{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T03:16:31Z","timestamp":1782962191707,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordination for the Improvement of Higher Education Personnel (Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2014CAPES)","doi-asserted-by":"publisher","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The mitral valve is the most susceptible to pathological alterations, such as mitral stenosis, characterized by failure of the valve to open completely. In this context, the objective of this study was to apply digital image processing (DIP) and develop a convolutional neural network (CNN) to provide decision support for specialists in the diagnosis of mitral stenosis based on transesophageal echocardiography examinations. The following procedures were implemented: acquisition of echocardiogram exams; application of DIP; use of augmentation techniques; and development of a CNN. The DIP classified 26.7% cases without stenosis, 26.7% with mild stenosis, 13.3% with moderate stenosis, and 33.3% with severe stenosis. A CNN was initially developed to classify videos into those four categories. However, the number of acquired exams was insufficient to effectively train the model for this purpose. So, the final model was trained to differentiate between videos with or without stenosis, achieving an accuracy of 92% with a loss of 0.26. The results demonstrate that both DIP and CNN are effective in distinguishing between cases with and without stenosis. Moreover, DIP was capable of classifying varying degrees of stenosis severity\u2014mild, moderate, and severe\u2014highlighting its potential as a valuable tool in clinical decision support.<\/jats:p>","DOI":"10.3390\/jimaging11080272","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T15:16:37Z","timestamp":1755184597000},"page":"272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Digital Image Processing and Convolutional Neural Network Applied to Detect Mitral Stenosis in Echocardiograms: Clinical Decision Support"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8744-6600","authenticated-orcid":false,"given":"Genilton de Fran\u00e7a","family":"Barros Filho","sequence":"first","affiliation":[{"name":"Postgraduate Program in Mechanical Engineering, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58051-900, PB, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9054-8659","authenticated-orcid":false,"given":"Jos\u00e9 Fernando de Morais","family":"Firmino","sequence":"additional","affiliation":[{"name":"Postgraduate Program in Mechanical Engineering, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58051-900, PB, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Israel","family":"Solha","sequence":"additional","affiliation":[{"name":"Postgraduate Program in Mechanical Engineering, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58051-900, PB, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ewerton Freitas de","family":"Medeiros","sequence":"additional","affiliation":[{"name":"Center of Medical Science, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58051-900, PB, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex dos Santos","family":"Felix","sequence":"additional","affiliation":[{"name":"National Institute of Cardiology, Rio de Janeiro 22240-006, RJ, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9083-7650","authenticated-orcid":false,"given":"Jos\u00e9 Carlos de","family":"Lima J\u00fanior","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58051-900, PB, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8809-8783","authenticated-orcid":false,"given":"Marcelo Dantas Tavares de","family":"Melo","sequence":"additional","affiliation":[{"name":"Center of Medical Science, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58051-900, PB, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelo Cavalcanti","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Federal University of Para\u00edba, Jo\u00e3o Pessoa 58051-900, PB, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","article-title":"GBD-NHLBI-JACC Global Burden of Cardiovascular Diseases Writing Group: Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u20132019: Update from the GBD 2019 Study","volume":"76","author":"Roth","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e254","DOI":"10.1161\/CIR.0000000000000950","article-title":"Heart Disease and Stroke Statistics\u20142021 Update: A Report from the American Heart Association","volume":"143","author":"Virani","year":"2021","journal-title":"Circulation"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1136\/heartjnl-2016-310894","article-title":"Epidemiology of valvular heart disease in a Swedish nationwide hospital-based register study","volume":"103","author":"Andell","year":"2017","journal-title":"Heart"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e38","DOI":"10.1161\/01.CIR.0000115202.33689.2C","article-title":"Mitral Valve Disease","volume":"109","author":"Turi","year":"2004","journal-title":"Circulation"},{"key":"ref_5","first-page":"e20230169","article-title":"Cardiovascular Disease in Latin American Women Gaps and opportunities","volume":"37","author":"Fajardo","year":"2024","journal-title":"Int. J. Cardiovasc. Sci."},{"key":"ref_6","unstructured":"Bonow, R.O., Mann, D.L., Zipes, D.P., and Libby, P. (2017). Echocardiography\u2014Mitral Stenosis\/Valve Diseases. Braunwald\u2019s Heart Disease: A Textbook of Cardiovascular Medicine, Elsevier."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1213\/ANE.0000000000002223","article-title":"Echocardiographic Assessment of Mitral Stenosis Orifice Area: A Comparison of a Novel Three-Dimensional Method Versus Conventional Techniques","volume":"125","author":"Karamnov","year":"2017","journal-title":"Anesth. Analg."