi.org/10.1093/bioinformatics/bty228] [PMID: 29949978]
[76]
Zhang, Y.; van der Werf, N.R.; Jiang, B.; van Hamersvelt, R.; Greuter, M.J.W.; Xie, X. Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study. Eur. Radiol., 2020, 30(2), 1285-1294.
[http://dx.doi.org/10.1007/s00330-019-06447-7] [PMID: 31630233]
[77]
Wang, Z.; Majewicz Fey, A. Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. Int. J. CARS, 2018, 13(12), 1959-1970.
[http://dx.doi.org/10.1007/s11548-018-1860-1] [PMID: 30255463]
[78]
Zhao, Y.; Guo, S.; Wang, Y.; Cui, J.; Ma, Y.; Zeng, Y.; Liu, X.; Jiang, Y.; Li, Y.; Shi, L.; Xiao, N. A CNN-based prototype method of unstructured surgical state perception and navigation for an endovascular surgery robot. Med. Biol. Eng. Comput., 2019, 57(9), 1875-1887.
[http://dx.doi.org/10.1007/s11517-019-02002-0] [PMID: 31222531]
[79]
Charisis, V.S.; Hadjileontiadis, L.J.; Liatsos, C.N.; Mavrogiannis, C.C.; Sergiadis, G.D. Capsule endoscopy image analysis using texture information from various colour models. Comput. Methods Programs Biomed., 2012, 107(1), 61-74.
[http://dx.doi.org/10.1016/j.cmpb.2011.10.004] [PMID: 22056811]
[80]
Wang, P.; Xiao, X.; Glissen Brown, J.R.; Berzin, T.M.; Tu, M.; Xiong, F.; Hu, X.; Liu, P.; Song, Y.; Zhang, D.; Yang, X.; Li, L.; He, J.; Yi, X.; Liu, J.; Liu, X. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat. Biomed. Eng., 2018, 2(10), 741-748.
[http://dx.doi.org/10.1038/s41551-018-0301-3] [PMID: 31015647]
[81]
Min, M.; Su, S.; He, W.; Bi, Y.; Ma, Z.; Liu, Y. Computer-aided diagnosis of colorectal polyps using linked color imaging colonoscopy to predict histology. Sci Rep., 2019, 279(1), 2881.
[http://dx.doi.org/10.1038/s41598-019-39416-7] [PMID: 30814661]
[82]
Eid, A.; Charisis, V.S.; Hadjileontiadis, L.J.; Sergiadis, G.D. A curvelet based lacunarity approach for ulcer detection from wireless capsule endoscopy images. Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, June 20–22 Porto, Portugal 2013, pp. 273-278.
[http://dx.doi.org/10.1109/CBMS.2013.6627801]
[83]
Yuan, Y.; Wang, J.; Li, B.; Meng, M.Q. Saliency based ulcer detection for wireless capsule endoscopy diagnosis. IEEE Trans. Med. Imaging, 2015, 34(10), 2046-2057.
[http://dx.doi.org/10.1109/TMI.2015.2418534] [PMID: 25850085]
[84]
Karargyris, A.; Bourbakis, N. Identification of ulcers in wireless capsule endoscopy videos. Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, June 28 2009, p. 554557.
[http://dx.doi.org/10.1109/ISBI.2009.5193107]
[85]
Chen, Y.; Lee, J. Ulcer detection in wireless capsule endoscopy video. Proceedings of the 20th ACM International Conference on Multimedia–MM ’12, 2012, , p. 1181.
[http://dx.doi.org/10.1145/2393347.2396413]
[86]
Gadermayr, M.; Kogler, H.; Karla, M.; Merhof, D.; Uhl, A.; Vécsei, A. Computer-aided texture analysis combined with experts’ knowledge: Improving endoscopic celiac disease diagnosis. World J. Gastroenterol., 2016, 22(31), 7124-7134.
[http://dx.doi.org/10.3748/wjg.v22.i31.7124] [PMID: 27610022]
[87]
Zhou, T.; Han, G.; Li, B.N.; Lin, Z.; Ciaccio, E.J.; Green, P.H.; Qin, J. Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method. Comput. Biol. Med., 2017, 85, 1-6.
[http://dx.doi.org/10.1016/j.compbiomed.2017.03.031] [PMID: 28412572]
[88]
Tenório, J.M.; Hummel, A.D.; Cohrs, F.M.; Sdepanian, V.L.; Pisa, I.T.; de Fátima Marin, H. Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease. Int. J. Med. Inform., 2011, 80(11), 793-802.
