{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T20:21:36Z","timestamp":1771705296740,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T00:00:00Z","timestamp":1741392000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T00:00:00Z","timestamp":1741392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project of the Ministry of Science and Technology of China","doi-asserted-by":"crossref","award":["2021ZD0140407"],"award-info":[{"award-number":["2021ZD0140407"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s10489-025-06419-z","type":"journal-article","created":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T01:02:27Z","timestamp":1741395747000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automated echocardiogram image quality assessment with YOLO and resnet in the left ventricular myocardium of A4C views"],"prefix":"10.1007","volume":"55","author":[{"given":"Weiyang","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiushuang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peifang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujiao","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yawei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongli","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowan","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3335-5700","authenticated-orcid":false,"given":"Kunlun","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,8]]},"reference":[{"key":"6419_CR1","doi-asserted-by":"publisher","unstructured":"Akkus Z, Aly YH, Attia IZ, Lopez-Jimenez F, Arruda-Olson AM, Pellikka PA, Pislaru SV, Kane GC, Friedman PA, Oh JK (2021) Artificial intelligence (ai)-empowered echocardiography interpretation: A state-of-the-art review. J Clin Med. 10(7). https:\/\/doi.org\/10.3390\/jcm10071391","DOI":"10.3390\/jcm10071391"},{"issue":"10","key":"6419_CR2","doi-asserted-by":"publisher","first-page":"2703","DOI":"10.1053\/j.jvca.2020.04.056","volume":"34","author":"H Fatima","year":"2020","unstructured":"Fatima H, Mahmood F, Sehgal S, Belani K, Sharkey A, Chaudhary O, Baribeau Y, Matyal R, Khabbaz KR (2020) Artificial intelligence for dynamic echocardiographic tricuspid valve analysis: A new tool in echocardiography. J Cardiothorac Vascular Anesthes. 34(10):2703\u20132706. https:\/\/doi.org\/10.1053\/j.jvca.2020.04.056","journal-title":"J Cardiothorac Vascular Anesthes."},{"key":"6419_CR3","doi-asserted-by":"publisher","unstructured":"Siqueira VS, Borges MM, Furtado RG, Dourado CN, Costa RM (2021) Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review. Artif Intell Med. 120:102165. https:\/\/doi.org\/10.1016\/j.artmed.2021.102165","DOI":"10.1016\/j.artmed.2021.102165"},{"issue":"4","key":"6419_CR4","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.1109\/TMI.2023.3339204","volume":"43","author":"P Liu","year":"2024","unstructured":"Liu P, Huang G, Jing J, Bian S, Cheng L, Lu XY, Rao C, Liu Y, Hua Y, Wang Y, He K (2024) An energy matching vessel segmentation framework in 3-D medical images. IEEE Trans Med Imaging 43(4):1476\u20131488","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"6419_CR5","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","volume":"5","author":"S Liu","year":"2019","unstructured":"Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX, Ni D, Wang T (2019) Deep learning in medical ultrasound analysis: A review. Eng 5(2):261\u2013275. https:\/\/doi.org\/10.1016\/j.eng.2018.11.020","journal-title":"Eng"},{"key":"6419_CR6","unstructured":"Li M, Zeng D, Fei H, Song H, Chen J, Cao S, Hu B, Zhou Y, Guo Y, Xu X, Huang K, Zhang J, Zhou Q. Automatic myocardial contrast echocardiography image quality assessment using