{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T19:29:45Z","timestamp":1785871785041,"version":"3.56.0"},"reference-count":32,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T00:00:00Z","timestamp":1722816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Introduction: Statistical shape modelling (SSM) is used to analyse morphology, discover qualitatively and quantitatively unique shape features within a population, and generate mean shapes and shape modes that show morphological variability. Hierarchical agglomerative clustering is a machine learning analysis used to identify subgroups within a given population in relation to shape features. We tested the application of both methods in the clinically relevant scenario of patients undergoing aortic valve repair (AVR). Every year, around 5000 patients undergo surgical AVR in the UK. Aims: Evaluate aortic morphology and identify subgroups amongst patients who had undergone AVR, including Ozaki, Ross, and valve-sparing procedures using SSM and unsupervised hierarchical clustering analysis. This methodological framework can evaluate both pre- and post-surgical variability across subgroups undergoing different surgeries. Methods: Pre- (n = 47) and post- (n = 35) operative three-dimensional (3D) aortic models were reconstructed from computed tomography (CT) and cardiac magnetic resonance (CMR) images. Computational analyses for SSM and hierarchical clustering were run separately for the two subgroups, assessing (a) ascending aorta only and (b) the whole aorta. This allows for exploring possible variations in morphological classification related to the input shape. Results: Most patients in the Ross procedure subgroup exhibited differences in aortic morphology from other subgroups, including an elongated ascending and wide aortic arch pre-operatively, and an elongated ascending aorta with a slightly enlarged sinus post-operatively. In hierarchical clustering, the Ross aortas also appeared to cluster together compared to the other surgical procedures, both pre-operatively and post-operatively. There were significant differences between clusters in terms of clustering distance in the pre-operative analyses (p = 0.003 for ascending aortas, p = 0.016 for whole aortas). There were no significant differences between the clusters in post-operative analyses (p = 0.47 for ascending, p = 0.19 for whole aorta). Conclusions: We demonstrated the feasibility of evaluating aortic morphology before and after different aortic valve surgeries using SSM and hierarchical clustering. This framework could be used to further explore shape features associated with surgical decision-making pre-operatively and, importantly, to identify subgroups whose morphology is associated with poorer clinical outcomes post-operatively. Statistical shape modelling (SSM) and unsupervised hierarchical clustering are two statistical methods that can be used to assess morphology, show morphological variations, with the latter being able to identify subgroups within a population. These methods have been applied to the population of aortic valve replacement (AVR) patients since there are different surgical procedures (traditional AVR, Ozaki, Ross, and valve-sparing). The aim is to evaluate aortic morphology and identify subgroups within this population before and after surgery. Computed tomography and cardiac magnetic resonance images were reconstructed into 3D models of the ascending aorta and whole aorta, which were then input into SSM and hierarchical clustering. The results show that the Ross aortic morphology is quite different from the other aortas. The clustering did not classify the aortas based on the surgical procedures; however, most of the Ross group did cluster together, indicating low variability within this surgical group.<\/jats:p>","DOI":"10.3390\/jcm13154577","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T15:45:22Z","timestamp":1722872722000},"page":"4577","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning and Statistical Shape Modelling Methodologies to Assess Vascular Morphology before and after Aortic Valve Replacement"],"prefix":"10.3390","volume":"13","author":[{"given":"Yousef","family":"Aljassam","sequence":"first","affiliation":[{"name":"Department of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol BS2 8HW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Froso","family":"Sophocleous","sequence":"additional","affiliation":[{"name":"Department of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol BS2 8HW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5774-1593","authenticated-orcid":false,"given":"Jan L.","family":"Bruse","sequence":"additional","affiliation":[{"name":"Fundaci\u00f3n