Differentiating Crohn’s disease from intestinal tuberculosis using a fusion correlation neural network

Knowledge-Based Systems - Tập 244 - Trang 108570 - 2022
Yinghao Chen1, Ying Li2, Minfeng Wu1, Fanggen Lu3, Muzhou Hou1, Yani Yin4,5,6
1School of Mathematics and Statistics, Central South University, Changsha, 410083, China
2Department of Infectious Diseases, Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, 410008, China
3Department of Gastroenterology, the Second Xiangya Hospital of Central South University, Changsha, 410008, China
4Department of Gastroenterology of Xiangya hospital, Central South University, Changsha 410013, China
5Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha 410013, China
6National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, 410008, China

Tài liệu tham khảo

Watermeyer, 2018, Differentiating Crohn’s disease from intestinal tuberculosis at presentation in patients with tissue granulomas, Samj South Afr. Med. J., 108, 399, 10.7196/SAMJ.2018.v108i5.13108 Meng, 2019, Analysis of phenotypic variables and differentiation between untypical Crohn’s disease and untypical intestinal tuberculosis, Dig. Dis. Sci., 64, 1967, 10.1007/s10620-019-05491-z Taylor, 1945, Chronic hypertrophic ileocaecal tuberculosis, and its relation to regional ileitis (Crohn’s disease), Br. J. Surg., 33, 178, 10.1002/bjs.18003313017 Cattell, 1946, The surgical treatment of tuberculosis of the bowel, Lahey Clin. Bull., 5, 6 Warren, 1948, Cicatrizing enteritis as a pathologic entity; analysis of 120 cases, Am. J. Pathol., 24, 475 Hoon, 1950, Ileocecal tuberculosis including a comparison of this disease with nonspecific regional enterocolitis and noncaseous tuberculated enterocolitis, Int. Abstr. Surg., 91, 417 Brenner, 1970, Tuberculous colitis simulating nonspecific granulomatous disease of the colon, Am. J. Dig. Dis., 15, 85, 10.1007/BF02239351 Nikolaus, 2007, Diagnostics of inflammatory bowel disease, Gastroenterology, 133, 1670, 10.1053/j.gastro.2007.09.001 Almadi, 2009, Differentiating intestinal tuberculosis from Crohn’s disease: A diagnostic challenge, Am. J. Gastroenterol., 104, 1003, 10.1038/ajg.2008.162 Donoghue, 2009, Intestinal tuberculosis, Curr. Opin. Infect. Dis., 22, 490, 10.1097/QCO.0b013e3283306712 Limsrivilai others, 2017, Meta-analytic Bayesian model for differentiating intestinal tuberculosis from Crohn’s disease, Am. J. Gastroenterol., 112, 415, 10.1038/ajg.2016.529 Limsrivilai, 2016, Meta-analytic Bayesian model for differentiating intestinal tuberculosis from Crohn’s disease utilising clinical, endoscopic, and cross-sectional imaging findings, and the interferon-gamma releasing assay, J. Crohns Colitis, 10, S225, 10.1093/ecco-jcc/jjw019.383 Kirsch, 2006, Role of colonoscopic biopsy in distinguishing between Crohn’s disease and intestinal tuberculosis, J. Clin. Pathol., 59, 840, 10.1136/jcp.2005.032383 Dutta, 2011, Distinguishing Crohn’s disease from intestinal tuberculosis–a prospective study, Trop. Gastroenterol. : Official J. Dig. Dis. Found., 32, 204 Yu, 2012, Clinical, endoscopic and histological differentiations between Crohn’s disease and intestinal tuberculosis, Digestion, 85, 202, 10.1159/000335431 Lei, 2013, Utility of in vitro interferon-gamma release assay in differential diagnosis between intestinal tuberculosis and Crohn’s disease, J. Dig. Dis., 14, 68, 10.1111/1751-2980.12017 Amarapurkar, 2008, Diagnosis of Crohn’s disease in India where tuberculosis is widely prevalent, World J. Gastroenterol., 14, 741, 10.3748/wjg.14.741 Ramadass, 2010, Fecal polymerase chain reaction for Mycobacterium tuberculosis IS6110 to distinguish Crohn’s disease from intestinal tuberculosis, Indian