[http://dx.doi.org/10.1016/j.ijmedinf.2011.08.001] [PMID: 21917512]
[89]
Hujoel, I.A.; Murphree, D.H., Jr; Van Dyke, C.T.; Choung, R.S.; Sharma, A.; Murray, J.A.; Rubio-Tapia, A. Machine Learning in Detection of Undiagnosed Celiac Disease. Clin. Gastroenterol. Hepatol., 2018, 16(8), 1354-1355.e1.
[http://dx.doi.org/10.1016/j.cgh.2017.12.022] [PMID: 29253540]
[90]
Girgis, H.Z.; Mitchell, B.R.; Dassopoulos, T.; Mullin, G.; Hager, G. An intelligent system to detect Crohn’s disease inflammation in wireless capsule endoscopy videos. Proceedings of the 2010 7th IEEE International Symposium on Biomedical Imaging (ISBI 2010), April 14–17Rotterdam, The Netherlands 2010, pp. 1373-1376.
[http://dx.doi.org/10.1109/ISBI.2010.5490253]
[91]
Kumar, R.; Zhao, Q.; Seshamani, S.; Mullin, G.; Hager, G.; Dassopoulos, T. Assessment of Crohn’s disease lesions in wireless capsule endoscopy images. IEEE Trans. Biomed. Eng., 2012, 59(2), 355-362.
[http://dx.doi.org/10.1109/TBME.2011.2172438] [PMID: 22020661]
[92]
Jebarani, W.S.L.; Daisy, V.J. Assessment of Crohn’s disease lesions in wireless capsule endoscopy images using SVM based classification. Proceedings of the 2013 International Conference on Signal Processing, Image Processing & Pattern Recognition, February 7–8 Coimbatore, India 2013, pp. 303-307.
[http://dx.doi.org/10.1109/ICSIPR.2013.6497945]
[93]
Charisis, V.S.; Hadjileontiadis, L.J. Potential of hybrid adaptive filtering in inflammatory lesion detection from capsule endoscopy images. World J. Gastroenterol., 2016, 22(39), 8641-8657.
[http://dx.doi.org/10.3748/wjg.v22.i39.8641] [PMID: 27818583]
[94]
Wei, Z.; Wang, W.; Bradfield, J.; Li, J.; Cardinale, C.; Frackelton, E.; Kim, C.; Mentch, F.; Van Steen, K.; Visscher, P.M.; Baldassano, R.N.; Hakonarson, H. International IBD Genetics Consortium. Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease. Am. J. Hum. Genet., 2013, 92(6), 1008-1012.
[http://dx.doi.org/10.1016/j.ajhg.2013.05.002] [PMID: 23731541]
[95]
Mossotto, E.; Ashton, J.J.; Coelho, T.; Beattie, R.M.; MacArthur, B.D.; Ennis, S. Classification of Paediatric Inflammatory Bowel Disease using Machine Learning. Sci. Rep., 2017, 7(1), 2427.
[http://dx.doi.org/10.1038/s41598-017-02606-2] [PMID: 28546534]
[96]
Nawarathna, R.; Oh, J.; Muthukudage, J.; Tavanapong, W.; Wong, J.; de Groen, P.C.; Tang, S.J. Abnormal Image Detection in Endoscopy Videos Using a Filter Bank and Local Binary Patterns. Neurocomputing, 2014, 144, 70-91.
[http://dx.doi.org/10.1016/j.neucom.2014.02.064] [PMID: 25132723]
[97]
Iakovidis, D.K.; Koulaouzidis, A. Automatic lesion detection in capsule endoscopy based on color saliency: closer to an essential adjunct for reviewing software. Gastrointest. Endosc., 2014, 80(5), 877-883.
[http://dx.doi.org/10.1016/j.gie.2014.06.026] [PMID: 25088924]
[98]
Deding, U.; Herp, J.; Havshoei, A.L.; Kobaek-Larsen, M.; Buijs, M.M.; Nadimi, E.S.; Baatrup, G. Colon capsule endoscopy versus CT colonography after incomplete colonoscopy. Application of artificial intelligence algorithms to identify complete colonic investigations. United Eur. Gastroenterol. J., 2020, 8(7), 782-789.
[http://dx.doi.org/10.1177/2050640620937593] [PMID: 32731841]
[99]
Buijs, M.M.; Ramezani, M.H.; Herp, J.; Kroijer, R.; Kobaek-Larsen, M.; Baatrup, G.; Nadimi, E.S. Assessment of bowel cleansing quality in colon capsule endoscopy using machine learning: a pilot study. Endosc. Int. Open, 2018, 6(8), E1044-E1050.
