{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:02:50Z","timestamp":1740135770792,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"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":["J Digit Imaging"],"DOI":"10.1007\/s10278-023-00872-3","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T17:01:42Z","timestamp":1687885302000},"page":"2113-2124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8185-0610","authenticated-orcid":false,"given":"Qiling","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bilian","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"issue":"4","key":"872_CR1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.jalz.2016.03.001","volume":"12","author":"J Gaugler","year":"2016","unstructured":"Gaugler, J., James, B., Johnson, T., Scholz, K., Weuve J.: Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s & Dementia, 12(4): 459\u2013509, 2016.","journal-title":"Alzheimer\u2019s & Dementia"},{"issue":"3","key":"872_CR2","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1038\/nrneurol.2015.250","volume":"12","author":"H Hampel","year":"2016","unstructured":"Hampel, H., Lista, S.: Dementia: the rising global tide of cognitive impairment. Nature Rev. Neurol., 12 (3): 131\u2013132, 2016.","journal-title":"Nature Rev. Neurol."},{"issue":"1","key":"872_CR3","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1001\/archneur.61.1.59","volume":"61","author":"M Grundman","year":"2004","unstructured":"Grundman, M., Petersen, R.C., Ferris, S.H., et al.: Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. Arch. Neurol., 61(1): 59\u201366, 2004.","journal-title":"Arch. Neurol."},{"key":"872_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-022-00758-w","author":"XT Li","year":"2023","unstructured":"Li, X.T., Allen, J.W., Hu R.: Implementation of automated pipeline for resting-state fMRI analysis with PACS integration.\u00a0J. Digit. Imaging, 2023. https:\/\/doi.org\/10.1007\/s10278-022-00758-w.","journal-title":"J. Digit. Imaging"},{"key":"872_CR5","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1002\/mrm.1910340409","volume":"34","author":"B Biswal","year":"1995","unstructured":"Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med., 34: 537\u2013541, 1995.","journal-title":"Magn. Reson. Med."},{"key":"872_CR6","doi-asserted-by":"crossref","unstructured":"Raimondo, L., Oliveira, \u013acaro A.F., Heij, J., et al.: Advances in resting state fMRI acquisitions for functional connectomics. NeuroImage, vol. 243, 2021.","DOI":"10.1016\/j.neuroimage.2021.118503"},{"issue":"5","key":"872_CR7","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.biopsych.2012.11.028","volume":"74","author":"YI Sheline","year":"2013","unstructured":"Sheline, Y.I., Raichle, M.E.: Resting state functional connectivity in preclinical Alzheimer\u2019s disease. Biol. Psychiatry, 74 (5): 340\u2013347, 2013.","journal-title":"Biol. Psychiatry"},{"issue":"2","key":"872_CR8","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.pscychresns.2012.03.002","volume":"202","author":"Z Liu","year":"2012","unstructured":"Liu, Z., Zhang, Y., Yan, H., Bai, L., Dai, R., et al.: Altered topological patterns of brain networks in mild cognitive impairment and Alzheimer\u2019s disease: a resting-state fMRI study. Psychiatry Res. Neuroimaging, 202(2): 118\u2013125, 2012.","journal-title":"Psychiatry Res. Neuroimaging"},{"issue":"9","key":"872_CR9","doi-asserted-by":"publisher","first-page":"2238","DOI":"10.1109\/TPAMI.2017.2750160","volume":"40","author":"A Mheich","year":"2018","unstructured":"Mheich, A., Hassan, M., Khalil, M., Gripon, V., Dufor, O., Wendling, F.: SimiNet: a novel method for quantifying brain network similarity.\u00a0IEEE Trans. Pattern Anal. Mach. Intell., 40(9): 2238\u20132249, 2018.\u00a0","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"872_CR10","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1523\/JNEUROSCI.3874-05.2006","volume":"26","author":"S Achard","year":"2006","unstructured":"Achard, S., Salvador, R., Whitcher, B., Suckling, J., Bullmore, E.: A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci., 26: 63\u201372, 2006.","journal-title":"J. Neurosci."