deep learning: Impact on myocardial perfusion evaluation. (1879-291X (Electronic))"},{"key":"6419_CR7","unstructured":"Labs RB, Vrettos A, Azarmehr N, Howard JP, Zolgharni M. Automated assessment of image quality in 2d echocardiography using deep learning. In: ICRMIRO 2020: International Conference on Radiology, Medical Imaging and Radiation Oncology"},{"key":"6419_CR8","doi-asserted-by":"crossref","unstructured":"Abdi A, Luong C, Tsang T, Jue J, Hawley D, Fleming S, Gin K, Swift J, Rohling R, Abolmaesumi PJITMI (2017) Automatic quality assessment of echocardiograms using convolutional neural networks: Feasibility on the apical four-chamber view, 1\u20131","DOI":"10.1117\/12.2254585"},{"key":"6419_CR9","doi-asserted-by":"crossref","unstructured":"Huang Z, Li L, Krizek GC, Sun L (2023) Research on traffic sign detection based on improved yolov8. J Comput Commun. 11(7)","DOI":"10.4236\/jcc.2023.117014"},{"key":"6419_CR10","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep Residual Learning for Image Recognition. arXiv:1512.03385","DOI":"10.1109\/CVPR.2016.90"},{"key":"6419_CR11","doi-asserted-by":"publisher","unstructured":"Gaspari R, Harvey J, DiCroce C, Nalbandian A, Hill M, Lindsay R, Nordberg A, Graham P, Kamilaris A, Gleeson T (2021) Echocardiographic pre-pause imaging and identifying the acoustic window during cpr reduces cpr pause time during acls - a prospective cohort study. Resusc Plus. 6:100094. https:\/\/doi.org\/10.1016\/j.resplu.2021.100094","DOI":"10.1016\/j.resplu.2021.100094"},{"key":"6419_CR12","doi-asserted-by":"publisher","unstructured":"Gaspari R, Teran F, Kamilaris A, Gleeson T (2021) Development and validation of a novel image quality rating scale for echocardiography during cardiac arrest. Resusc Plus. 6:100097. https:\/\/doi.org\/10.1016\/j.resplu.2021.100097","DOI":"10.1016\/j.resplu.2021.100097"},{"key":"6419_CR13","doi-asserted-by":"publisher","unstructured":"Wu J, Wei G, Fan Y, Yu L, Chen B (2024) B-ultrasound guided venipuncture vascular recognition system based on deep learning. Biomed Signal Process Contr. 87. https:\/\/doi.org\/10.1016\/j.bspc.2023.105495","DOI":"10.1016\/j.bspc.2023.105495"},{"issue":"1","key":"6419_CR14","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12947-021-00261-2","volume":"19","author":"J Zhou","year":"2021","unstructured":"Zhou J, Du M, Chang S, Chen Z (2021) Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis. Cardiovasc Ultrasound. 19(1):29. https:\/\/doi.org\/10.1186\/s12947-021-00261-2","journal-title":"Cardiovasc Ultrasound."},{"issue":"16","key":"6419_CR15","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1161\/circulationaha.118.034338","volume":"138","author":"J Zhang","year":"2018","unstructured":"Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, Lassen MH, Fan E, Aras MA, Jordan C, Fleischmann KE, Melisko M, Qasim A, Shah SJ, Bajcsy R, Deo RC (2018) Fully automated echocardiogram interpretation in clinical practice. Circulation. 138(16):1623\u20131635. https:\/\/doi.org\/10.1161\/circulationaha.118.034338","journal-title":"Circulation."},{"key":"6419_CR16","doi-asserted-by":"publisher","unstructured":"Subhan S, Malik J, Haq Au, Qadeer MS, Zaidi SMJ, Orooj F, Zaman H, Mehmoodi A, Majeedi U (2023) Role of artificial intelligence and machine learning in interventional cardiology. Current Prob Cardio. 