Vicomtech, Basque Research and Technology Alliance BRTA, Mikeletegi 57, 20009 Donostia-San Sebasti\u00e1n, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vico","family":"Schot","sequence":"additional","affiliation":[{"name":"Department of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol BS2 8HW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimo","family":"Caputo","sequence":"additional","affiliation":[{"name":"Department of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol BS2 8HW, UK"},{"name":"Bristol Heart Institute, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol BS2 8HW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0413-149X","authenticated-orcid":false,"given":"Giovanni","family":"Biglino","sequence":"additional","affiliation":[{"name":"Department of Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol BS2 8HW, UK"},{"name":"Bristol Heart Institute, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol BS2 8HW, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/978-3-030-19385-0_5","article-title":"Statistical Shape Models: Understanding and Mastering Variation in Anatomy","volume":"1156","author":"Ambellan","year":"2019","journal-title":"Adv. Exp. Med. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Khan, N., Peterson, A.C., Aubert, B., Morris, A., Atkins, P.R., Lenz, A.L., Anderson, A.E., and Elhabian, S.Y. (2023). Statistical Multi-Level Shape Models for Scalable Modeling of Multi-Organ Anatomies. Front Bioeng Biotechnol., 11.","DOI":"10.3389\/fbioe.2023.1089113"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1038\/nmeth.4346","article-title":"Principal Component Analysis","volume":"14","author":"Lever","year":"2017","journal-title":"Nat. Methods"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.bone.2013.12.006","article-title":"Statistical Shape and Appearance Models of Bones","volume":"60","author":"Sarkalkan","year":"2014","journal-title":"Bone"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e2037519","DOI":"10.1001\/jamanetworkopen.2020.37519","article-title":"Feasibility of Imaging-Based 3-Dimensional Models to Design Patient-Specific Osteosynthesis Plates and Drilling Guides","volume":"4","author":"IJpma","year":"2021","journal-title":"JAMA Netw. Open"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.1111\/jch.13984","article-title":"Unsupervised hierarchical clustering identifies a metabolically challenged subgroup of hypertensive individuals","volume":"22","author":"Vaura","year":"2020","journal-title":"J. Clin. Hypertens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.neunet.2012.06.007","article-title":"Hierarchical kernel spectral clustering","volume":"35","author":"Alzate","year":"2012","journal-title":"Neural Netw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13790","DOI":"10.1073\/pnas.191502998","article-title":"Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses","volume":"98","author":"Bhattacharjee","year":"2001","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rodriguez, M.Z., Comin, C.H., Casanova, D., Bruno, O.M., Amancio, D.R., Costa, L.D.F., and Rodrigues, F.A. (2019). Clustering algorithms: A comparative approach. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0210236"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sebastiani, P., and Perls, T. (2016). Detection of Significant Groups in Hierarchical Clustering by Resampling. Front. Genet., 7.","DOI":"10.3389\/fgene.2016.00144"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"126433","DOI":"10.1016\/j.physa.2021.126433","article-title":"Revisiting agglomerative clustering","volume":"585","author":"Tokuda","year":"2022","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.1109\/TBME.2017.2655364","article-title":"Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering Applied to Healthy and Pathological Aortic Arches","volume":"64","author":"Bruse","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2502","DOI":"10.1111\/jocs.15555","article-title":"Surgical aortic valve replacement in small aortic annulus","volume":"36","author":"Vaidya","year":"2021","journal-title":"J. Card. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"433","DOI":"10.21037\/acs-2021-rp-24","article-title":"The Ross Procedure and Valve-sparing Root Replacement Procedures in the Adult Patient: Do Guidelines Follow the Evidence?","volume":"10","author":"Ervoort","year":"2021","journal-title":"Ann. Cardiothorac. Surg."