{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T17:09:49Z","timestamp":1746292189557},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T00:00:00Z","timestamp":1611532800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T00:00:00Z","timestamp":1611532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s11227-021-03630-w","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T09:07:47Z","timestamp":1611565667000},"page":"8674-8693","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Adoption value of deep learning and serological indicators in the screening of atrophic gastritis based on artificial intelligence"],"prefix":"10.1007","volume":"77","author":[{"given":"Jianhai","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suna","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinhua","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,25]]},"reference":[{"issue":"8","key":"3630_CR1","first-page":"756","volume":"51","author":"F Yao","year":"2017","unstructured":"Yao F, Shi CL, Liu CC et al (2017) Economic burden of stomach cancer in China during 1996\u20132015: a systematic review. Zhonghua Yu Fang Yi Xue Za Zhi 51(8):756\u2013762","journal-title":"Zhonghua Yu Fang Yi Xue Za Zhi"},{"issue":"3","key":"3630_CR2","doi-asserted-by":"publisher","first-page":"e12483","DOI":"10.1111\/hel.12483","volume":"23","author":"K Venneman","year":"2018","unstructured":"Venneman K, Huybrechts I, Gunter MJ et al (2018) The epidemiology of Helicobacter pylori infection in Europe and the impact of lifestyle on its natural evolution toward stomach cancer after infection: a systematic review. Helicobacter 23(3):e12483","journal-title":"Helicobacter"},{"issue":"3","key":"3630_CR3","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1615\/JEnvironPatholToxicolOncol.2018026839","volume":"37","author":"Y Li","year":"2018","unstructured":"Li Y, Xia R, Zhang B, Li C (2018) Chronic atrophic gastritis: a review. J Environ Pathol Toxicol Oncol 37(3):241\u2013259","journal-title":"J Environ Pathol Toxicol Oncol"},{"issue":"8","key":"3630_CR4","first-page":"100","volume":"89","author":"KI Rodriguez-Castro","year":"2018","unstructured":"Rodriguez-Castro KI, Franceschi M, Miraglia C et al (2018) Autoimmune diseases in autoimmune atrophic gastritis. Acta Biomed 89(8):100\u2013103","journal-title":"Acta Biomed"},{"issue":"1","key":"3630_CR5","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1002\/ijc.31667","volume":"144","author":"S Tahara","year":"2019","unstructured":"Tahara S, Tahara T, Horiguchi N et al (2019) DNA methylation accumulation in gastric mucosa adjacent to cancer after Helicobacter pylori eradication. Int J Cancer 144(1):80\u201388","journal-title":"Int J Cancer"},{"issue":"11","key":"3630_CR6","doi-asserted-by":"publisher","first-page":"4364","DOI":"10.1007\/s00464-013-3042-0","volume":"27","author":"Y Xuan","year":"2013","unstructured":"Xuan Y, Hur H, Byun CS et al (2013) Efficacy of intraoperative gastroscopy for tumor localization in totally laparoscopic distal gastrectomy for cancer in the middle third of the stomach. Surg Endosc 27(11):4364\u20134370","journal-title":"Surg Endosc"},{"issue":"4","key":"3630_CR7","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/s10916-019-1223-7","volume":"43","author":"R Thillaikkarasi","year":"2019","unstructured":"Thillaikkarasi R, Saravanan S (2019) An enhancement of deep learning algorithm for brain tumor segmentation using kernel based CNN with M-SVM. J Med Syst 43(4):84","journal-title":"J Med Syst"},{"key":"3630_CR8","first-page":"1998","volume":"2017","author":"S Hussain","year":"2017","unstructured":"Hussain S, Anwar SM, Majid M (2017) Brain tumor segmentation using cascaded deep convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc 2017:1998\u20132001","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"issue":"1","key":"3630_CR9","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1159\/000441742","volume":"93","author":"Y Yamaguchi","year":"2016","unstructured":"Yamaguchi