},{"key":"872_CR11","doi-asserted-by":"crossref","unstructured":"Meunier, D., Lambiotte, R., Bullmore, E.T.: Modular and hierarchically modular organization of brain networks. Frontiers Neurosci., vol. 4, 2010, Art no. 200.","DOI":"10.3389\/fnins.2010.00200"},{"issue":"4","key":"872_CR12","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1016\/j.neurobiolaging.2013.10.081","volume":"35","author":"MR Brier","year":"2014","unstructured":"Brier, M.R., Thomas, J.B., Fagan, A.M., Hassenstab, J., Holtzman, D.M., et al.: Functional connectivity and graph theory in preclinical Alzheimer\u2019s disease. Neurobiol. Aging, 35(4): 757\u2013768, 2014.","journal-title":"Neurobiol. Aging"},{"issue":"1","key":"872_CR13","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.neuroimage.2005.12.057","volume":"32","author":"G Marrelec","year":"2006","unstructured":"Marrelec, G., Krainik, A., Duffau, H., et al.: Partial correlation for functional brain interactivity investigation in functional MRI. Neuroimage, 32(1): 228\u201337, 2006.","journal-title":"Neuroimage"},{"key":"872_CR14","unstructured":"Varoquaux, G., Gramfort, A., Poline, J.-B., Thirion, B.: Brain covariance selection: better individual functional connectivity models using population prior. In Advances in Neural Information Processing Systems, pp. 2334\u20132342, 2010."},{"issue":"3","key":"872_CR15","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1016\/j.neuroimage.2009.12.120","volume":"50","author":"S Huang","year":"2010","unstructured":"Huang, S., Li, J., Sun, L., Ye, J., Fleisher, A., Wu, T., Chen, K., Reiman, E.: Learning brain connectivity of Alzheimer\u2019s disease by sparse inverse covariance estimation. NeuroImage, 50(3): 935\u2013949, 2010.","journal-title":"NeuroImage"},{"issue":"5","key":"872_CR16","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1109\/TMI.2011.2140380","volume":"30","author":"H Lee","year":"2011","unstructured":"Lee, H., Lee, D.S., Kang, H., Kim, B.N., Chung, M.K.: Sparse brain network recovery under compressed sensing. IEEE Trans. Med. Imag., 30(5): 1154\u20131165, 2011.","journal-title":"IEEE Trans. Med. Imag."},{"key":"872_CR17","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1007\/s00429-013-0524-8","volume":"219","author":"C-Y Wee","year":"2014","unstructured":"Wee, C.-Y., Yap, P.-T., Zhang, D., Wang, L., Shen, D.: Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification. Brain Struct. Funct., 219: 641\u2013656, 2014.","journal-title":"Brain Struct. Funct."},{"key":"872_CR18","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/j.patcog.2018.12.001","volume":"88","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Zhang, H., Chen, X., Liu, M., Zhu, X., Lee, S.-W., Shen, D.: Strength and similarity guided group-level brain functional network construction for MCI diagnosis. Pattern Recognit., 88: 421\u2013430, 2019.","journal-title":"Pattern Recognit."},{"key":"872_CR19","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1016\/j.neuron.2012.12.028","volume":"77","author":"S Mueller","year":"2013","unstructured":"Mueller, S., Wang, D., Fox, M.D., Yeo, B.T.T., et al.: Individual variability in functional connectivity architecture of the human brain. Neuron, 77: 586\u2013595, 2013.","journal-title":"Neuron"},{"issue":"7","key":"872_CR20","first-page":"1912","volume":"67","author":"X Jiang","year":"2020","unstructured":"Jiang, X., Zhang, L., Qiao, L., Shen, D.: Estimating functional connectivity networks via low-rank tensor approximation with applications to MCI identification. IEEE Trans. Biomed. Eng., 67(7): 1912\u20131920, 2020.","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"872_CR21","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/j.neuroimage.2016.07.058","volume":"141","author":"L Qiao","year":"2016","unstructured":"Qiao, L., Han, Z., Kim, M., Teng, S., Zhang, L., Shen D.: Estimating functional brain networks by incorporating a modularity prior. NeuroImage, 141: 399\u2013407, 2016.","journal-title":"NeuroImage"},{"issue":"4","key":"872_CR22","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/JBHI.2019.2934230","volume":"24","author":"W Li","year":"2020","unstructured":"Li, W., Zhang, L., Qiao, L., Shen, D.: Toward a better estimation of functional brain network for mild cognitive impairment identification: a transfer learning view. IEEE J. Biomed. Health Inform., 24(4):1160\u20131168, 2020.","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"2","key":"872_CR23","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1109\/TBME.2021.3102015","volume":"69","author":"Y Xue","year":"2022","unstructured":"Xue, Y., Zhang, Y., Zhang, L., Lee, S.