{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T10:45:16Z","timestamp":1785926716707,"version":"3.56.0"},"reference-count":29,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Neonatal echocardiography remains an essential imaging modality for the assessment of congenital and hemodynamic cardiovascular abnormalities in critically ill neonates. Recent advances in quantitative ultrasound biomarkers, deformation imaging, volumetric reconstruction, and artificial intelligence (AI)-assisted analysis have substantially expanded the diagnostic capabilities of neonatal cardiovascular ultrasound imaging. This narrative review critically examines current developments in quantitative echocardiographic imaging, advanced volumetric methodologies, and AI-assisted cardiovascular ultrasound analysis, with emphasis on neonatal intensive care applications. A literature search was conducted using PubMed, Google Scholar, Scopus and Embase focusing on neonatal echocardiography, spatiotemporal image correlation (STIC), speckle-tracking echocardiography, artificial intelligence, machine learning, and quantitative cardiovascular imaging. Recent studies suggest that advanced methodologies, including speckle-tracking echocardiography, STIC-based reconstruction, automated segmentation algorithms, and deep learning frameworks, may improve image standardization, automated quantification, and congenital heart disease detection. However, important challenges persist, including operator dependency, dataset heterogeneity, limited external validation, cross-platform variability, and incomplete integration into routine neonatal intensive care workflows. Most AI-assisted echocardiographic systems remain investigational and require further prospective multicenter validation before widespread clinical implementation can be achieved.<\/jats:p>","DOI":"10.3390\/jimaging12080356","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T09:16:22Z","timestamp":1785921382000},"page":"356","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantitative Ultrasound Imaging and Artificial Intelligence in Neonatal Echocardiography: Methodological Advances, Reproducibility Challenges, and Computational Perspectives"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9065-7971","authenticated-orcid":false,"given":"Aikaterini I.","family":"Nikolaou","sequence":"first","affiliation":[{"name":"Neonatal Intensive Care Unit, School of Medicine, University of Ioannina, 45500 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikitas","family":"Chatzigiannis","sequence":"additional","affiliation":[{"name":"Neonatal Intensive Care Unit, School of Medicine, University of Ioannina, 45500 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Alexandra","family":"Kefala","sequence":"additional","affiliation":[{"name":"Neonatal Intensive Care Unit, School of Medicine, University of Ioannina, 45500 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4245-1032","authenticated-orcid":false,"given":"Eleni","family":"Papaioannou","sequence":"additional","affiliation":[{"name":"Neonatal Intensive Care Unit, Hippokration Hospital, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Baltogianni","sequence":"additional","affiliation":[{"name":"Neonatal Intensive Care Unit, School of Medicine, University of Ioannina, 45500 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lida-Eleni","family":"Giaprou","sequence":"additional","affiliation":[{"name":"Neonatal Intensive Care Unit, School of Medicine, University of Ioannina, 45500 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5679-3850","authenticated-orcid":false,"given":"Vasileios","family":"Giapros","sequence":"additional","affiliation":[{"name":"Neonatal Intensive Care Unit, School of Medicine, University of Ioannina, 45500 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bravo-Valenzuela, N.J., Giffoni, M.C., Nieblas, C.O., Werner, H., Tonni, G., Granese, R., Gon\u00e7alves, L.F., and Araujo J\u00fanior, E. (2024). Three-dimensional ultrasound for physical and virtual fetal heart models: Current status and future perspectives. J. Clin. Med., 13.","DOI":"10.20944\/preprints202411.0207.v1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1097\/GRF.0000000000000992","article-title":"Enhancing fetal cardiac imaging with artificial intelligence: A review of the current evidence and future directions","volume":"69","author":"Martins","year":"2026","journal-title":"Clin. Obstet. Gynecol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nestaas, E. (2022). Neonatologist performed echocardiography for evaluating the newborn infant. Front. Pediatr., 10.","DOI":"10.3389\/fped.2022.853205"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1097\/MOP.0000000000000863","article-title":"Point-of-care ultrasound in the neonatal ICU","volume":"32","author":"Miller","year":"2020","journal-title":"Curr. Opin. Pediatr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1016\/j.echo.2011.07.014","article-title":"Targeted neonatal echocardiography in the neonatal intensive care unit: Practice guidelines and recommendations for training","volume":"24","author":"Mertens","year":"2011","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.echo.2023.11.016","article-title":"Guidelines and recommendations for targeted neonatal echocardiography and cardiac point-of-care ultrasound in the neonatal intensive care unit: An update from the American Society of Echocardiography","volume":"37","author":"McNamara","year":"2024","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, Q., Yin, J., Zhang, X., Ou, H., Li, F., Zhang, Y., Wan, W., Guo, C., Cao, Y., and Luo, T. (2025). Applications of artificial intelligence in early childhood health management: A systematic review from fetal to pediatric periods. Front. Pediatr., 13.","DOI":"10.3389\/fped.2025.1613150"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liastuti, L.D., and Nursakina, Y. (2025). Diagnostic accuracy of artificial intelligence models in detecting congenital heart disease in the second-trimester fetus through prenatal cardiac screening: A systematic review and meta-analysis. Front. Cardiovasc. Med., 12.","DOI":"10.3389\/fcvm.2025.1473544"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1002\/uog.12563","article-title":"Fetal Intelligent Navigation Echocardiography (FINE): A novel method for rapid, simple, and automatic examination of the fetal heart","volume":"42","author":"Yeo","year":"2013","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1002\/jum.14616","article-title":"Fetal intelligent navigation echocardiography (FINE) detects 98% of congenital heart disease","volume":"37","author":"Yeo","year":"2018","journal-title":"J. Ultrasound Med."