{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T02:02:36Z","timestamp":1794276156328,"version":"4.9.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T00:00:00Z","timestamp":1697414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T00:00:00Z","timestamp":1697414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Science Foundation","award":["DGE1841052"],"award-info":[{"award-number":["DGE1841052"]}]},{"name":"National Science Foundation","award":["1531752"],"award-info":[{"award-number":["1531752"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This method uses several binarized images from different angles to reconstruct a 3D coronary tree without any knowledge of image acquisition parameters. The method consists of a single backbone network and separate stages for vessel centerline and radius reconstruction. The output is an analytical matrix representation of the coronary tree suitable for downstream applications such as hemodynamic modeling of local vessel narrowing (i.e., stenosis). The network was trained using a dataset of synthetic coronary trees from a vessel generator informed by both clinical image data and literature values on coronary anatomy. Our multi-stage network achieved sub-pixel accuracy in reconstructing vessel radius (RMSE\u2009=\u20090.16\u2009\u00b1\u20090.07 mm) and stenosis radius (MAE\u2009=\u20090.27\u2009\u00b1\u20090.18 mm), the most important feature used to inform diagnostic decisions. The network also led to 52% and 38% reduction in vessel centerline reconstruction errors compared to a single-stage network and projective geometry-based methods, respectively. Our method demonstrated robustness to overcome challenges such as vessel foreshortening or overlap in the input images. This work is an important step towards automated analysis of anatomic and functional disease severity in the coronary arteries.<\/jats:p>","DOI":"10.1038\/s41598-023-44633-2","type":"journal-article","created":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T18:04:15Z","timestamp":1697479455000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A multi-stage neural network approach for coronary 3D reconstruction from uncalibrated X-ray angiography images"],"prefix":"10.1038","volume":"13","author":[{"given":"Kritika","family":"Iyer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brahmajee K.","family":"Nallamothu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. Alberto","family":"Figueroa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raj R.","family":"Nadakuditi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,16]]},"reference":[{"key":"44633_CR1","unstructured":"Nichols, W. W., O\u2019Rourke, M. F., Vlachopoulos, C., & McDonald, D. A. McDonald\u2019s Blood Flow in Arteries\u202f: Theoretical, Experimental and Clinical Principles. London: Hodder Arnold, 2011. Accessed: Apr. 16, 2019. [Online]. Available: https:\/\/www.crcpress.com\/McDonalds-Blood-Flow-in-Arteries-Theoretical-Experimental-and-Clinical\/Vlachopoulos-ORourke-Nichols\/p\/book\/9780340985014"},{"issue":"4","key":"44633_CR2","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1161\/01.CIR.87.4.1354","volume":"87","author":"NH Pijls","year":"1993","unstructured":"Pijls, N. H., van Son, J. A., Kirkeeide, R. L., De Bruyne, B. & Gould, K. L. Experimental basis of determining maximum coronary, myocardial, and collateral blood flow by pressure measurements for assessing functional stenosis severity before and after percutaneous transluminal coronary angioplasty. Circulation 87(4), 1354\u20131367. https:\/\/doi.org\/10.1161\/01.CIR.87.4.1354 (1993).","journal-title":"Circulation"},{"issue":"5","key":"44633_CR3","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1161\/CIRCULATIONAHA.117.031907","volume":"137","author":"WF Fearon","year":"2018","unstructured":"Fearon, W. F. et al. Clinical outcomes and cost-effectiveness of fractional flow reserve-guided percutaneous coronary intervention in patients with stable coronary artery disease: Three-year follow-up of the FAME 2 trial (fractional flow reserve versus angiography for multivessel evaluation). Circulation 137(5), 480\u2013487. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.117.031907 (2018).","journal-title":"Circulation"},{"issue":"19","key":"44633_CR4","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1056\/NEJMoa1616540","volume":"376","author":"M G\u00f6tberg","year":"2017","unstructured":"G\u00f6tberg, M. et al. Instantaneous wave-free ratio versus fractional flow reserve to guide PCI. N. Engl. J. Med. 376(19), 1813\u20131823. https:\/\/doi.org\/10.1056\/NEJMoa1616540 (2017).","journal-title":"N. Engl. J. Med."},{"key":"44633_CR5","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.118.037350","author":"WF Fearon","year":"2019","unstructured":"Fearon, W. F. et al. Accuracy of fractional flow reserve derived from coronary angiography. Circulation https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.118.037350 (2019).","journal-title":"Circulation"},{"issue":"25","key":"44633_CR6","doi-asserted-by":"publisher","first-page":"3077","DOI":"10.1016\/j.jacc.2017.10.035","volume":"70","author":"B Xu","year":"2017","unstructured":"Xu, B. et al. Diagnostic accuracy of angiography-based quantitative flow ratio measurements for online assessment of coronary stenosis. J. Am. Coll. Cardiol. 70(25), 3077\u20133087. https:\/\/doi.org\/10.1016\/j.jacc.2017.10.035 (2017).","journal-title":"J. Am. Coll. Cardiol."},{"issue":"11","key":"44633_CR7","doi-asserted-by":"publisher","DOI":"10.1002\/cnm.3255","volume":"35","author":"JM Carson","year":"2019","unstructured":"Carson, J. M., Roobottom, C., Alcock, R. & Nithiarasu, P. Computational instantaneous wave-free ratio (IFR) for patient-specific coronary artery stenoses using 1D network models. Int. J. Num. Methods Biomed. Eng. 35(11), e3255. https:\/\/doi.org\/10.1002\/cnm.3255 (2019).","journal-title":"Int. J. Num. Methods Biomed. Eng."},{"issue":"22","key":"44633_CR8","doi-asserted-by":"publisher","first-page":"2233","DOI":"10.1016\/j.jacc.2012.11.083","volume":"61","author":"CA Taylor","year":"2013","unstructured":"Taylor, C. A., Fonte, T. A. & Min, J. K. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: Scientific basis. J. Am. Coll. Cardiol. 61(22), 2233\u20132241. https:\/\/doi.org\/10.1016\/j.jacc.2012.11.083 (2013).","journal-title":"J. Am. Coll. Cardiol."},{"issue":"11","key":"44633_CR9","doi-asserted-by":"publisher","first-page":"4543","DOI":"10.21037\/qims-20-1324","volume":"11","author":"J Jiang","year":"2021","unstructured":"Jiang, J. et al. Fractional flow reserve for coronary stenosis assessment derived from fusion of intravascular ultrasound and X-ray angiography. Quant Imaging Med. Surg. 11(11), 4543\u20134555. https:\/\/doi.org\/10.21037\/qims-20-1324 (2021).","journal-title":"Quant Imaging Med. Surg."},{"issue":"7","key":"44633_CR10","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1093\/cvr\/cvz289","volume":"116","author":"J Li","year":"2020","unstructured":"Li, J. et al. Accuracy of computational pressure-fluid dynamics applied to coronary angiography to derive fractional flow reserve: FLASH FFR. Cardiovasc. Res. 116(7), 1349\u20131356. https:\/\/doi.org\/10.1093\/cvr\/cvz289 (2020).","journal-title":"Cardiovasc. Res."},{"issue":"4","key":"44633_CR11","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.1109\/TMI.2019.2944092","volume":"39","author":"A Banerjee","year":"2020","unstructured":"Banerjee, A. et al. Point-cloud method for automated 3D coronary tree reconstruction from multiple non-simultaneous angiographic projections. IEEE Trans. Med. Imaging 39(4), 1278\u20131290. https:\/\/doi.org\/10.1109\/TMI.2019.2944092 (2020).","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"44633_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-19440-9","volume":"8","author":"AM Vukicevic","year":"2018","unstructured":"Vukicevic, A. M. et al. Three-dimensional reconstruction and NURBS-based structured meshing of coronary arteries from the conventional X-ray angiography projection images. Sci. Rep. 8(1), 1. https:\/\/doi.org\/10.1038\/s41598-018-19440-9 (2018).","journal-title":"Sci. Rep."