{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T02:39:56Z","timestamp":1772678396014,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17246-0","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T01:02:12Z","timestamp":1697158932000},"page":"41951-41979","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Artificial intelligence-based myocardial infarction diagnosis: a comprehensive review of modern techniques"],"prefix":"10.1007","volume":"83","author":[{"given":"Hafeez Ur Rehman","family":"Siddiqui","sequence":"first","affiliation":[]},{"given":"Kainat","family":"Zafar","sequence":"additional","affiliation":[]},{"given":"Adil Ali","family":"Saleem","sequence":"additional","affiliation":[]},{"given":"Rukhshanda","family":"Sehar","sequence":"additional","affiliation":[]},{"given":"Furqan","family":"Rustam","sequence":"additional","affiliation":[]},{"given":"Sandra","family":"Dudley","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,13]]},"reference":[{"issue":"24","key":"17246_CR1","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1001\/jama.283.24.3223","volume":"283","author":"JG Canto","year":"2000","unstructured":"Canto JG, Shlipak MG, Rogers WJ, Malmgren JA, Frederick PD, Lambrew CT, Ornato JP, Barron HV, Kiefe CI (2000) Prevalence, clinical characteristics, and mortality among patients with myocardial infarction presenting without chest pain. Jama 283(24):3223\u20133229","journal-title":"Jama"},{"issue":"10065","key":"17246_CR2","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/S0140-6736(16)30677-8","volume":"389","author":"GW Reed","year":"2017","unstructured":"Reed GW, Rossi JE, Cannon CP (2017) Acute myocardial infarction. The Lancet 389(10065):197\u2013210","journal-title":"The Lancet"},{"key":"17246_CR3","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1007\/s10916-009-9314-5","volume":"34","author":"E Jayachandran","year":"2010","unstructured":"Jayachandran E, Joseph KP, Acharya UR (2010) Analysis of myocardial infarction using discrete wavelet transform. J Med Syst 34:985\u2013992","journal-title":"J Med Syst"},{"issue":"3","key":"17246_CR4","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1161\/hh1501.094396","volume":"89","author":"EE Creemers","year":"2001","unstructured":"Creemers EE, Cleutjens JP, Smits JF, Daemen MJ (2001) Matrix metalloproteinase inhibition after myocardial infarction: a new approach to prevent heart failure? Circ Res 89(3):201\u2013210","journal-title":"Circ Res"},{"issue":"2","key":"17246_CR5","first-page":"88","volume":"1","author":"S Banerjee","year":"2012","unstructured":"Banerjee S, Mitra M (2012) Cross wavelet transform based analysis of electrocardiogram signals. International Journal of Electrical, Electronics and Computer Engineering 1(2):88\u201392","journal-title":"International Journal of Electrical, Electronics and Computer Engineering"},{"key":"17246_CR6","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.compbiomed.2014.08.010","volume":"61","author":"B Liu","year":"2015","unstructured":"Liu B, Liu J, Wang G, Huang K, Li F, Zheng Y, Luo Y, Zhou F (2015) A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Comput Biol Med 61:178\u2013184","journal-title":"Comput Biol Med"},{"key":"17246_CR7","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.ins.2021.05.035","volume":"571","author":"M Hammad","year":"2021","unstructured":"Hammad M, Kandala RN, Abdelatey A, Abdar M, Zomorodi-Moghadam M, San Tan R, Acharya UR, P\u0142awiak J, Tadeusiewicz R, Makarenkov V et al (2021) Automated detection of shockable ecg signals: a review. Inf Sci 571:580\u2013604","journal-title":"Inf Sci"},{"key":"17246_CR8","doi-asserted-by":"crossref","unstructured":"Bousseljot R, Kreiseler D, Schnabel A (1995) Nutzung der ekg-signaldatenbank cardiodat der ptb \u00fcber das internet","DOI":"10.1515\/bmte.1994.39.s1.250"},{"key":"17246_CR9","doi-asserted-by":"crossref","unstructured":"Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology 2(4)","DOI":"10.1136\/svn-2017-000101"},{"issue":"10","key":"17246_CR10","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","volume":"2","author":"K-H