[http://dx.doi.org/10.1055/a-0627-7136] [PMID: 30105292]
[100]
Li, B.; Meng, M.Q.H. Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments. Comput. Biol. Med., 2009, 39(2), 141-147.
[http://dx.doi.org/10.1016/j.compbiomed.2008.11.007] [PMID: 19147126]
[101]
Hassan, A.R.; Haque, M.A. Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos. Comput. Methods Programs Biomed., 2015, 122(3), 341-353.
[http://dx.doi.org/10.1016/j.cmpb.2015.09.005] [PMID: 26390947]
[102]
Pan, G.; Yan, G.; Qiu, X.; Cui, J. Bleeding detection in Wireless Capsule Endoscopy based on Probabilistic Neural Network. J. Med. Syst., 2011, 35(6), 1477-1484.
[http://dx.doi.org/10.1007/s10916-009-9424-0] [PMID: 20703770]
[103]
Guolan, L.V.; Yan, G.; Wang, Z. Bleeding detection in wireless capsule endoscopy images based on color invariants and spatial pyramids using support vector machines. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 30 2011, pp. 6643-6646.
[http://dx.doi.org/10.1109/IEMBS.2011.6091638]
[104]
Fu, Y.; Zhang, W.; Mandal, M.; Meng, M.Q. Computer-aided bleeding detection in WCE video. IEEE J. Biomed. Health Inform., 2014, 18(2), 636-642.
[http://dx.doi.org/10.1109/JBHI.2013.2257819] [PMID: 24608063]
[105]
Ghosh, T.; Fattah, S.A.; Shahnaz, C.; Wahid, K.A. An automatic bleeding detection scheme in wireless capsule endoscopy based on histogram of an RGB-indexed image. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2014, pp. 4683-4686.
[http://dx.doi.org/10.1109/EMBC.2014.6944669]
[106]
Sainju, S.; Bui, F.M.; Wahid, K.A. Automated bleeding detection in capsule endoscopy videos using statistical features and region growing. J. Med. Syst., 2014, 38(4), 25.
[http://dx.doi.org/10.1007/s10916-014-0025-1] [PMID: 24696394]
[107]
Jia, X.; Meng, M.Q.H. A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), August 16–20 Orlando, FL 2016, pp. 639-642.
[http://dx.doi.org/10.1109/EMBC.2016.7590783]
[108]
Usman, M.A.; Satrya, G.B.; Usman, M.R.; Shin, S.Y. Detection of small colon bleeding in wireless capsule endoscopy videos. Comput. Med. Imaging Graph., 2016, 54, 16-26.
[http://dx.doi.org/10.1016/j.compmedimag.2016.09.005] [PMID: 27793502]
[109]
Leenhardt, R.; Vasseur, P.; Li, C.; Saurin, J.C.; Rahmi, G.; Cholet, F.; Becq, A.; Marteau, P.; Histace, A.; Dray, X. CAD-CAP Database Working Group. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. Gastrointest. Endosc., 2019, 89(1), 189-194.
[http://dx.doi.org/10.1016/j.gie.2018.06.036] [PMID: 30017868]
[110]
Siegel, R.; Ma, J.; Zou, Z.; Jemal, A. Cancer statistics, 2014. CA Cancer J. Clin., 2014, 64(1), 9-29.
[http://dx.doi.org/10.3322/caac.21208] [PMID: 24399786]
[111]
Hidalgo, M. Pancreatic cancer. N. Engl. J. Med., 2010, 362(17), 1605-1617.
[http://dx.doi.org/10.1056/NEJMra0901557] [PMID: 20427809]
[112]
DiMagno, E.P.; Malagelada, J.R.; Taylor, W.F.; Go, V.L. A prospective comparison of current diagnostic tests for pancreatic cancer. N. Engl. J. Med., 1977, 297(14), 737-742.
[http://dx.doi.org/10.1056/NEJM197710062971401] [PMID: 895803]
[113]
Gudjonsson, B. Cancer of the pancreas. 50 years of surgery. Cancer, 1987, 60(9), 2284-2303.
[http://dx.doi.org/10.1002/1097-0142(19871101)60:9<2284::AID-CNCR2820600930>3.0.CO;2-V] [PMID: 3326653]
[114]
Kulkarni, N.M.; Hough, D.M.; Tolat, P.P.; Soloff, E.V.; Kambadakone, A.R. Pancreatic adenocarcinoma: cross-sectional imaging techniques. Abdom. Radiol. (NY), 2018, 43, 253-263.