-W., Qiao, L., Shen, D.: Learning brain functional networks with latent temporal dependency for MCI identification. IEEE Trans. Biomed. Eng., 69(2): 590\u2013601, 2022.","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"872_CR24","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/JBHI.2022.3218652","volume":"27","author":"R Yu","year":"2023","unstructured":"Yu, R., Zhang, H., Wu, X., Fei, X., Yang, Q., et al.: Outcome prediction of unconscious patients based on weighted sparse brain network construction. IEEE J. Biomed. Health Inform., vol.27, no.1, pp. 469\u2013479, 2023.","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"6","key":"872_CR25","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.1109\/JBHI.2019.2893880","volume":"23","author":"W Li","year":"2019","unstructured":"Li, W., Qiao, L., Zhang, L., Wang, Z., Shen, D.: Functional brain network estimation with time series self-scrubbing. IEEE J. Biomed. Health Inform., 23(6): 2494\u20132504, 2019.","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"872_CR26","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.neuroimage.2016.01.005","volume":"129","author":"H-I Suk","year":"2016","unstructured":"Suk, H.-I., Wee, C.-Y., Lee, S.-W., Shen, D.: State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage, 129: 292\u2013307, 2016.","journal-title":"NeuroImage"},{"issue":"9","key":"872_CR27","doi-asserted-by":"publisher","first-page":"1953","DOI":"10.1109\/TBME.2018.2842769","volume":"65","author":"Y Wang","year":"2018","unstructured":"Wang, Y., Lin, K., Qi, Y., Lian, Q., Feng, S., Wu, Z., Pan, G.: Estimating brain connectivity with varying length time lags using recurrent neural network. IEEE Trans. Biomed. Eng., 65(9): 1953\u20131963, 2018.","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"872_CR28","doi-asserted-by":"crossref","unstructured":"Yan, W., Zhang, H., Sui, J., Shen D.: Deep chronnectome learning via full bidirectional long short-term memory networks for MCI diagnosis. In Proc. Medical Image Computing and Computer-assisted Intervention, pp. 249\u2013257, 2018.","DOI":"10.1007\/978-3-030-00931-1_29"},{"issue":"2","key":"872_CR29","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1109\/TMI.2019.2928790","volume":"39","author":"T-E Kam","year":"2020","unstructured":"Kam, T.-E., Zhang, H., Jiao, Z., Shen D.: Deep learning of static and dynamic brain functional networks for early MCI detection. IEEE Trans. Med. Imaging, 39(2): 478\u2013487, 2020.","journal-title":"IEEE Trans. Med. Imaging"},{"key":"872_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118048","volume":"236","author":"J Lee","year":"2021","unstructured":"Lee, J., Ko, W., Kang, E., S uk, H-Il: A unified framework for personalized regions selection and functional relation modeling for early MCI identification. NeuroImage, vol. 236, 118048, 2021.","journal-title":"NeuroImage"},{"key":"872_CR31","doi-asserted-by":"crossref","unstructured":"Rekik, I., Li, G., Lin, W., Shen, D.: Estimation of brain network atlases using diffusive-shrinking graphs: application to developing brains. In Proc. Int. Conf. Inf. Process. Med. Imag., pp. 385\u2013397, 2017.","DOI":"10.1007\/978-3-319-59050-9_31"},{"key":"872_CR32","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv:1312.6114v10 [stat.ML], 1 May 2014."},{"issue":"13","key":"872_CR33","first-page":"13","volume":"4","author":"C-G Yan","year":"2010","unstructured":"Yan, C-G., Zang, Y-F.: DPARSF: a MATLAB toolbox for \u201cpipeline\u201d data analysis of resting-state fMRI. Frontiers Syst. Neurosci., 4(13): 13, 2010.","journal-title":"Frontiers Syst. Neurosci."},{"issue":"10","key":"872_CR34","doi-asserted-by":"publisher","first-page":"5019","DOI":"10.1002\/hbm.23711","volume":"38","author":"X Chen","year":"2017","unstructured":"Chen, X., Zhang, H., Zhang, L., Shen, C., Lee, S.-W., Shen, D.: Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification. Hum. Brain Mapping, 38(10): 5019\u20135034, 2017.","journal-title":"Hum. Brain Mapping"},{"issue":"1","key":"872_CR35","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1): 273\u2013289, 2002.","journal-title":"NeuroImage"},{"issue":"1","key":"872_CR36","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Computer and System Sciences, 55(1): 119-139, 1997.","journal-title":"J. Computer and System Sciences"},{"issue":"1","key":"872_CR37","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Machine Learning Research, 15(1): 1929\u20131958, 2014.","journal-title":"J. Machine Learning Research"},{"key":"872_CR38","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014."},{"key":"872_CR39","doi-asserted-by":"crossref","unstructured":"Xia, M., Wang, J., He, Y.: BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One, 8, (7): e68910, 2013.","DOI":"10.1371\/journal.pone.0068910"},{"issue":"3","key":"872_CR40","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","volume":"7","author":"MS Albert","year":"2011","unstructured":"Albert, M.S., DeKosky, S.T., Dickson, D., Dubois, B., Feldman, H.H., Fox N.C.: The diagnosis of mild cognitive impairment due to Alzheimer\u2019s disease: recommendations from the National Institute on Aging-Alzheimer\u2019s Association Workgroups on Diagnostic Guidelines for Alzheimer\u2019s disease. Alzheimers Dementia, 7(3): 270\u2013279, 2011.","journal-title":"Alzheimers Dementia"},{"issue":"4","key":"872_CR41","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1097\/WCO.0b013e328306f2c5","volume":"21","author":"M Greicius","year":"2008","unstructured":"Greicius M.: Resting-state functional connectivity in neuropsychiatric disorders. Current Opin. Neurol., 21(4): 424\u2013430, 2008.","journal-title":"Current Opin. Neurol."