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, A., Doan, T.T., Reddy, C., and Jone, P.-N. (2025). Artificial intelligence in fetal and pediatric echocardiography. Children, 12.","DOI":"10.3390\/children12010014"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1161\/CIRCULATIONAHA.120.047530","article-title":"Automated recognition of regional wall motion abnormalities through deep neural network interpretation of transthoracic echocardiography","volume":"142","author":"Huang","year":"2020","journal-title":"Circulation"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e63175","DOI":"10.2196\/63175","article-title":"Opportunities and challenges of using artificial intelligence in predicting clinical outcomes and length of stay in neonatal intensive care units: Systematic review","volume":"27","author":"Tudor","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e75270","DOI":"10.2196\/75270","article-title":"Artificial intelligence-assisted image extraction in neonatal echocardiography for congenital heart disease diagnosis in Sub-Saharan Africa: Protocol for model development","volume":"14","author":"Leke","year":"2025","journal-title":"JMIR Res. Protoc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/ehjci\/jeu184","article-title":"Definitions for a common standard for 2D speckle tracking echocardiography: Consensus document of the EACVI\/ASE\/Industry Task Force to standardize deformation imaging","volume":"16","author":"Voigt","year":"2015","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1093\/ehjci\/jey042","article-title":"Standardization of left atrial, right ventricular, and right atrial deformation imaging using two-dimensional speckle tracking echocardiography: A consensus document of the EACVI\/ASE\/Industry Task Force to standardize deformation imaging","volume":"19","author":"Badano","year":"2018","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1016\/j.echo.2025.07.007","article-title":"Clinical applications of strain echocardiography: A clinical consensus statement from the American Society of Echocardiography developed in collaboration with the European Association of Cardiovascular Imaging of the European Society of Cardiology","volume":"38","author":"Thomas","year":"2025","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3079","DOI":"10.1016\/j.ultrasmedbio.2021.07.011","article-title":"Quality assurance of segmental strain values provided by commercial 2-D speckle tracking echocardiography using in silico models: A report from the EACVI-ASE Strain Standardization Task Force","volume":"47","author":"Alessandrini","year":"2021","journal-title":"Ultrasound Med. Biol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jcmg.2017.01.027","article-title":"Variability and reproducibility of segmental longitudinal strain measurement: A report from the EACVI-ASE Strain Standardization Task Force","volume":"11","author":"Mirea","year":"2018","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2707","DOI":"10.1002\/jum.16305","article-title":"Automated identification of patent ductus arteriosus using a computer vision model","volume":"42","author":"Erno","year":"2023","journal-title":"J. Ultrasound Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2060","DOI":"10.1038\/s41598-025-85216-7","article-title":"Precision fetal cardiology detects cyanotic congenital heart disease using maternal saliva metabolome and artificial intelligence","volume":"15","author":"Ashrafi","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4951","DOI":"10.1007\/s00431-024-05773-3","article-title":"Automated analysis of heart sound signals in screening for structural heart disease in children","volume":"183","author":"Papunen","year":"2024","journal-title":"Eur. J. Pediatr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103170","DOI":"10.1016\/j.radi.2025.103170","article-title":"Artificial intelligence aided ultrasound imaging of foetal congenital heart disease: A scoping review","volume":"31","author":"Norris","year":"2025","journal-title":"Radiography"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"29382","DOI":"10.1038\/s41598-024-79361-8","article-title":"Extraction and evaluation of features of preterm patent ductus arteriosus in chest X-ray images using deep learning","volume":"14","author":"Chang","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1016\/j.jcmg.2018.11.037","article-title":"The future of cardiovascular computed tomography: Advanced analytics and clinical insights","volume":"12","author":"Nicol","year":"2019","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3610","DOI":"10.1053\/j.jvca.2022.05.004","article-title":"Retraining an artificial intelligence algorithm to calculate left ventricular ejection fraction in pediatrics","volume":"36","author":"Zuercher","year":"2022","journal-title":"J. Cardiothorac. Vasc. Anesth."},{"key":"ref_27","first-page":"e97874","article-title":"Bringing imaging to the bedside: The growing impact of point-of-care ultrasound in pediatric and neonatal intensive care","volume":"17","author":"Manoj","year":"2025","journal-title":"Cureus"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e101263","DOI":"10.1136\/bmjopen-2025-101263","article-title":"Prenatal detection of congenital heart defects using deep learning-based image and video analysis: Protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study","volume":"15","author":"Patey","year":"2025","journal-title":"BMJ Open"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Quintana, S., Schwarz, C.E., Shelevytsky, I., Shelevytska, V., Semenova, O., Factor, A., Popovici, E., and Temko, A. (2021). A framework for AI-assisted detection of patent ductus arteriosus from neonatal phonocardiogram. 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