},{"issue":"8","key":"44633_CR13","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1016\/j.compmedimag.2005.07.001","volume":"29","author":"CV Bourantas","year":"2005","unstructured":"Bourantas, C. V. et al. A method for 3D reconstruction of coronary arteries using biplane angiography and intravascular ultrasound images. Comput. Med. Imaging Graph. 29(8), 597\u2013606. https:\/\/doi.org\/10.1016\/j.compmedimag.2005.07.001 (2005).","journal-title":"Comput. Med. Imaging Graph."},{"issue":"4","key":"44633_CR14","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/42.848183","volume":"19","author":"SJ Chen","year":"2000","unstructured":"Chen, S. J. & Carroll, J. D. 3-D Reconstruction of coronary arterial tree to optimize angiographic visualization. IEEE Trans. Med. Imaging 19(4), 318\u2013336. https:\/\/doi.org\/10.1109\/42.848183 (2000).","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"44633_CR15","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1109\/TMI.2006.873224","volume":"25","author":"C Blondel","year":"2006","unstructured":"Blondel, C., Malandain, G., Vaillant, R. & Ayache, N. Reconstruction of coronary arteries from a single rotational X-ray projection sequence. IEEE Trans. Med. Imaging 25(5), 653\u2013663. https:\/\/doi.org\/10.1109\/TMI.2006.873224 (2006).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"44633_CR16","doi-asserted-by":"publisher","unstructured":"Cardenes, R., Novikov, A., Gunn, J., Hose, R., & Frangi, A. F. 3D reconstruction of coronary arteries from rotational X-ray angiography. In 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), May 2012, 618\u2013621. https:\/\/doi.org\/10.1109\/ISBI.2012.6235624 (2012).","DOI":"10.1109\/ISBI.2012.6235624"},{"key":"44633_CR17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0190650","author":"F Galassi","year":"2018","unstructured":"Galassi, F. et al. 3D reconstruction of coronary arteries from 2D angiographic projections using nonuniform rational basis splines (NURBS) for accurate modelling of coronary stenoses. PLoS ONE https:\/\/doi.org\/10.1371\/journal.pone.0190650 (2018).","journal-title":"PLoS ONE"},{"issue":"5","key":"44633_CR18","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1023\/A:1026528209003","volume":"16","author":"KR Hoffmann","year":"2000","unstructured":"Hoffmann, K. R. et al. A system for determination of 3D vessel tree centerlines from biplane images. Int. J. Cardiovasc. Imaging 16(5), 315\u2013330. https:\/\/doi.org\/10.1023\/A:1026528209003 (2000).","journal-title":"Int. J. Cardiovasc. Imaging"},{"issue":"6","key":"44633_CR19","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1016\/j.media.2009.07.010","volume":"13","author":"U Jandt","year":"2009","unstructured":"Jandt, U., Sch\u00e4fer, D., Grass, M. & Rasche, V. Automatic generation of 3D coronary artery centerlines using rotational X-ray angiography. Med. Image Anal. 13(6), 846\u2013858. https:\/\/doi.org\/10.1016\/j.media.2009.07.010 (2009).","journal-title":"Med. Image Anal."},{"issue":"7","key":"44633_CR20","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1007\/s10554-009-9528-0","volume":"26","author":"R Liao","year":"2010","unstructured":"Liao, R., Luc, D., Sun, Y. & Kirchberg, K. 3-D reconstruction of the coronary artery tree from multiple views of a rotational X-ray angiography. Int. J. Cardiovasc. Imaging 26(7), 733\u2013749. https:\/\/doi.org\/10.1007\/s10554-009-9528-0 (2010).","journal-title":"Int. J. Cardiovasc. Imaging"},{"issue":"12","key":"44633_CR21","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1109\/TMI.2004.837340","volume":"23","author":"B Movassaghi","year":"2004","unstructured":"Movassaghi, B., Rasche, V., Grass, M., Viergever, M. A. & Niessen, W. J. A