Y, Nagata Y, Hiratsuka R et al (2016) Gastric cancer screening by combined assay for serum anti-Helicobacter pylori IgG antibody and serum pepsinogen levels-the ABC method. Digestion 93(1):13\u201318","journal-title":"Digestion"},{"issue":"8","key":"3630_CR10","doi-asserted-by":"publisher","first-page":"e016999","DOI":"10.1136\/bmjopen-2017-016999","volume":"7","author":"M Leja","year":"2017","unstructured":"Leja M, Park JY, Murillo R et al (2017) Multicentric randomised study of Helicobacter pylori eradication and pepsinogen testing for prevention of gastric cancer mortality: the GISTAR study. BMJ Open 7(8):e016999","journal-title":"BMJ Open"},{"issue":"3","key":"3630_CR11","first-page":"628","volume":"26","author":"A Begum","year":"2017","unstructured":"Begum A, Baten MA, Begum Z et al (2017) Role of serum pepsinogen I and II ratio in screening of gastric carcinoma. Mymensingh Med J 26(3):628\u2013634","journal-title":"Mymensingh Med J"},{"issue":"3","key":"3630_CR12","doi-asserted-by":"publisher","first-page":"104","DOI":"10.4166\/kjg.2018.72.3.104","volume":"72","author":"K Yoon","year":"2018","unstructured":"Yoon K, Kim N (2018) Reversibility of atrophic gastritis and intestinal metaplasia by eradication of Helicobacter pylori. Korean J Gastroenterol 72(3):104\u2013115","journal-title":"Korean J Gastroenterol"},{"issue":"15","key":"3630_CR13","doi-asserted-by":"publisher","first-page":"e117","DOI":"10.3346\/jkms.2018.33.e117","volume":"33","author":"EH Jin","year":"2018","unstructured":"Jin EH, Chung SJ, Lim JH (2018) Training effect on the inter-observer agreement in endoscopic diagnosis and grading of atrophic gastritis according to level of endoscopic experience. J Korean Med Sci 33(15):e117","journal-title":"J Korean Med Sci"},{"issue":"5","key":"3630_CR14","doi-asserted-by":"publisher","first-page":"2020","DOI":"10.1111\/hel.12727","volume":"25","author":"N Chapelle","year":"2020","unstructured":"Chapelle N, Petryszyn P, Blin J, Leroy M, Tamara Matysiak\u3023udnik (2020) A panel of stomach: specific biomarkers (gastropanel) for the diagnosis of atrophic gastritis: a prospective, multicenter study in a low gastric cancer incidence area. Helicobacter 25(5):2020","journal-title":"Helicobacter"},{"issue":"7","key":"3630_CR15","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1111\/apt.14248","volume":"46","author":"RM Zagari","year":"2017","unstructured":"Zagari RM, Rabitti S, Greenwood DC et al (2017) Systematic review with meta-analysis: diagnostic performance of the combination of pepsinogen, gastrin-17 and anti-Helicobacter pylori antibodies serum assays for the diagnosis of atrophic gastritis. Aliment Pharmacol Ther 46(7):657\u2013667","journal-title":"Aliment Pharmacol Ther"},{"issue":"1","key":"3630_CR16","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1186\/s12876-017-0641-6","volume":"17","author":"Y Tong","year":"2017","unstructured":"Tong Y, Wu Y, Song Z et al (2017) The potential value of serum pepsinogen for the diagnosis of atrophic gastritis among the health check-up populations in China: a diagnostic clinical research. BMC Gastroenterol 17(1):88","journal-title":"BMC Gastroenterol"},{"issue":"4","key":"3630_CR17","doi-asserted-by":"publisher","first-page":"563","DOI":"10.3748\/wjg.v23.i4.563","volume":"23","author":"F Cavalcoli","year":"2017","unstructured":"Cavalcoli F, Zilli A, Conte D, Massironi S (2017) Micronutrient deficiencies in patients with chronic atrophic autoimmune gastritis: A review. World J Gastroenterol 23(4):563\u2013572","journal-title":"World J Gastroenterol"},{"issue":"9","key":"3630_CR18","first-page":"699","volume":"111","author":"S P\u00e9rezRomero","year":"2019","unstructured":"P\u00e9rezRomero S, Alberca de Las Parras F, S\u00e1nchezDelR\u00edo A et al (2019) Quality indicators in gastroscopy. Gastroscopy procedure. Rev Esp Enferm Dig 111(9):699\u2013709","journal-title":"Rev Esp Enferm Dig"},{"issue":"6","key":"3630_CR19","doi-asserted-by":"publisher","first-page":"E138","DOI":"10.1055\/a-0866-9051","volume":"51","author":"K