48(7). https:\/\/doi.org\/10.1016\/j.cpcardiol.2023.101698","DOI":"10.1016\/j.cpcardiol.2023.101698"},{"issue":"19","key":"6419_CR17","doi-asserted-by":"publisher","first-page":"2039","DOI":"10.1016\/j.jacc.2014.08.003","volume":"64","author":"RM Campbell","year":"2014","unstructured":"Campbell RM, Douglas PS, Eidem BW, Lai WW, Lopez L, Sachdeva R (2014) Acc\/aap\/aha\/ase\/hrs\/scai\/scct\/scmr\/sope 2014 appropriate use criteria for initial transthoracic echocardiography in outpatient pediatric cardiology: A report of the american college of cardiology appropriate use criteria task force, american academy of pediatrics, american heart association, american society of echocardiography, heart rhythm society, society for cardiovascular angiography and interventions, society of cardiovascular computed tomography, society for cardiovascular magnetic resonance, and society of pediatric echocardiography. J American College of Cardio. 64(19):2039\u20132060. https:\/\/doi.org\/10.1016\/j.jacc.2014.08.003","journal-title":"J American College of Cardio."},{"issue":"9","key":"6419_CR18","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1016\/j.echo.2019.05.006","volume":"32","author":"PS Douglas","year":"2019","unstructured":"Douglas PS, Carabello BA, Lang RM, Lopez L, Pellikka PA, Picard MH, Thomas JD, Varghese P, Wang TY, Weissman NJ, Wilgus R, Bozkurt B, Jneid H, Al-Khatib SM, Anderson HV, Gilstrap L, Graham GN, Jones GK, Kao D, Lopez L, Marcus G, Rymer J, Tcheng JE, Weintraub WS (2019) 2019 acc\/aha\/ase key data elements and definitions for transthoracic echocardiography: A report of the american college of cardiology\/american heart association task force on clinical data standards (writing committee to develop clinical data standards for transthoracic echocardiography) and the american society of echocardiography. J American Soc Echocardiograph. 32(9):1161\u20131248. https:\/\/doi.org\/10.1016\/j.echo.2019.05.006","journal-title":"J American Soc Echocardiograph."},{"issue":"1","key":"6419_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.echo.2010.11.006","volume":"24","author":"MH Picard","year":"2011","unstructured":"Picard MH, Adams D, Bierig SM, Dent JM, Douglas PS, Gillam LD, Keller AM, Malenka DJ, Masoudi FA, McCulloch M, Pellikka PA, Peters PJ, Stainback RF, Strachan GM, Zoghbi WA (2011) American society of echocardiography recommendations for quality echocardiography laboratory operations. J American Soc Echocardiograph. 24(1):1\u201310. https:\/\/doi.org\/10.1016\/j.echo.2010.11.006","journal-title":"J American Soc Echocardiograph."},{"issue":"1","key":"6419_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.echo.2014.10.003","volume":"28","author":"RM Lang","year":"2015","unstructured":"Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, Flachskampf FA, Foster E, Goldstein SA, Kuznetsova T, Lancellotti P, Muraru D, Picard MH, Rietzschel ER, Rudski L, Spencer KT, Tsang W, Voigt JU (2015) Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the american society of echocardiography and the european association of cardiovascular imaging. J Am Soc Echocardiogr. 28(1):1\u20133914. https:\/\/doi.org\/10.1016\/j.echo.2014.10.003","journal-title":"J Am Soc Echocardiogr."},{"issue":"5","key":"6419_CR21","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1016\/j.jemermed.2021.10.032","volume":"62","author":"RJ Gaspari","year":"2022","unstructured":"Gaspari RJ, Gleeson T, Alerhand S, Caputo W, Damewood S, Dicroce C, Dwyer K, Gibbons R, Greenstein J, Harvey J, Hill M, Hoffmann B, Jordan MK, Karfunkle B, Kropf C, Lindsay R, Luo S, Lusiak M, Nalbandian A, Naraghi L, Nelson B, Nickels LC, Nolting L, Nordberg A, Panicker A, Pare J, Peach M, Pinto D, Graham P, Rose G, Russell F, Schafer J, Scheatzle M, Schnittke N, Shpilko M, Soucy Z, Stowell JR, Vryhof D, Gottlieb M (2022) A multicenter, prospective study comparing subxiphoid and parasternal views during brief echocardiography: Effect on image quality, acquisition time, and visualized anatomy. The J Emergency Med. 62(5):648\u2013656. https:\/\/doi.org\/10.1016\/j.jemermed.2021.10.032","journal-title":"The J Emergency Med."