J. Gastroenterol.: Official J. Indian Soc. Gastroenterol., 29, 152, 10.1007/s12664-010-0022-3 Jin, 2010, Histopathology and TB-PCR kit analysis in differentiating the diagnosis of intestinal tuberculosis and Crohn’s disease, World J. Gastroenterol., 16, 2496, 10.3748/wjg.v16.i20.2496 Makharia, 2010, Clinical, endoscopic, and histological differentiations between Crohn’s disease and intestinal tuberculosis, Am. J. Gastroenterol., 105, 642, 10.1038/ajg.2009.585 Fei, 2014, Fluorescent quantitative PCR of Mycobacterium tuberculosis for differentiating intestinal tuberculosis from Crohn’s disease, Braz. J. Med. Biol. Res., 47, 166, 10.1590/1414-431X20133277 Limsrivilai, 2021, Intestinal tuberculosis or Crohn’s disease: a review of the diagnostic models designed to differentiate between these two gastrointestinal diseases, Intest. Res., 19, 21, 10.5217/ir.2019.09142 Ooi, 2016, Asia Pacific consensus statements on Crohn’s disease. Part 1: Definition, diagnosis, and epidemiology (Asia Pacific Crohn’s disease consensus-Part 1), J. Gastroenterol. Hepatol., 31, 45, 10.1111/jgh.12956 Banerjee, 2018, Risk factors for diagnostic delay in Crohn’s disease and their impact on long-term complications: how do they differ in a tuberculosis endemic region?, Aliment. Pharmacol. Ther., 47, 1367, 10.1111/apt.14617 Sharma, 2018, Letter: mucosal response in discriminating intestinal tuberculosis from Crohn’s disease-when to look for it?, Aliment. Pharmacol. Ther., 47, 859, 10.1111/apt.14495 Mouli, 2017, Endoscopic and clinical responses to anti-tubercular therapy can differentiate intestinal tuberculosis from Crohn’s disease, Aliment. Pharmacol. Ther., 45, 27, 10.1111/apt.13840 Lee, 2006, Analysis of colonoscopic findings in the differential diagnosis between intestinal tuberculosis and Crohn’s disease, Endoscopy, 38, 592, 10.1055/s-2006-924996 Yang, 2007, Epidemiology of inflammatory bowel disease in the Songpa-Kangdong District, Seoul, Korea, 1986–2005: A Kasid study, Gastroenterology, 132, A660 Pulimood, 2011, Differentiation of Crohn’s disease from intestinal tuberculosis in India in 2010, World J. Gastroenterol., 17, 433, 10.3748/wjg.v17.i4.433 Tandon, 1972, Pathology of intestinal tuberculosis and its distinction from Crohn’s disease, Gut, 13, 260, 10.1136/gut.13.4.260 Li, 2011, Predictors of clinical and endoscopic findings in differentiating Crohn’s disease from intestinal tuberculosis, Dig. Dis. Sci., 56, 188, 10.1007/s10620-010-1231-4 Kedia, 2015, Computerized tomography-based predictive model for differentiation of Crohn’s disease from intestinal tuberculosis, Indian J. Gastroenterol., 34, 135, 10.1007/s12664-015-0550-y Jung, 2016, Predictive factors for differentiating between Crohn’s disease and intestinal tuberculosis in Koreans, Am. J. Gastroenterol., 111, 1156, 10.1038/ajg.2016.212 Jung, 2016, Predictive factors for differentiating between Crohn’s disease and intestinal tuberculosis in Korean, Gastroenterology, 150, S552, 10.1016/S0016-5085(16)31889-3 Bae, 2017, Development and validation of a novel prediction model for differential diagnosis between Crohn’s disease and intestinal tuberculosis, Inflamm. Bowel Dis., 23, 1614, 10.1097/MIB.0000000000001162 Wu, 2018, Diagnostic performance of a 5-marker predictive model for differential diagnosis between intestinal tuberculosis and Crohn’s disease, Inflamm. Bowel Dis., 24, 2452, 10.1093/ibd/izy154 He, 2019, Development and validation of a novel diagnostic nomogram to differentiate between intestinal tuberculosis and crohn’s disease: A 6-year prospective multicenter study, Am. J. Gastroenterol., 114, 490, 10.14309/ajg.0000000000000064 Jordan, 2015, Machine learning: Trends, perspectives, and prospects, Science, 