Nishihara","year":"2019","unstructured":"Nishihara K, Oono Y, Kuwata T et al (2019) Depressed gastric-type adenoma in nonatrophic gastric mucosa without Helicobacter pylori infection. Endoscopy 51(6):E138\u2013E140","journal-title":"Endoscopy"},{"issue":"4","key":"3630_CR20","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.jcjo.2018.04.019","volume":"53","author":"PS Grewal","year":"2018","unstructured":"Grewal PS, Oloumi F, Rubin U, Tennant MTS (2018) Deep learning in ophthalmology: a review. Can J Ophthalmol 53(4):309\u2013313","journal-title":"Can J Ophthalmol"},{"issue":"8 Pt 1","key":"3630_CR21","doi-asserted-by":"publisher","first-page":"1549","DOI":"10.1016\/j.jcmg.2019.06.009","volume":"12","author":"G Litjens","year":"2019","unstructured":"Litjens G, Ciompi F, Wolterink JM et al (2019) State-of-the-art deep learning in cardiovascular image analysis. JACC Cardiovasc Imaging 12(8 Pt 1):1549\u20131565","journal-title":"JACC Cardiovasc Imaging"},{"key":"3630_CR22","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1007\/s11036-020-01550-2","volume":"25","author":"M Kumar","year":"2020","unstructured":"Kumar M, Alshehri M, Alghamdi R, Sharma P, Deep V (2020) A de-ann inspired skin cancer detection approach using fuzzy c-means clustering. Mobile Netw Appl 25:1319\u20131329","journal-title":"Mobile Netw Appl"},{"issue":"9","key":"3630_CR23","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1016\/j.ajpath.2019.05.007","volume":"189","author":"S Wang","year":"2019","unstructured":"Wang S, Yang DM, Rong R, Zhan X, Xiao G (2019) Pathology image analysis using segmentation deep learning algorithms. Am J Pathol 189(9):1686\u20131698","journal-title":"Am J Pathol"},{"issue":"1","key":"3630_CR24","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1002\/mp.13264","volume":"46","author":"B Sahiner","year":"2019","unstructured":"Sahiner B, Pezeshk A, Hadjiiski LM et al (2019) Deep learning in medical imaging and radiation therapy. Med Phys 46(1):e1\u2013e36","journal-title":"Med Phys"},{"key":"3630_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2017.09.005","volume":"153","author":"Y Xiao","year":"2018","unstructured":"Xiao Y, Wu J, Lin Z, Zhao X (2018) A deep learning-based multi-model ensemble method for cancer prediction. Comput Methods Programs Biomed 153:1\u20139","journal-title":"Comput Methods Programs Biomed"},{"key":"3630_CR26","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1109\/TIP.2017.2749145","volume":"153","author":"SL Al-Khafaji","year":"2018","unstructured":"Al-Khafaji SL, Jun Z, Zia A, Liew AW (2018) Spectral-spatial scale invariant feature transform for hyperspectral images. IEEE Trans Image Process 27(2):837\u2013850","journal-title":"IEEE Trans Image Process"},{"key":"3630_CR27","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.compbiomed.2019.01.026","volume":"107","author":"Q Zhou","year":"2019","unstructured":"Zhou Q, Zhou Z, Chen C et al (2019) Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images. Comput Biol Med 107:47\u201357","journal-title":"Comput Biol Med"},{"issue":"20","key":"3630_CR28","doi-asserted-by":"publisher","first-page":"e10820","DOI":"10.1097\/MD.0000000000010820","volume":"97","author":"W Su","year":"2018","unstructured":"Su W, Zhou B, Qin G et al (2018) Low PG I\/II ratio as a marker of atrophic gastritis: association with nutritional and metabolic status in healthy people. Medicine (Baltimore) 97(20):e10820","journal-title":"Medicine (Baltimore)"},{"issue":"1","key":"3630_CR29","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1515\/bmc-2019-0010","volume":"10","author":"F Mansour-Ghanaei","year":"2019","unstructured":"Mansour-Ghanaei F, Joukar F, Baghaee M, Sepehrimanesh M, Hojati A (2019) Only serum pepsinogen I and pepsinogen I\/II ratio are specific and sensitive biomarkers for screening of gastric cancer. Biomol Concepts 10(1):82\u201390","journal-title":"Biomol