quantitative analysis of 3-D coronary modeling from two or more projection images. IEEE Trans. Med. Imaging 23(12), 1517\u20131531. https:\/\/doi.org\/10.1109\/TMI.2004.837340 (2004).","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"44633_CR22","doi-asserted-by":"publisher","first-page":"2079","DOI":"10.1109\/TBME.2015.2408633","volume":"62","author":"W Cong","year":"2015","unstructured":"Cong, W. et al. Quantitative analysis of deformable model-based 3-D reconstruction of coronary artery from multiple angiograms. IEEE Trans. Biomed. Eng. 62(8), 2079\u20132090. https:\/\/doi.org\/10.1109\/TBME.2015.2408633 (2015).","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"5","key":"44633_CR23","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.compmedimag.2009.12.004","volume":"34","author":"S Zheng","year":"2010","unstructured":"Zheng, S., Meiying, T. & Jian, S. Sequential reconstruction of vessel skeletons from X-ray coronary angiographic sequences. Comput. Med. Imaging Graph. 34(5), 333\u2013345. https:\/\/doi.org\/10.1016\/j.compmedimag.2009.12.004 (2010).","journal-title":"Comput. Med. Imaging Graph."},{"issue":"9","key":"44633_CR24","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1109\/TMI.2002.804421","volume":"21","author":"C Canero","year":"2002","unstructured":"Canero, C., Vilarino, F., Mauri, J. & Radeva, P. Predictive (un)distortion model and 3-D reconstruction by biplane snakes. IEEE Trans. Med. Imaging 21(9), 1188\u20131201. https:\/\/doi.org\/10.1109\/TMI.2002.804421 (2002).","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"44633_CR25","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1088\/0031-9155\/59\/4\/975","volume":"59","author":"J Yang","year":"2014","unstructured":"Yang, J. et al. External force back-projective composition and globally deformable optimization for 3-D coronary artery reconstruction. Phys. Med. Biol. 59(4), 975\u20131003. https:\/\/doi.org\/10.1088\/0031-9155\/59\/4\/975 (2014).","journal-title":"Phys. Med. Biol."},{"key":"44633_CR26","unstructured":"Siemens, A. G. Artis Q\/Q.zen\/zeego system owners manual (2015)."},{"key":"44633_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.101956","volume":"92","author":"DM Bappy","year":"2021","unstructured":"Bappy, D. M., Hong, A., Choi, E., Park, J.-O. & Kim, C.-S. Automated three-dimensional vessel reconstruction based on deep segmentation and bi-plane angiographic projections. Comput. Med. Imaging Graph. 92, 101956. https:\/\/doi.org\/10.1016\/j.compmedimag.2021.101956 (2021).","journal-title":"Comput. Med. Imaging Graph."},{"key":"44633_CR28","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.media.2016.02.007","volume":"32","author":"S \u00c7imen","year":"2016","unstructured":"\u00c7imen, S., Gooya, A., Grass, M. & Frangi, A. F. Reconstruction of coronary arteries from X-ray angiography: A review. Med. Image Anal. 32, 46\u201368. https:\/\/doi.org\/10.1016\/j.media.2016.02.007 (2016).","journal-title":"Med. Image Anal."},{"issue":"1","key":"44633_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-97355-8","volume":"11","author":"K Iyer","year":"2021","unstructured":"Iyer, K. et al. AngioNet: A convolutional neural network for vessel segmentation in X-ray angiography. Sci. Rep. 11(1), 1. https:\/\/doi.org\/10.1038\/s41598-021-97355-8 (2021).","journal-title":"Sci. Rep."},{"key":"44633_CR30","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. Accessed: Apr. 27, 2018. [Online]. Available: http:\/\/arxiv.org\/abs\/1512.03385 (2015)."},{"issue":"4","key":"44633_CR31","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1007\/s00330-007-0821-9","volume":"18","author":"F Cademartiri","year":"2008","unstructured":"Cademartiri, F. et al. Prevalence of anatomical variants and coronary anomalies in 543 consecutive patients studied with 64-slice CT coronary angiography. Eur. Radiol. 18(4), 781\u2013791. https:\/\/doi.org\/10.1007\/s00330-007-0821-9 (2008).","journal-title":"Eur. Radiol."