},{"key":"6419_CR22","doi-asserted-by":"crossref","unstructured":"Van\u00a0Woudenberg N, Liao Z, Abdi AH, Girgis H, Luong C, Vaseli H, Behnami D, Zhang H, Gin K, Rohling R, Tsang T, Abolmaesumi P. Quantitative echocardiography: Real-time quality estimation and view classification implemented on a mobile android device. In: Stoyanov D, Taylor Z, Aylward S, Tavares JMRS, Xiao Y, Simpson A, Martel A, Maier-Hein L, Li S, Rivaz H, Reinertsen I, Chabanas M, Farahani K (eds) Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation, pp 74\u201381. Springer","DOI":"10.1007\/978-3-030-01045-4_9"},{"key":"6419_CR23","unstructured":"Rachel B\u00a0Liu, FCAEUS MD, Mike\u00a0Blaivas FAma-l MD, Chris\u00a0Moore FAma-laote MD, Adam B\u00a0Sivitz, Ama-l MD, Matt\u00a0Flannigan FAEUSISC-C DO, Alfredo\u00a0Tirado FAma-laote MD, Vivek S\u00a0Tayal, FAEUSPC MD (2018) Acep emergency ultrasound standard reporting guidelines. American College of Emergency Physicians"},{"key":"6419_CR24","doi-asserted-by":"publisher","unstructured":"Yang F, Chen X, Lin X, Chen X, Wang W, Liu B, Li Y, Pu H, Zhang L, Huang D, Zhang M, Li X, Wang H, Wang Y, Guo H, Deng Y, Zhang L, Zhong Q, Li Z, Yu L, Duan Y, Zhang P, Wu Z, Burkhoff D, Wang Q, He K (2022) Automated analysis of doppler echocardiographic videos as a screening tool for valvular heart diseases. JACC: Cardiovascular Imaging. 15(4):551\u2013563. https:\/\/doi.org\/10.1016\/j.jcmg.2021.08.015","DOI":"10.1016\/j.jcmg.2021.08.015"},{"key":"6419_CR25","doi-asserted-by":"publisher","unstructured":"Ahmed A, Imran AS, Manaf A, Kastrati Z, Daudpota SM (2024) Enhancing wrist abnormality detection with yolo: Analysis of state-of-the-art single-stage detection models. Biomed Signal Process Contr. 93. https:\/\/doi.org\/10.1016\/j.bspc.2024.106144","DOI":"10.1016\/j.bspc.2024.106144"},{"key":"6419_CR26","doi-asserted-by":"publisher","unstructured":"Salman ME, Cakirsoy Cakar G, Azimjonov J, Kosem M, Hakki\u00a0Cedimoglu (2022) Automated prostate cancer grading and diagnosis system using deep learning-based yolo object detection algorithm. Exp Syst Appl. 201:117148. https:\/\/doi.org\/10.1016\/j.eswa.2022.117148","DOI":"10.1016\/j.eswa.2022.117148"},{"key":"6419_CR27","doi-asserted-by":"crossref","unstructured":"Vishal\u00a0Chandra PGS, Singh3 V (2020) Mitral valve leaflet tracking in echocardiography using custom yolo3. Procedia Comput Sci. (171)","DOI":"10.1016\/j.procs.2020.04.089"},{"key":"6419_CR28","doi-asserted-by":"publisher","unstructured":"Zhuang Z, Liu G, Ding W, Raj ANJ, Qiu S, Guo J, Yuan Y (2020) Cardiac vfm visualization and analysis based on yolo deep learning model and modified 2d continuity equation. Comput Med Imaging Graph. 82:101732 https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101732","DOI":"10.1016\/j.compmedimag.2020.101732"},{"key":"6419_CR29","doi-asserted-by":"publisher","unstructured":"Samant S, Bakhos JJ, Wu W, Zhao S, Kassab GS, Khan B, Panagopoulos A, Makadia J, Oguz UM, Banga A, Fayaz M, Glass W, Chiastra C, Burzotta F, LaDisa JF, Iaizzo P, Murasato Y, Dubini G, Migliavacca F, Mickley T, Bicek A, Fontana J, West NEJ, Mortier P, Boyers PJ, Gold JP, Anderson DR, Tcheng JE, Windle JR, Samady H, Jaffer FA, Desai NR, Lansky A, Mena-Hurtado C, Abbott D, Brilakis ES, Lassen JF, Louvard Y, Stankovic G, Serruys PW, Velazquez E, Elias P, Bhatt DL, Dangas G, Chatzizisis YS (2023) Artificial intelligence, computational simulations, and extended reality in cardiovascular interventions. JACC: Cardiovascular Interventions. 