Concepts"},{"issue":"12","key":"3630_CR30","doi-asserted-by":"publisher","first-page":"3825","DOI":"10.31557\/APJCP.2019.20.12.3825","volume":"20","author":"L Mezmale","year":"2019","unstructured":"Mezmale L, Isajevs S, Bogdanova I et al (2019) Prevalence of atrophic gastritis in Kazakhstan and the accuracy of pepsinogen tests to detect gastric mucosal atrophy. Asian Pac J Cancer Prev 20(12):3825\u20133829","journal-title":"Asian Pac J Cancer Prev"},{"issue":"2","key":"3630_CR31","first-page":"137","volume":"19","author":"S Massarrat","year":"2016","unstructured":"Massarrat S, Haj-Sheykholeslami A (2016) Increased serum pepsinogen II level as a marker of pangastritis and corpus-predominant gastritis in gastric cancer prevention. Arch Iran Med 19(2):137\u2013140","journal-title":"Arch Iran Med"},{"issue":"7","key":"3630_CR32","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1111\/apt.14248","volume":"46","author":"RM Zagari","year":"2017","unstructured":"Zagari RM, Rabitti S, Greenwood DC, Eusebi LH, Vestito A, Bazzoli F (2017) Systematic review with meta-analysis: diagnostic performance of the combination of pepsinogen, gastrin-17 and anti-Helicobacter pylori antibodies serum assays for the diagnosis of atrophic gastritis. Aliment Pharmacol Ther 46(7):657\u2013667","journal-title":"Aliment Pharmacol Ther"},{"key":"3630_CR33","doi-asserted-by":"publisher","first-page":"102245","DOI":"10.1016\/j.nano.2020.102245","volume":"29","author":"X Shao","year":"2020","unstructured":"Shao X, Zhang H, Wang Y et al (2020) Deep convolutional neural networks combine Raman spectral signature of serum for prostate cancer bone metastases screening. Nanomedicine 29:102245","journal-title":"Nanomedicine"},{"issue":"20","key":"3630_CR34","doi-asserted-by":"publisher","first-page":"4876","DOI":"10.7150\/jca.28769","volume":"10","author":"Q Guan","year":"2019","unstructured":"Guan Q, Wang Y, Ping B et al (2019) Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J Cancer 10(20):4876\u20134882","journal-title":"J Cancer"},{"key":"3630_CR35","doi-asserted-by":"publisher","first-page":"4629859","DOI":"10.1155\/2019\/4629859","volume":"2019","author":"AM Dawud","year":"2019","unstructured":"Dawud AM, Yurtkan K, Oztoprak H (2019) Application of deep learning in neuroradiology: brain haemorrhage classification using transfer learning. Comput Intell Neurosci 2019:4629859","journal-title":"Comput Intell Neurosci"},{"issue":"2","key":"3630_CR36","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1148\/radiol.2018180958","volume":"290","author":"Y Ding","year":"2019","unstructured":"Ding Y, Sohn JH, Kawczynski MG et al (2019) A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology 290(2):456\u2013464","journal-title":"Radiology"},{"key":"3630_CR37","first-page":"55","volume":"264","author":"C Brito","year":"2019","unstructured":"Brito C, Machado A, Sousa A (2019) Electrocardiogram beat-classification based on a ResNet network. Stud Health Technol Inform 264:55\u201359","journal-title":"Stud Health Technol Inform"},{"key":"3630_CR38","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/j.patcog.2018.08.012","volume":"86","author":"J Cai","year":"2019","unstructured":"Cai J, Xing F, Batra A et al (2019) Texture analysis for muscular dystrophy classification in MRI with improved class activation mapping. Pattern Recognit 86:368\u2013375","journal-title":"Pattern Recognit"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-03630-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-021-03630-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-03630-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T21:17:29Z","timestamp":1626470249000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-021-03630-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,25]]},"references-count":38,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["3630"],"URL":"https:\/\/doi.org\/10.1007\/s11227-021-03630-w","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,25]]},"assertion":[{"value":"10 January 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}