349, 255, 10.1126/science.aaa8415 Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056 Butler, 2018, Machine learning for molecular and materials science, Nature, 559, 547, 10.1038/s41586-018-0337-2 Chen, 2021, Numerical solving of the generalized Black–Scholes differential equation using Laguerre neural network, Digit. Signal Process., 112, 10.1016/j.dsp.2021.103003 Chen, 2022, Prediction of safety parameters of pressurized water reactor based on feature fusion neural network, Ann. Nucl. Energy, 166, 10.1016/j.anucene.2021.108803 Chen, 2021, Research on users’ participation mechanisms in virtual tourism communities by Bayesian network, Knowl.-Based Syst., 226, 10.1016/j.knosys.2021.107161 Strohmaier, 2020, Ontology, neural networks, and the social sciences, Synthese Zeng, 1999, Prediction and classification with neural network models, Sociol. Methods Res., 27, 499, 10.1177/0049124199027004002 DeTienne, 2003, Neural networks as statistical tools for business researchers, Organ. Res. Methods, 6, 236, 10.1177/1094428103251907 Weng, 2021, Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic, Resour. Policy, 73, 10.1016/j.resourpol.2021.102148 Silver, 2016, Mastering the game of Go with deep neural networks and tree search, Nature, 529, 484, 10.1038/nature16961 He, 2016, Deep residual learning for image recognition, 770 Chen, 2020, A deep residual compensation extreme learning machine and applications, J. Forecast., 39, 986, 10.1002/for.2663 Weng, 2020, Gold price forecasting research based on an improved online extreme learning machine algorithm, J. Ambient Intell. Humaniz. Comput., 11, 4101, 10.1007/s12652-020-01682-z Gulshan others, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, Jama-J. Am. Med. Assoc., 316, 2402, 10.1001/jama.2016.17216 Hosny, 2018, Artificial intelligence in radiology, Nat. Rev. Cancer, 18, 500, 10.1038/s41568-018-0016-5 Hannun, 2019, Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network, Nat. Med., 25, 65, 10.1038/s41591-018-0268-3 Yang, 2020, Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions, J. Thorac. Dis., 12, 165, 10.21037/jtd.2020.02.64 Zhang, 2021, A novel voting convergent difference neural network for diagnosing breast cancer, Neurocomputing, 437, 339, 10.1016/j.neucom.2021.01.083 Hara, 2015, Analysis of function of rectified linear unit used in deep learning Li, 2018, Improving deep neural network with multiple parametric exponential linear units, Neurocomputing, 301, 11, 10.1016/j.neucom.2018.01.084 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Khedr, 2016, Enhancing iterative dichotomiser 3 algorithm for classification decision tree, Wiley Interdiscip. Rev.-Data Min. Knowl. Discov., 6, 70, 10.1002/widm.1177 Jiang, 2008, A combined classification algorithm based on C4.5 and NB, vol. 5370, 350 Zhang, 2016, C4.5 or Naive Bayes: A discriminative model selection approach, vol. 9886, 419 Teixeira, 2004, Classification and regression tree, Rev. Des. Mal. Respir., 21, 1174, 10.1016/S0761-8425(04)71596-X N. Cohen-Shapira, L. Rokach, B. Shapira, G. Katz, R. Vainshtein, Acm, AutoGRD: Model recommendation through graphical dataset representation, in: Proceedings of the 28th Acm International Conference on Information & Knowledge Management, 2019, pp. 821–830. Cao, 2021, Correlation-driven framework based on graph convolutional network for clinical disease classification, J. Stat. Comput. Simul., 9, 1 Jhobta, 2006, Spectrum of perforation peritonitis in India–review of 504 consecutive cases, World J. Emerg. Surg.: WJES, 1, 10.1186/1749-7922-1-26 Yadav, 2013, Spectrum of perforation peritonitis in Delhi: 77 cases experience, Indian J. Surg., 75, 133, 10.1007/s12262-012-0609-2