{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T23:25:13Z","timestamp":1787268313845,"version":"build-2736575974"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Amrita Hospital, Faridabad"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cardiovasc Imaging"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Assessment of pulmonary vascularity on chest radiographs (CXRs) in congenital heart disease (CHD) is limited by subjectivity, and existing criteria lack sufficient validation. Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy. We developed and validated structured criteria and a DLM to evaluate pulmonary vascularity and compared it with conventional clinical interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 400 deidentified CXRs from CHD patients with Fick-derived pulmonary-to-systemic flow ratio (Qp:Qs) were evaluated by three experts and three trainees in pediatric cardiology and radiology using nine prespecified criteria. A separate set of 1,088 CXRs was used to train the DLM. The clinician-interpreted CXRs were subsequently assessed by the DLM to predict the Qp:Qs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Intraclass correlation between Fick- and DLM-derived Qp:Qs was 0.782 (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001), with a root mean squared error of 0.395 (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001). The DLM showed a strong agreement with Fick-derived classification of pulmonary vascularity (\u03ba\u2009=\u20090.770,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001; predicting Qp:Qs\u2009&gt;\u20091.5: 90% specificity, 98% sensitivity, area under the receiver operating characteristic curve [AUROC] 98%; predicting Qp:Qs\u2009&lt;\u20090.9: 92% specificity, 95% sensitivity, AUROC 98%). For interpreting pulmonary vascularity as increased or decreased, clinician concordance was 75% and 66%, respectively, whereas DLM concordance was 88% and 98%, respectively (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.03). Among the structured criteria, the number of end-on vessels demonstrated strong interobserver agreement, whereas agreement for individual peripheral vessels, the size of end-on vessels, branch pulmonary arteries with their respective bronchi, and opacity of lung fields was modest.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Deep learning\u2013based analysis of CXRs enables accurate evaluation of pulmonary vascularity and outperforms structured assessment by clinicians.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                    <jats:p>Central illustration<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s44348-026-00084-7","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T23:02:09Z","timestamp":1787266929000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning\u2013based prediction"],"prefix":"10.1186","volume":"34","author":[{"given":"Nisanth","family":"Selvam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dilip","family":"Parasu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshay","family":"Desale","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pranoti","family":"Toshniwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taniya Rachel","family":"Issac","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shine","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Navaneetha","family":"Sasikumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilpa","family":"Radhakrishnan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raman Krishna","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,21]]},"reference":[{"key":"84_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1097\/00005382-198907000-00003","volume":"4","author":"LE Swischuk","year":"1989","unstructured":"Swischuk LE, Stansberry SD. Pulmonary vascularity in pediatric heart disease. J Thorac Imaging. 1989;4:1\u20136.","journal-title":"J Thorac Imaging"},{"key":"84_CR2","first-page":"665","volume":"71","author":"S Viswanathan","year":"2008","unstructured":"Viswanathan S, Kumar RK. Assessment of operability of congenital cardiac shunts with increased pulmonary vascular resistance. Catheter Cardiovasc Interv. 2008;71:665\u201370.","journal-title":"Catheter Cardiovasc Interv"},{"key":"84_CR3","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1007\/s12055-024-01786-7","volume":"41","author":"S Kumar","year":"2025","unstructured":"Kumar S, Kumar RK. Common shunt lesions with pulmonary hypertension: who will benefit from surgery? Indian J Thorac Cardiovasc Surg. 2025;41:718\u201329.","journal-title":"Indian J Thorac Cardiovasc Surg"},{"key":"84_CR4","doi-asserted-by":"publisher","first-page":"2607","DOI":"10.1161\/CIR.0b013e31821b1f10","volume":"123","author":"TF Feltes","year":"2011","unstructured":"Feltes TF, Bacha E, Beekman RH, Cheatham JP, Feinstein JA, Gomes AS, et al. Indications for cardiac catheterization and intervention in pediatric cardiac disease: a scientific statement from the American Heart Association. Circulation. 2011;123:2607\u201352.","journal-title":"Circulation"},{"key":"84_CR5","first-page":"369","volume":"57","author":"C Mar\u00edn Rodr\u00edguez","year":"2015","unstructured":"Mar\u00edn Rodr\u00edguez C, S\u00e1nchez Alegre ML, Lancharro Zapata \u00c1, Alarc\u00f3n Rodr\u00edguez J. What radiologists need to know about the pulmonary-systemic flow ratio (Qp\/Qs): what it is, how to calculate it, and what it is for. Radiol. 