16(20):2479\u20132497 . https:\/\/doi.org\/10.1016\/j.jcin.2023.07.022","DOI":"10.1016\/j.jcin.2023.07.022"},{"issue":"7","key":"6419_CR30","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1016\/j.echo.2023.02.017","volume":"36","author":"IM Salte","year":"2023","unstructured":"Salte IM, \u00d8stvik A, Olaisen SH, Karlsen S, Dahlslett T, Smistad E, Eriksen-Volnes TK, Brunvand H, Haugaa KH, Edvardsen T, Dalen H, Lovstakken L, Grenne B (2023) Deep learning for improved precision and reproducibility of left ventricular strain in echocardiography: A test-retest study. J American Soc Echocardiograph. 36(7):788\u2013799. https:\/\/doi.org\/10.1016\/j.echo.2023.02.017","journal-title":"J American Soc Echocardiograph."},{"issue":"1","key":"6419_CR31","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s10554-020-01981-8","volume":"37","author":"C Luong","year":"2021","unstructured":"Luong C, Liao Z, Abdi A, Girgis H, Rohling R, Gin K, Jue J, Yeung D, Szefer E, Thompson D, Tsang MY, Lee PK, Nair P, Abolmaesumi P, Tsang TSM (2021) Automated estimation of echocardiogram image quality in hospitalized patients. Int J Cardiovasc Imaging. 37(1):229\u2013239. https:\/\/doi.org\/10.1007\/s10554-020-01981-8","journal-title":"Int J Cardiovasc Imaging."},{"issue":"6","key":"6419_CR32","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1001\/jamacardio.2021.0185","volume":"6","author":"A Narang","year":"2021","unstructured":"Narang A, Bae R, Hong H, Thomas Y, Surette S, Cadieu C, Chaudhry A, Martin RP, McCarthy PM, Rubenson DS, Goldstein S, Little SH, Lang RM, Weissman NJ, Thomas JD (2021) Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use. JAMA Cardiol. 6(6):624\u2013632. https:\/\/doi.org\/10.1001\/jamacardio.2021.0185","journal-title":"JAMA Cardiol."},{"key":"6419_CR33","doi-asserted-by":"crossref","unstructured":"J XGZQYFWYLRZJ, B L (2023) Quality assessment for fetal four-chamber ultrasound views based on two-stage segmentation. journal of image and graphics. J Image Graph. 28(08):2476\u20132490","DOI":"10.11834\/jig.220347"},{"issue":"1","key":"6419_CR34","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.echo.2023.10.004","volume":"37","author":"WA Zoghbi","year":"2024","unstructured":"Zoghbi WA, Jone PN, Chamsi-Pasha MA, Chen T, Collins KA, Desai MY, Grayburn P, Groves DW, Hahn RT, Little SH, Kruse E, Sanborn D, Shah SB, Sugeng L, Swaminathan M, Thaden J, Thavendiranathan P, Tsang W, Weir-McCall JR, Gill E (2024) Guidelines for the evaluation of prosthetic valve function with cardiovascular imaging: A report from the american society of echocardiography developed in collaboration with the society for cardiovascular magnetic resonance and the society of cardiovascular computed tomography. J Am Soc Echocardiogr. 37(1):2\u201363. https:\/\/doi.org\/10.1016\/j.echo.2023.10.004","journal-title":"J Am Soc Echocardiogr."},{"key":"6419_CR35","doi-asserted-by":"publisher","unstructured":"Wu Y, Wang Y, Li D, Zhang J (2023) Two-step detection of concrete internal condition using array ultrasound and deep learning. NDT and E International. 139 . https:\/\/doi.org\/10.1016\/j.ndteint.2023.102945","DOI":"10.1016\/j.ndteint.2023.102945"},{"issue":"6","key":"6419_CR36","doi-asserted-by":"publisher","first-page":"1868","DOI":"10.1109\/tmi.2019.2959209","volume":"39","author":"Z Liao","year":"2020","unstructured":"Liao Z, Girgis H, Abdi A, Vaseli H, Hetherington J, Rohling R, Gin K, Tsang T, Abolmaesumi P (2020) On modelling label uncertainty in deep neural networks: Automatic estimation of intra- observer variability in 2d echocardiography quality assessment. IEEE Trans Med Imaging. 39(6):1868\u20131883. https:\/\/doi.org\/10.1109\/tmi.2019.2959209","journal-title":"IEEE Trans Med Imaging."