Yu","year":"2018","unstructured":"Yu K-H, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nature biomedical engineering 2(10):719\u2013731","journal-title":"Nature biomedical engineering"},{"issue":"2","key":"17246_CR11","doi-asserted-by":"crossref","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","volume":"6","author":"T Davenport","year":"2019","unstructured":"Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future healthcare journal 6(2):94","journal-title":"Future healthcare journal"},{"issue":"3","key":"17246_CR12","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.tcm.2021.02.002","volume":"32","author":"JM Ribeiro","year":"2022","unstructured":"Ribeiro JM, Astudillo P, de Backer O, Budde R, Nuis RJ, Goudzwaard J, Van Mieghem NM, Lumens J, Mortier P, Mattace-Raso F et al (2022) Artificial intelligence and transcatheter interventions for structural heart disease: a glance at the (near) future. Trends in cardiovascular medicine 32(3):153\u2013159","journal-title":"Trends in cardiovascular medicine"},{"issue":"6245","key":"17246_CR13","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255\u2013260","journal-title":"Science"},{"issue":"21","key":"17246_CR14","doi-asserted-by":"crossref","first-page":"9575","DOI":"10.1109\/JSEN.2019.2928777","volume":"19","author":"AS Alharthi","year":"2019","unstructured":"Alharthi AS, Yunas SU, Ozanyan KB (2019) Deep learning for monitoring of human gait: a review. IEEE Sensors J 19(21):9575\u20139591","journal-title":"IEEE Sensors J"},{"issue":"4","key":"17246_CR15","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/S0002-9343(02)01185-3","volume":"113","author":"JP Hellermann","year":"2002","unstructured":"Hellermann JP, Jacobsen SJ, Gersh BJ, Rodeheffer RJ, Reeder GS et al (2002) Heart failure after myocardial infarction: a review. The American journal of medicine 113(4):324\u2013330","journal-title":"The American journal of medicine"},{"issue":"14","key":"17246_CR16","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1016\/j.jacc.2019.02.018","volume":"73","author":"Y Sandoval","year":"2019","unstructured":"Sandoval Y, Jaffe AS (2019) Type 2 myocardial infarction: Jacc review topic of the week. J Am Coll Cardiol 73(14):1846\u20131860","journal-title":"J Am Coll Cardiol"},{"key":"17246_CR17","doi-asserted-by":"crossref","unstructured":"Lewandrowski K, Chen A, Januzzi J (2002) Cardiac markers for myocardial infarction: a brief review. Pathology Patterns Reviews 118(suppl_1):S93\u2013S99","DOI":"10.1309\/3EK7-YVV9-228C-E1XT"},{"issue":"1","key":"17246_CR18","first-page":"139","volume":"65","author":"G Hankins","year":"1985","unstructured":"Hankins G, Wendel GD Jr, Leveno KJ, Stoneham J (1985) Myocardial infarction during pregnancy: a review. Obstet Gynecol 65(1):139\u2013146","journal-title":"Obstet Gynecol"},{"issue":"1","key":"17246_CR19","first-page":"9","volume":"1","author":"E Braunwald","year":"2012","unstructured":"Braunwald E (2012) The treatment of acute myocardial infarction: the past, the present, and the future. European Heart Journal: Acute Cardiovascular Care 1(1):9\u201312","journal-title":"European Heart Journal: Acute Cardiovascular Care"},{"issue":"7","key":"17246_CR20","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1109\/TBME.2015.2405134","volume":"62","author":"L Sharma","year":"2015","unstructured":"Sharma L, Tripathy R, Dandapat S (2015) Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans Biomed Eng 62(7):1827\u20131837","journal-title":"IEEE Trans Biomed Eng"},{"issue":"9","key":"17246_CR21","doi-asserted-by":"crossref","first-page":"488","DOI":"10.3390\/e19090488","volume":"19","author":"M Kumar","year":"2017","unstructured":"Kumar M, Pachori RB, Acharya UR (2017) Automated diagnosis of myocardial infarction ecg signals using sample entropy in flexible analytic wavelet transform framework. Entropy 19(9):488","journal-title":"Entropy"},{"key":"17246_CR22","doi-asserted-by":"crossref","unstructured":"Diker A, C\u00f6mert Z, Avci E, Velappan S (2018) Intelligent system based on genetic algorithm and support vector machine for detection of myocardial infarction from ecg signals. In 2018 26th