[http://dx.doi.org/10.1007/s00261-017-1380-4] [PMID: 29128993]
[115]
Freeny, P.C.; Marks, W.M.; Ryan, J.A.; Traverso, L.W. Pancreatic ductal adenocarcinoma: diagnosis and staging with dynamic CT. Radiology, 1988, 166(1 Pt 1), 125-133.
[http://dx.doi.org/10.1148/radiology.166.1.2827228] [PMID: 2827228]
[116]
Das, A.; Nguyen, C.C.; Li, F.; Li, B. Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue. Gastrointest. Endosc., 2008, 67(6), 861-867.
[http://dx.doi.org/10.1016/j.gie.2007.08.036] [PMID: 18179797]
[117]
Săftoiu, A.; Vilmann, P.; Gorunescu, F.; Janssen, J.; Hocke, M.; Larsen, M.; Iglesias-Garcia, J.; Arcidiacono, P.; Will, U.; Giovannini, M.; Dietrich, C.F.; Havre, R.; Gheorghe, C.; McKay, C.; Gheonea, D.I.; Ciurea, T. European EUS Elastography Multicentric Study Group. Efficacy of an artificial neural network-based approach to endoscopic ultrasound elastography in diagnosis of focal pancreatic masses. Clin. Gastroenterol. Hepatol., 2012, 10(1), 84-90.e1.
[http://dx.doi.org/10.1016/j.cgh.2011.09.014] [PMID: 21963957]
[118]
Zhu, M.; Xu, C.; Yu, J.; Wu, Y.; Li, C.; Zhang, M.; Jin, Z.; Li, Z. Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) images: a diagnostic test. PLoS One, 2013, 8(5), e63820.
[http://dx.doi.org/10.1371/journal.pone.0063820] [PMID: 23704940]
[119]
Yang, Y.; Chen, H.; Wang, D.; Luo, W.; Zhu, B.; Zhang, Z. Diagnosis of pancreatic carcinoma based on combined measurement of multiple serum tumor markers using artificial neural network analysis. Chin. Med. J. (Engl.), 2014, 127(10), 1891-1896.
[PMID: 24824251]
[120]
Săftoiu, A.; Vilmann, P.; Dietrich, C.F.; Iglesias-Garcia, J.; Hocke, M.; Seicean, A.; Ignee, A.; Hassan, H.; Streba, C.T.; Ioncică, A.M.; Gheonea, D.I.; Ciurea, T. Quantitative contrast-enhanced harmonic EUS in differential diagnosis of focal pancreatic masses (with videos). Gastrointest. Endosc., 2015, 82(1), 59-69.
[http://dx.doi.org/10.1016/j.gie.2014.11.040] [PMID: 25792386]
[121]
Ozkan, M.; Cakiroglu, M.; Kocaman, O.; Kurt, M.; Yilmaz, B.; Can, G.; Korkmaz, U.; Dandil, E.; Eksi, Z. Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images. Endosc. Ultrasound, 2016, 5(2), 101-107.
[http://dx.doi.org/10.4103/2303-9027.180473] [PMID: 27080608]
[122]
Gatos, I.; Tsantis, S.; Spiliopoulos, S.; Karnabatidis, D.; Theotokas, I.; Zoumpoulis, P.; Loupas, T.; Hazle, J.D.; Kagadis, G.C. A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography. Ultrasound Med. Biol., 2017, 43(9), 1797-1810.
[http://dx.doi.org/10.1016/j.ultrasmedbio.2017.05.002] [PMID: 28634041]
[123]
Sowa, J.P.; Heider, D.; Bechmann, L.P.; Gerken, G.; Hoffmann, D.; Canbay, A. Novel algorithm for non-invasive assessment of fibrosis in NAFLD. PLoS One, 2013, 8(4), e62439.
[http://dx.doi.org/10.1371/journal.pone.0062439] [PMID: 23638085]
[124]
Sowa, J.P.; Atmaca, Ö.; Kahraman, A.; Schlattjan, M.; Lindner, M.; Sydor, S.; Scherbaum, N.; Lackner, K.; Gerken, G.; Heider, D.; Arteel, G.E.; Erim, Y.; Canbay, A. Non-invasive separation of alcoholic and non-alcoholic liver disease with predictive modeling. PLoS One, 2014, 9(7), e101444.
[http://dx.doi.org/10.1371/journal.pone.0101444] [PMID: 24988316]
[125]
Yip, T.C.; Ma, A.J.; Wong, V.W.; Tse, Y.K.; Chan, H.L.; Yuen, P.C.; Wong, G.L. Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population. Aliment. Pharmacol. Ther., 2017, 46(4), 447-456.