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e027391","DOI":"10.1161\/JAHA.122.027391","article-title":"Aortic Valve Reconstruction with Autologous Pericardium Versus a Bioprosthesis: The Ozaki Procedure in Perspective","volume":"12","author":"Unai","year":"2023","journal-title":"J. Am. Heart. Assoc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"s28","DOI":"10.1136\/heartjnl-2018-313515","article-title":"Patient-prosthesis mismatch following aortic valve replacement","volume":"105","author":"Bilkhu","year":"2019","journal-title":"Heart"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.jcmg.2018.10.020","article-title":"Imaging for Predicting and Assessing Prosthesis-Patient Mismatch After Aortic Valve Replacement","volume":"12","author":"Pibarot","year":"2019","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.athoracsur.2018.01.090","article-title":"The Incidence and Consequence of Prosthesis-Patient Mismatch After Surgical Aortic Valve Replacement","volume":"106","author":"Fallon","year":"2018","journal-title":"Ann. Thorac. Surg."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1016\/j.jacc.2012.07.005","article-title":"Valve prosthesis-patient mismatch, 1978 to 2011: From original concept to compelling evidence","volume":"60","author":"Pibarot","year":"2012","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1136\/heartjnl-2022-321740","article-title":"Formal Consensus Study on Surgery to Replace the Aortic Valve in Adults Aged 18\u201360 Years","volume":"109","author":"Stoica","year":"2023","journal-title":"Heart."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bruse, J.L., McLeod, K., Biglino, G., Ntsinjana, H.N., Capelli, C., Hsia, T.Y., Sermesant, M., Pennec, X., and Taylor, A.M. (2016). A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: Assessing arch morphology of repaired coarctation of the aorta. BMC Med. Imaging, 16.","DOI":"10.1186\/s12880-016-0142-z"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1109\/TMI.2011.2135375","article-title":"A Statistical Model for Quantification and Prediction of Cardiac Remodelling: Application to Tetralogy of Fallot","volume":"30","author":"Mansi","year":"2011","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_23","unstructured":"M\u00fcllner, D. (2011). Modern hierarchical, agglomerative clustering algorithms. arXiv."},{"key":"ref_24","first-page":"3","article-title":"Agglomerative Hierarchical Clustering Algorithm\u2014A Review","volume":"3","author":"Sasirekha","year":"2013","journal-title":"Int. J. Sci. Res. Publ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TNN.2005.845141","article-title":"Survey of clustering algorithms","volume":"16","author":"Xu","year":"2005","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e1219","DOI":"10.1002\/widm.1219","article-title":"Algorithms for hierarchical clustering: An overview, II","volume":"7","author":"Murtagh","year":"2017","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_27","first-page":"93","article-title":"Comparative Study of K-Means and Hierarchical Clustering Techniques","volume":"2","author":"Kaushik","year":"2014","journal-title":"Int. J. Softw. Hardw. Res. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1016\/j.cad.2009.07.003","article-title":"Shape-based clustering for 3D CAD objects: A comparative study of effectiveness","volume":"41","author":"Jayanti","year":"2009","journal-title":"Comput.-Aided Des."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gundelwein, L., Ramm, H., Goubergrits, L., Kelm, M., and Lamecker, H. (2018). 3D Shape Analysis for Coarctation of the Aorta. Shape in Medical Imaging, Springer.","DOI":"10.1007\/978-3-030-04747-4_7"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110303","DOI":"10.1016\/j.asoc.2023.110303","article-title":"Hierarchical Clustering: Visualization, Feature Importance and Model Selection","volume":"141","author":"Cabezas","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1093\/comjnl\/9.4.373","article-title":"A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems","volume":"9","author":"Lance","year":"1967","journal-title":"Comput. J."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sophocleous, F., B\u00f4ne, A., Shearn, A.I.U., Forte, M.N.V., Bruse, J.L., Caputo, M., and Biglino, G. (2022). Feasibility of a Longitudinal Statistical Atlas Model to Study Aortic Growth in Congenital Heart Disease. Comput. Biol. Med., 144.","DOI":"10.1016\/j.compbiomed.2022.105326"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/15\/4577\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:30:24Z","timestamp":1760110224000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/13\/15\/4577"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,5]]},"references-count":32,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["jcm13154577"],"URL":"https:\/\/doi.org\/10.3390\/jcm13154577","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,5]]}}}