},{"issue":"5","key":"44633_CR32","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1008881","volume":"17","author":"CJ Arthurs","year":"2021","unstructured":"Arthurs, C. J. et al. CRIMSON: An open-source software framework for cardiovascular integrated modelling and simulation. PLOS Comput. Biol. 17(5), e1008881. https:\/\/doi.org\/10.1371\/journal.pcbi.1008881 (2021).","journal-title":"PLOS Comput. Biol."},{"key":"44633_CR33","doi-asserted-by":"publisher","unstructured":"Tan, M., & Le, Q. V. EfficientNetV2: Smaller models and faster training. https:\/\/doi.org\/10.48550\/arXiv.2104.00298 (2021).","DOI":"10.48550\/arXiv.2104.00298"},{"key":"44633_CR34","doi-asserted-by":"publisher","unstructured":"Dosovitskiy, A., et al. An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. https:\/\/doi.org\/10.48550\/arXiv.2010.11929 (2021).","DOI":"10.48550\/arXiv.2010.11929"},{"issue":"26","key":"44633_CR35","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.1056\/NEJM199606273342604","volume":"334","author":"NH Pijls","year":"1996","unstructured":"Pijls, N. H. et al. Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses. N. Engl. J. Med. 334(26), 1703\u20131708. https:\/\/doi.org\/10.1056\/NEJM199606273342604 (1996).","journal-title":"N. Engl. J. Med."},{"key":"44633_CR36","doi-asserted-by":"publisher","unstructured":"Ferdian, E., et al. Cerebrovascular super-resolution 4D Flow MRI\u2014using deep learning to non-invasively quantify velocity, flow, and relative pressure. bioRxiv, p. 2021.08.25.457611. https:\/\/doi.org\/10.1101\/2021.08.25.457611 (2021).","DOI":"10.1101\/2021.08.25.457611"},{"key":"44633_CR37","doi-asserted-by":"publisher","unstructured":"Chen, Y., Shi, F., Christodoulou, A. G., Xie, Y., Zhou, Z., & Li, D. Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network. In Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G., eds., Medical image computing and computer assisted intervention\u2014MICCAI 2018, Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 91\u201399. https:\/\/doi.org\/10.1007\/978-3-030-00928-1_11 (2018).","DOI":"10.1007\/978-3-030-00928-1_11"},{"issue":"5","key":"44633_CR38","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/S0730-725X(02)00511-8","volume":"20","author":"H Greenspan","year":"2002","unstructured":"Greenspan, H., Oz, G., Kiryati, N. & Peled, S. MRI inter-slice reconstruction using super-resolution. Magn. Reson. Imaging 20(5), 437\u2013446. https:\/\/doi.org\/10.1016\/S0730-725X(02)00511-8 (2002).","journal-title":"Magn. Reson. Imaging"},{"key":"44633_CR39","doi-asserted-by":"publisher","unstructured":"Yang, J., Li, C., Dai, X., Yuan, L., & Gao, J. Focal modulation networks. arXiv, Nov. 05. https:\/\/doi.org\/10.48550\/arXiv.2203.11926 (2022).","DOI":"10.48550\/arXiv.2203.11926"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-44633-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-44633-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-44633-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T17:11:33Z","timestamp":1700500293000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-44633-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,16]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["44633"],"URL":"https:\/\/doi.org\/10.1038\/s41598-023-44633-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2782923\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,16]]},"assertion":[{"value":"5 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"CAF and BKN are founders of AngioInsight, Inc, a startup company which uses machine learning and signal processing to assist physicians with angiography interpretation. AngioInsight did not sponsor this work. This work is a part of a patent which has been filed by the University of Michigan, US patent 11,386,563 <i>Anatomical and Functional Assessment of Coronary Artery Disease Using Machine Learning<\/i>. All other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17603"}}