},{"issue":"6","key":"6419_CR37","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1038\/s41569-023-00878-y","volume":"20","author":"I Fernandez-Ruiz","year":"2023","unstructured":"Fernandez-Ruiz I (2023) Ai outperforms sonographers at diagnosing cardiac function on echocardiography. Nat Rev Cardiol. 20(6):371. https:\/\/doi.org\/10.1038\/s41569-023-00878-y","journal-title":"Nat Rev Cardiol."},{"key":"6419_CR38","doi-asserted-by":"publisher","unstructured":"Lopez L, Saurers DL, Barker PCA, Cohen MS, Colan SD, Dwyer J, Forsha D, Friedberg MK, Lai WW, Printz BF, Sachdeva R, Soni-Patel NR, Truong DT, Young LT, Altman CA (2024) Guidelines for performing a comprehensive pediatric transthoracic echocardiogram: Recommendations from the american society of echocardiography. J Am Soc Echocardiogr. 37(2):119\u2013170. https:\/\/doi.org\/10.1016\/j.echo.2023.11.015","DOI":"10.1016\/j.echo.2023.11.015"},{"key":"6419_CR39","unstructured":"Cardiology AAC, Pediatric (2018) Acc. adult congenital and pediatric cardiology quality network quality metrics. https:\/\/cvquality.acc.org\/initiatives\/acpc-quality-network\/quality-metrics"},{"key":"6419_CR40","doi-asserted-by":"publisher","unstructured":"Ostvik A, Smistad E, Aase SA, Haugen BO, Lovstakken L (2019) Real-time standard view classification in transthoracic echocardiography using convolutional neural networks. Ultrasound Med Biol. 45(2):374\u2013384. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2018.07.024","DOI":"10.1016\/j.ultrasmedbio.2018.07.024"},{"issue":"5","key":"6419_CR41","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1536\/ihj.20-236","volume":"61","author":"S Hei","year":"2020","unstructured":"Hei S, Iwataki M, Jang JY, Kuwaki H, Fukuda S, Kim YJ, Toki M, Onoue T, Hayashi A, Nishino S, Watanabe N, Hayashida A, Tsuda Y, Araki M, Nishimura Y, Song JK, Yoshida K, Levine RA, Otsuji Y (2020) Relations of augmented systolic annular expansion and leaflet\/papillary muscle dynamics in late-systolic mitral valve prolapse evaluated by echocardiography with a speckle tracking analysis. Int Heart J. 61(5):970\u2013978. https:\/\/doi.org\/10.1536\/ihj.20-236","journal-title":"Int Heart J."},{"key":"6419_CR42","doi-asserted-by":"publisher","unstructured":"Ramirez-Bautista JA, Chaparro-C\u00e1rdenas SL, Esmer C, Huerta-Ruelas JA (2024) Artificial intelligence approaches to physiological parameter analysis in the monitoring and treatment of non-communicable diseases: A review. Biomed Signal Process Contr. 87. https:\/\/doi.org\/10.1016\/j.bspc.2023.105463","DOI":"10.1016\/j.bspc.2023.105463"},{"key":"6419_CR43","doi-asserted-by":"publisher","unstructured":"Mumtaz H, Saqib M, Ansar F, Zargar D, Hameed M, Hasan M, Muskan P (2022) The future of cardiothoracic surgery in artificial intelligence. Annal Med Surg. 80 . https:\/\/doi.org\/10.1016\/j.amsu.2022.104251","DOI":"10.1016\/j.amsu.2022.104251"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06419-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06419-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06419-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:30:45Z","timestamp":1758310245000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06419-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,8]]},"references-count":43,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["6419"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06419-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,8]]},"assertion":[{"value":"26 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2025","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}],"article-number":"513"}}