Signal processing and communications applications conference (SIU). Ieee, pp 1\u20134","DOI":"10.1109\/SIU.2018.8404299"},{"key":"17246_CR23","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cmpb.2019.03.012","volume":"175","author":"C Han","year":"2019","unstructured":"Han C, Shi L (2019) Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features. Comput Methods Prog Biomed 175:9\u201323","journal-title":"Comput Methods Prog Biomed"},{"issue":"6","key":"17246_CR24","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1007\/s11517-021-02372-4","volume":"59","author":"G Valizadeh","year":"2021","unstructured":"Valizadeh G, Babapour Mofrad F, Shalbaf A (2021) Parametric-based feature selection via spherical harmonic coefficients for the left ventricle myocardial infarction screening. Med Biol Eng Comput 59(6):1261\u20131283","journal-title":"Med Biol Eng Comput"},{"key":"17246_CR25","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104273","volume":"80","author":"O Attallah","year":"2023","unstructured":"Attallah O, Ragab DA (2023) Auto-myin: automatic diagnosis of myocardial infarction via multiple glcms, cnns, and svms. Biomedical Signal Processing and Control 80:104273","journal-title":"Biomedical Signal Processing and Control"},{"key":"17246_CR26","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120368","volume":"228","author":"G Valizadeh","year":"2023","unstructured":"Valizadeh G, Mofrad FB (2023) Parametrized pre-trained network (ppnet): a novel shape classification method using spharms for mi detection. Expert Syst Appl 228:120368","journal-title":"Expert Syst Appl"},{"key":"17246_CR27","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.knosys.2016.01.040","volume":"99","author":"UR Acharya","year":"2016","unstructured":"Acharya UR, Fujita H, Sudarshan VK, Oh SL, Adam M, Koh JE, Tan JH, Ghista DN, Martis RJ, Chua CK et al (2016) Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl-Based Syst 99:146\u2013156","journal-title":"Knowl-Based Syst"},{"key":"17246_CR28","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ins.2016.10.013","volume":"377","author":"UR Acharya","year":"2017","unstructured":"Acharya UR, Fujita H, Adam M, Lih OS, Sudarshan VK, Hong TJ, Koh JE, Hagiwara Y, Chua CK, Poo CK et al (2017) Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ecg signals: a comparative study. Inf Sci 377:17\u201329","journal-title":"Inf Sci"},{"issue":"23","key":"17246_CR29","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. circulation 101(23):e215\u2013e220","journal-title":"circulation"},{"issue":"4","key":"17246_CR30","doi-asserted-by":"crossref","first-page":"R1078","DOI":"10.1152\/ajpregu.1996.271.4.R1078","volume":"271","author":"N Iyengar","year":"1996","unstructured":"Iyengar N, Peng C, Morin R, Goldberger AL, Lipsitz LA (1996) Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 271(4):R1078\u2013R1084","journal-title":"American Journal of Physiology-Regulatory, Integrative and Comparative Physiology"},{"key":"17246_CR31","doi-asserted-by":"crossref","unstructured":"Zhang J, Lin F, Xiong P, Du H, Zhang H, Liu M, Hou Z, Liu X (2019) Automated detection and localization of myocardial infarction with staked sparse autoencoder and treebagger. IEEE Access 7:70 634\u201370 642","DOI":"10.1109\/ACCESS.2019.2919068"},{"key":"17246_CR32","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1007\/s11760-019-01617-y","volume":"14","author":"Z Lin","year":"2020","unstructured":"Lin Z, Gao Y, Chen Y, Ge Q, Mahara G, Zhang J (2020) Automated detection of myocardial infarction using robust features extracted from 12-lead ecg. SIViP 14:857\u2013865","journal-title":"SIViP"},{"key":"17246_CR33","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2020.106621","volume":"84","author":"I Kayikcioglu","year":"2020","unstructured":"Kayikcioglu I, Akdeniz F, K\u00f6se C, Kayikcioglu T (2020) Time-frequency approach to ecg classification of myocardial infarction. Comput Electr Eng 84:106621","journal-title":"Comput Electr Eng"},{"key":"17246_CR34","doi-asserted-by":"crossref","unstructured":"Mohd\u00a0Faizal AS, Hon WY, Thevarajah TM, Khor SM, Chang S-W (2023) A biomarker discovery of acute myocardial infarction using feature selection and machine learning. Med Biol Eng Comput, pp 1\u201315","DOI":"10.1007\/s11517-023-02841-y"},{"key":"17246_CR35","doi-asserted-by":"crossref","unstructured":"Reasat T, Shahnaz C (2017) Detection of inferior myocardial infarction using shallow convolutional neural networks. In 2017 IEEE region 10 humanitarian technology conference (R10-HTC). IEEE, pp 718\u2013721","DOI":"10.1109\/R10-HTC.2017.8289058"},{"key":"17246_CR36","doi-asserted-by":"crossref","unstructured":"Gupta A, Huerta E, Zhao Z, Moussa I (2021) Deep learning for cardiologist-level myocardial infarction detection in electrocardiograms. In 8th European medical and biological engineering conference: proceedings of the EMBEC 2020, November 29\u2013December 3, 2020 Portoro\u017e, Slovenia. Springer, pp 341\u2013355","DOI":"10.1007\/978-3-030-64610-3_40"},{"issue":"12","key":"17246_CR37","doi-asserted-by":"crossref","first-page":"4509","DOI":"10.1109\/JSEN.2019.2896308","volume":"19","author":"RK Tripathy","year":"2019","unstructured":"Tripathy RK, Bhattacharyya A, Pachori RB (2019) A novel approach for detection of myocardial infarction from ecg signals of multiple electrodes. IEEE Sensors J 19(12):4509\u20134517","journal-title":"IEEE Sensors J"},{"key":"17246_CR38","doi-asserted-by":"crossref","unstructured":"Zhang G, Si Y, Wang D, Yang W, Sun Y (2019) Automated detection of myocardial infarction using a gramian angular field and principal component analysis network. IEEE Access 7:171\u00a0570\u2013171\u00a0583","DOI":"10.1109\/ACCESS.2019.2955555"},{"issue":"9","key":"17246_CR39","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.3390\/app9091879","volume":"9","author":"K Feng","year":"2019","unstructured":"Feng K, Pi X, Liu H, Sun K (2019) Myocardial infarction classification based on convolutional neural network and recurrent neural network. Appl Sci 9(9):1879","journal-title":"Appl Sci"},{"issue":"2","key":"17246_CR40","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1109\/JBHI.2019.2910082","volume":"24","author":"W Liu","year":"2019","unstructured":"Liu W, Wang F, Huang Q, Chang S, Wang H, He J (2019) Mfb-cbrnn: a hybrid network for mi detection using 12-lead ecgs. IEEE journal of biomedical and health informatics 24(2):503\u2013514","journal-title":"IEEE journal of biomedical and health informatics"},{"key":"17246_CR41","volume":"185","author":"C Han","year":"2020","unstructured":"Han C, Shi L (2020) Ml-resnet: a novel network to detect and locate myocardial infarction using 12 leads ecg. Comput Methods Prog Biomed 185:105138","journal-title":"Comput Methods Prog Biomed"},{"issue":"4","key":"17246_CR42","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.3390\/s20041020","volume":"20","author":"L Fu","year":"2020","unstructured":"Fu L, Lu B, Nie B, Peng Z, Liu H, Pi X (2020) Hybrid network with attention mechanism for detection and location of myocardial infarction based on 12-lead electrocardiogram signals. Sensors 20(4):1020","journal-title":"Sensors"},{"key":"17246_CR43","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104457","volume":"134","author":"V Jahmunah","year":"2021","unstructured":"Jahmunah V, Ng EYK, San TR, Acharya UR (2021) Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using gaborcnn model with ecg signals. Comput Biol Med 134:104457","journal-title":"Comput Biol Med"},{"key":"17246_CR44","volume":"185","author":"Y-C Kim","year":"2020","unstructured":"Kim Y-C, Kim KR, Choe YH (2020) Automatic myocardial segmentation in dynamic contrast enhanced perfusion mri using monte carlo dropout in an encoder-decoder convolutional neural network. Comput Methods Prog Biomed 185:105150","journal-title":"Comput Methods Prog Biomed"},{"issue":"3","key":"17246_CR45","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1097\/PAF.0000000000000672","volume":"42","author":"J Garland","year":"2021","unstructured":"Garland J, Hu M, Duffy M, Kesha K, Glenn C, Morrow P, Stables S, Ondruschka B, Da Broi U, Tse RD (2021) Classifying microscopic acute and old myocardial infarction using convolutional neural networks. The