},{"key":"872_CR42","doi-asserted-by":"crossref","unstructured":"Sun, L., Xue, Y., Zhang, Y., Qiao, L., Zhang, L., Liu, M.: Estimating sparse functional connectivity networks via hyperparameter-free learning model. Artificial Intelligence in Medicine,\u00a0111(8): 102004(1\u201312), 2021.","DOI":"10.1016\/j.artmed.2020.102004"},{"key":"872_CR43","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.tics.2009.05.001","volume":"13","author":"T Singer","year":"2009","unstructured":"Singer, T., Critchley, H.D., Preuschoff, K.: A common role of insula in feelings, empathy and uncertainty. Trends Cogn. Sci., 13: 334\u2013340, 2009.","journal-title":"Trends Cogn. Sci."},{"issue":"5\u20136","key":"872_CR44","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1007\/s00429-010-0260-2","volume":"214","author":"SM Nelson","year":"2010","unstructured":"Nelson, S.M., Dosenbach, N.U., Cohen, A.L., Wheeler, M.E., Schlaggar, B.L., Petersen S.E.: Role of the anterior insula in task-level control and focal attention. Brain Struct. Funct., 214(5-6): 669\u2013680, 2010.","journal-title":"Brain Struct. Funct."},{"issue":"11","key":"872_CR45","doi-asserted-by":"publisher","first-page":"2944","DOI":"10.1002\/hbm.22113","volume":"34","author":"WK Simmons","year":"2013","unstructured":"Simmons, W.K., Avery, J.A., Barcalow, J.C., Bodurka, J., Drevets, W.C., Bellgowan, P.: Keeping the body in mind: insula functional organization and functional connectivity integrate interoceptive, exteroceptive, and emotional awareness. Human Brain Mapping, 34(11): 2944\u20132958, 2013.","journal-title":"Human Brain Mapping"},{"key":"872_CR46","doi-asserted-by":"crossref","unstructured":"Qiu, S., Joshi, P.S., Miller, M.I., Xue, C., Zhou, X., Karjadi, C., et al.: Development and validation of an interpretable deep learning framework for Alzheimer's disease classification. Brain, 2020.","DOI":"10.1093\/brain\/awaa137"},{"key":"872_CR47","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1093\/cercor\/bhj127","volume":"17","author":"CJ Stam","year":"2007","unstructured":"Stam, C.J., Jones, B.F., Nolte, G., Breakspear, M., Scheltens, P.: Small-world networks and functional connectivity in Alzheimer's disease. Cereb. Cortex, 17: 92\u201399, 2007.","journal-title":"Cereb. Cortex"},{"issue":"9","key":"872_CR48","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.tics.2004.07.008","volume":"8","author":"O Sporns","year":"2004","unstructured":"Sporns, O., Chialvo, D., Kaiser, M., Hilgetag, C.: Organization, development and function of complex brain networks. Trends in Cognitive Sciences 8(9): 418\u2013425, 2004.","journal-title":"Trends in Cognitive Sciences"},{"key":"872_CR49","doi-asserted-by":"publisher","first-page":"2889","DOI":"10.1523\/JNEUROSCI.3554-12.2013","volume":"33","author":"A Alexander-Bloch","year":"2013","unstructured":"Alexander-Bloch, A., Raznahan, A., Bullmore, E., Giedd, J.: The convergence of maturational change and structural covariance in human cortical networks. J. Neurosci., 33:2889\u20132899, 2013.","journal-title":"J. Neurosci."},{"key":"872_CR50","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1038\/427311a","volume":"427","author":"B Draganski","year":"2004","unstructured":"Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., May, A.: Neuroplasticity: changes in grey matter induced by training. Nature, 427:311\u2013312, 2004.","journal-title":"Nature"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00872-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-023-00872-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00872-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T06:06:19Z","timestamp":1694757979000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-023-00872-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,27]]},"references-count":50,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["872"],"URL":"https:\/\/doi.org\/10.1007\/s10278-023-00872-3","relation":{},"ISSN":["1618-727X"],"issn-type":[{"type":"electronic","value":"1618-727X"}],"subject":[],"published":{"date-parts":[[2023,6,27]]},"assertion":[{"value":"13 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}