[http://dx.doi.org/10.1111/apt.14172] [PMID: 28585725]
[126]
Ma, H.; Xu, C.F.; Shen, Z.; Yu, C.H.; Li, Y.M. Application of machine learning techniques for clinical predictive modeling: cross-sectional study on nonalcoholic fatty liver disease in China. BioMed Res. Int., 2018, 2018, 4304376.
[http://dx.doi.org/10.1155/2018/4304376] [PMID: 30402478]
[127]
Wu, C.C.; Yeh, W.C.; Hsu, W.D.; Islam, M.M.; Nguyen, P.A.A.; Poly, T.N.; Wang, Y.C.; Yang, H.C.; Jack Li, Y.C. Prediction of fatty liver disease using machine learning algorithms. Comput. Methods Programs Biomed., 2019, 170, 23-29.
[http://dx.doi.org/10.1016/j.cmpb.2018.12.032] [PMID: 30712601]
[128]
Fialoke, S.; Malarstig, A.; Miller, M.R.; Dumitriu, A. 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.
[PMID: 30815083]
[129]
Piscaglia, F.; Cucchetti, A.; Benlloch, S.; Vivarelli, M.; Berenguer, J.; Bolondi, L.; Pinna, A.D.; Berenguer, M. Prediction of significant fibrosis in hepatitis C virus infected liver transplant recipients by artificial neural network analysis of clinical factors. Eur. J. Gastroenterol. Hepatol., 2006, 18(12), 1255-1261.
[http://dx.doi.org/10.1097/01.meg.0000243885.55562.7e] [PMID: 17099373]
[130]
Hashem, S.; Esmat, G.; Elakel, W.; Habashy, S.; Raouf, S.A.; Elhefnawi, M.; Eladawy, M.; ElHefnawi, M. Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2018, 15(3), 861-868.
[http://dx.doi.org/10.1109/TCBB.2017.2690848] [PMID: 28391204]
[131]
Wang, D.; Wang, Q.; Shan, F.; Liu, B.; Lu, C. Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers. BMC Infect. Dis., 2010, 10, 251.
[http://dx.doi.org/10.1186/1471-2334-10-251] [PMID: 20735842]
[132]
Chen, Y.; Luo, Y.; Huang, W.; Hu, D.; Zheng, R.Q.; Cong, S.Z.; Meng, F.K.; Yang, H.; Lin, H.J.; Sun, Y.; Wang, X.Y.; Wu, T.; Ren, J.; Pei, S.F.; Zheng, Y.; He, Y.; Hu, Y.; Yang, N.; Yan, H. Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B. Comput. Biol. Med., 2017, 89, 18-23.
[http://dx.doi.org/10.1016/j.compbiomed.2017.07.012] [PMID: 28779596]
[133]
Wei, R.; Wang, J.; Wang, X.; Xie, G.; Wang, Y.; Zhang, H.; Peng, C.Y.; Rajani, C.; Kwee, S.; Liu, P.; Jia, W. Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning. EBioMedicine, 2018, 35, 124-132.
[http://dx.doi.org/10.1016/j.ebiom.2018.07.041] [PMID: 30100397]
[134]
Wang, K.; Lu, X.; Zhou, H.; Gao, Y.; Zheng, J.; Tong, M.; Wu, C.; Liu, C.; Huang, L.; Jiang, T.; Meng, F.; Lu, Y.; Ai, H.; Xie, X.Y.; Yin, L.P.; Liang, P.; Tian, J.; Zheng, R. 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(4), 729-741.
[http://dx.doi.org/10.1136/gutjnl-2018-316204] [PMID: 29730602]
[135]
Liu, X.; Faes, L.; Kale, A.U.; Wagner, S.K.; Fu, D.J.; Bruynseels, A.; Mahendiran, T.; Moraes, G.; Shamdas, M.; Kern, C.; Ledsam, J.R.; Schmid, M.K.; Balaskas, K.; Topol, E.J.; Bachmann, L.M.; Keane, P.A.; Denniston, A.K. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health, 2019, 1(6), e271-e297.
[http://dx.doi.org/10.1016/S2589-7500(19)30123-2] [PMID: 33323251]
[136]
Shen, J.; Zhang, C.J.P.; Jiang, B.; Chen, J.; Song, J.; Liu, Z.; He, Z.; Wong, S.Y.; Fang, P.H.; Ming, W.K. Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Med. Inform., 2019, 7(3), e10010.
[http://dx.doi.org/10.2196/10010] [PMID: 31420959]

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