American Journal of Forensic Medicine and Pathology 42(3):230\u2013234","journal-title":"The American Journal of Forensic Medicine and Pathology"},{"key":"17246_CR46","doi-asserted-by":"crossref","unstructured":"Degerli A, Zabihi M, Kiranyaz S, Hamid T, Mazhar R, Hamila R, Gabbouj M (2021) Early detection of myocardial infarction in low-quality echocardiography. IEEE Access 9:34\u00a0442\u201334\u00a0453","DOI":"10.1109\/ACCESS.2021.3059595"},{"key":"17246_CR47","volume":"198","author":"Y Guo","year":"2021","unstructured":"Guo Y, Du G-Q, Shen W-Q, Du C, He P-N, Siuly S (2021) Automatic myocardial infarction detection in contrast echocardiography based on polar residual network. Comput Methods Prog Biomed 198:105791","journal-title":"Comput Methods Prog Biomed"},{"key":"17246_CR48","doi-asserted-by":"crossref","unstructured":"Hammad M, Alkinani MH, Gupta B, Abd El-Latif AA (2021) Myocardial infarction detection based on deep neural network on imbalanced data. Multimedia Systems, pp 1\u201313","DOI":"10.1007\/s00530-020-00728-8"},{"key":"17246_CR49","doi-asserted-by":"crossref","unstructured":"Alghamdi A, Hammad M, Ugail H, Abdel-Raheem A, Muhammad K, Khalifa HS, Abd El-Latif AA (2020) Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multimedia tools and applications, pp 1\u201322","DOI":"10.1007\/s11042-020-08769-x"},{"key":"17246_CR50","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.ins.2017.06.027","volume":"415","author":"UR Acharya","year":"2017","unstructured":"Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals. Inf Sci 415:190\u2013198","journal-title":"Inf Sci"},{"issue":"5","key":"17246_CR51","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1109\/JBHI.2017.2771768","volume":"22","author":"W Liu","year":"2017","unstructured":"Liu W, Zhang M, Zhang Y, Liao Y, Huang Q, Chang S, Wang H, He J (2017) Real-time multilead convolutional neural network for myocardial infarction detection. IEEE journal of biomedical and health informatics 22(5):1434\u20131444","journal-title":"IEEE journal of biomedical and health informatics"},{"key":"17246_CR52","volume":"213","author":"FB Mofrad","year":"2023","unstructured":"Mofrad FB, Valizadeh G (2023) Densenet-based transfer learning for lv shape classification: introducing a novel information fusion and data augmentation using statistical shape\/color modeling. Expert Syst Appl 213:119261","journal-title":"Expert Syst Appl"},{"issue":"11","key":"17246_CR53","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng P-A, Cetin I, Lekadir K, Camara O, Ballester MAG et al (2018) Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37(11):2514\u20132525","journal-title":"IEEE Trans Med Imaging"},{"key":"17246_CR54","doi-asserted-by":"crossref","first-page":"2409","DOI":"10.1007\/s00330-003-1957-x","volume":"13","author":"K Scheffler","year":"2003","unstructured":"Scheffler K, Lehnhardt S (2003) Principles and applications of balanced ssfp techniques. Eur Radiol 13:2409\u20132418","journal-title":"Eur Radiol"},{"issue":"4","key":"17246_CR55","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3390\/data5040089","volume":"5","author":"A Lalande","year":"2020","unstructured":"Lalande A, Chen Z, Decourselle T, Qayyum A, Pommier T, Lorgis L, de La Rosa E, Cochet A, Cottin Y, Ginhac D et al (2020) Emidec: a database usable for the automatic evaluation of myocardial infarction from delayed-enhancement cardiac mri. Data 5(4):89","journal-title":"Data"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17246-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17246-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17246-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T13:34:55Z","timestamp":1712237695000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17246-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,13]]},"references-count":55,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["17246"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17246-0","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,13]]},"assertion":[{"value":"30 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interests"}}]}}