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1111\/echo.14397","article-title":"Measurement of mitral valve area in patients with mitral stenosis by 3D echocardiography: A comparison between direct planimetry on 3D zoom and 3D quantification","volume":"36","author":"Sadeghian","year":"2019","journal-title":"Echocardiography"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1007\/s10554-022-02523-0","article-title":"Measurement of mitral valve area by direct three-dimensional planimetry compared to multiplanar reconstruction in patients with rheumatic mitral stenosis","volume":"38","author":"Sakha","year":"2022","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105940","DOI":"10.1016\/j.cmpb.2021.105940","article-title":"Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings","volume":"200","author":"Alkhodari","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1109\/THMS.2022.3211460","article-title":"AI-CardioCare: Artificial Intelligence Based Device for Cardiac Health Monitoring","volume":"52","author":"Joshi","year":"2022","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2514215","DOI":"10.1109\/TIM.2023.3274174","article-title":"Explainable Deep Convolutional Neural Network for Valvular Heart Diseases Classification Using PCG Signals","volume":"72","author":"Bhardwaj","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2885","DOI":"10.1093\/eurheartj\/ehab153","article-title":"Electrocardiogram screening for aortic valve stenosis using artificial intelligence","volume":"42","author":"Attia","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1161\/CIRCULATIONAHA.121.057869","article-title":"rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography","volume":"146","author":"Jing","year":"2022","journal-title":"Circulation"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wegner, F.K., Vidal, M.L.B., Niehues, P., Willy, K., Radke, R.M., Garthe, P.D., Eckardt, L., Baumgartner, H., Diller, G.P., and Orwat, S. (2022). Accuracy of Deep Learning Echocardiographic View Classification in Patients with Congenital or Structural Heart Disease: Importance of Specific Datasets. J. Clin. Med., 11.","DOI":"10.3390\/jcm11030690"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","article-title":"Fully Automated Echocardiogram Interpretation in Clinical Practice","volume":"138","author":"Zhang","year":"2018","journal-title":"Circulation"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1213\/ANE.0000000000000016","article-title":"Guidelines for Performing a Comprehensive Transesophageal Echocardiographic Examination: Recommendations from the American Society of Echocardiography and the Society of Cardiovascular Anesthesiologists","volume":"118","author":"Hahn","year":"2014","journal-title":"Anesth. Analg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.echo.2021.07.006","article-title":"Recommended Standards for the Performance of Transesophageal Echocardiographic Screening for Structural Heart Intervention: From the American Society of Echocardiography","volume":"35","author":"Hahn","year":"2022","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1213\/ANE.0000000000001276","article-title":"Intraoperative Evaluation of Mitral Stenosis by Transesophageal Echocardiography","volume":"123","author":"Cherry","year":"2016","journal-title":"Anesth. Analg."},{"key":"ref_20","unstructured":"Tekalp, A.M. (1995). Basics of Video. Digital Video Processing, Prentice Hall."},{"key":"ref_21","unstructured":"Gonzalez, R.C., and Woods, R.E. (2002). Introduction. Digital Image Processing, Prentice Hall. [2nd ed.]."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1002\/jsid.1275","article-title":"Impact of visual enhancement and color conversion algorithms on remote sound recovery from silent videos","volume":"32","author":"Choong","year":"2024","journal-title":"J. Soc. Inf. Disp."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Blackledge, J.M. (2005). Segmantation and Edge Detection. Digital Image Processing\u2014Mathematical and Computational Methods, Horwood Publishing. [1st ed.].","DOI":"10.1533\/9780857099464.4.486"},{"key":"ref_24","unstructured":"J\u00e4hne, B. (2005). Segmentation. Digital Image Processing, Springer. [6th ed.]."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e371","DOI":"10.36660\/abcimg.2023371i","article-title":"Automatic Measurement of the Mitral Valve Based on Echocardiography Using Digital Image Processing","volume":"36","author":"Solha","year":"2023","journal-title":"Arq. Bras. Cardiol. Imagem Cardiovasc."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Sufian, A., Sultana, F., Chakrabarti, A., and De, D. (2020). Fundamental Concepts of Convolutional Neural Network. Recent Trends and Advances in Artificial Intelligence and Internet of Things, Springer.","DOI":"10.1007\/978-3-030-32644-9_36"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Teuwen, J., and Moriakov, N. (2020). Convolutional neural networks. Handbook of Medical Image Computing and Computer Assisted Intervention, Academic Press.","DOI":"10.1016\/B978-0-12-816176-0.00025-9"},{"key":"ref_28","unstructured":"Bishop, C.M. (2007). Pattern Recognition and Machine Learning (Information Science and Statistics), Springer."},{"key":"ref_29","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_30","unstructured":"Kingma, D.P., and Ba, J. (2024, October 01). Adam: A Method for Stochastic Optimization. Available online: https:\/\/arxiv.org\/abs\/1412.6980."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102165","DOI":"10.1016\/j.artmed.2021.102165","article-title":"Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review","volume":"120","author":"Siqueira","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shahid, K.T., and Schizas, I. (2020). Unsupervised Mitral Valve Tracking for Disease Detection in Echocardiogram Videos. J. Imaging, 6.","DOI":"10.3390\/jimaging6090093"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2890","DOI":"10.1049\/iet-ipr.2019.1757","article-title":"Novel approach for automatic mid-diastole frame detection in 2D echocardiography sequences for performing planimetry of the mitral valve orifice","volume":"14","author":"Faraji","year":"2020","journal-title":"IET Image Process"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/272\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:27:01Z","timestamp":1760034421000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,14]]},"references-count":33,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["jimaging11080272"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11080272","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,14]]}}}