2015;57:369\u201379.","journal-title":"Radiol"},{"key":"84_CR6","doi-asserted-by":"publisher","first-page":"2955","DOI":"10.1161\/01.CIR.91.12.2955","volume":"91","author":"WG Hundley","year":"1995","unstructured":"Hundley WG, Li HF, Lange RA, Pfeifer DP, Meshack BM, Willard JE, et al. Assessment of left-to-right intracardiac shunting by velocity-encoded, phase-difference magnetic resonance imaging. A comparison with oximetric and indicator dilution techniques. Circulation. 1995;91:2955\u201360.","journal-title":"Circulation"},{"key":"84_CR7","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1183\/09031936.05.00122104","volume":"25","author":"A Vonk-Noordegraaf","year":"2005","unstructured":"Vonk-Noordegraaf A, van Wolferen SA, Marcus JT, Boonstra A, Postmus PE, Peeters JW, et al. Noninvasive assessment and monitoring of the pulmonary circulation. Eur Respir J. 2005;25:758\u201366.","journal-title":"Eur Respir J"},{"key":"84_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1097\/00005382-198801000-00003","volume":"3","author":"CE Ravin","year":"1988","unstructured":"Ravin CE. Pulmonary vascularity: radiographic considerations. J Thorac Imaging. 1988;3:1\u201313.","journal-title":"J Thorac Imaging"},{"key":"84_CR9","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s00247-006-0133-2","volume":"36","author":"BF Laya","year":"2006","unstructured":"Laya BF, Goske MJ, Morrison S, Reid JR, Swischuck L, Ey EH, et al. The accuracy of chest radiographs in the detection of congenital heart disease and in the diagnosis of specific congenital cardiac lesions. Pediatr Radiol. 2006;36:677\u201381.","journal-title":"Pediatr Radiol"},{"key":"84_CR10","doi-asserted-by":"publisher","first-page":"e15","DOI":"10.1542\/peds.103.2.e15","volume":"103","author":"NH Birkebaek","year":"1999","unstructured":"Birkebaek NH, Hansen LK, Elle B, Andersen PE, Friis M, Egeblad M, et al. Chest roentgenogram in the evaluation of heart defects in asymptomatic infants and children with a cardiac murmur: reproducibility and accuracy. Pediatrics. 1999;103:e15.","journal-title":"Pediatrics"},{"issue":"Suppl 1","key":"84_CR11","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10554-012-0073-x","volume":"28","author":"M Tumkosit","year":"2012","unstructured":"Tumkosit M, Yingyong N, Mahayosnond A, Choo KS, Goo HW. Accuracy of chest radiography for evaluating significantly abnormal pulmonary vascularity in children with congenital heart disease. Int J Cardiovasc Imaging. 2012;28(Suppl 1):69\u201375.","journal-title":"Int J Cardiovasc Imaging"},{"key":"84_CR12","doi-asserted-by":"publisher","first-page":"230","DOI":"10.4329\/wjr.v6.i6.230","volume":"6","author":"L Cardinale","year":"2014","unstructured":"Cardinale L, Priola AM, Moretti F, Volpicelli G. Effectiveness of chest radiography, lung ultrasound and thoracic computed tomography in the diagnosis of congestive heart failure. World J Radiol. 2014;6:230\u20137.","journal-title":"World J Radiol"},{"key":"84_CR13","first-page":"2015","volume":"8","author":"KL Hua","year":"2015","unstructured":"Hua KL, Hsu CH, Hidayati SC, Cheng WH, Chen YJ. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther. 2015;8:2015\u201322.","journal-title":"Onco Targets Ther"},{"key":"84_CR14","doi-asserted-by":"crossref","unstructured":"Ausawalaithong W, Thirach A, Marukatat S, Wilaiprasitporn T.\u00a0Automatic lung cancer prediction from chest x-ray images using the deep learning approach.\u00a02018 11th Biomedical Engineering International Conference (BMEiCON); 2018 Nov 21\u201324; Chiang Mai, Thailand. IEEE; 2018. p. 1\u20135.","DOI":"10.1109\/BMEiCON.2018.8609997"},{"key":"84_CR15","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1148\/radiol.2017162326","volume":"284","author":"P Lakhani","year":"2017","unstructured":"Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284:574\u201382.","journal-title":"Radiology"},{"key":"84_CR16","doi-asserted-by":"publisher","unstructured":"Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning [Preprint]. arXiv:1711.05225; [posted 2017 Nov 14]. https:\/\/doi.org\/10.48550\/arXiv.1711.05225.","DOI":"10.48550\/arXiv.1711.05225"},{"key":"84_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-76359-w","volume":"10","author":"K Kusunose","year":"2020","unstructured":"Kusunose K, Hirata Y, Tsuji T, Kotoku J, Sata M. Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest x ray. Sci Rep. 2020;10:19311.","journal-title":"Sci Rep"},{"key":"84_CR18","doi-asserted-by":"publisher","DOI":"10.1093\/radadv\/umae003","volume":"1","author":"VV Danilov","year":"2024","unstructured":"Danilov VV, Makoveev AO, Proutski A, Ryndova I, Karpovsky A, Gankin Y. Explainable AI to identify radiographic features of pulmonary edema. Radiology Advances. 2024;1:umae003.","journal-title":"Radiology Advances"},{"key":"84_CR19","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1001\/jamacardio.2019.5620","volume":"5","author":"S Toba","year":"2020","unstructured":"Toba S, Mitani Y, Yodoya N, Ohashi H, Sawada H, Hayakawa H, et al. Prediction of pulmonary to systemic flow ratio in patients with congenital heart disease using deep learning-based analysis of chest radiographs. JAMA Cardiol. 2020;5:449\u201357.","journal-title":"JAMA Cardiol"},{"key":"84_CR20","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1148\/72.5.689","volume":"72","author":"DC Arnois","year":"1959","unstructured":"Arnois DC, Silverman FN, Turner ME. The radiographic evaluation of pulmonary vasculature in children with congenital cardiovascular disease. Radiology. 1959;72:689\u201398.","journal-title":"Radiology"},{"key":"84_CR21","volume-title":"Chest radiographic interpretation in pediatric cardiac patients","year":"2010","unstructured":"Yoo SJ, MacDonald C, Babyn P, editors. Chest radiographic interpretation in pediatric cardiac patients. Thieme; 2010."},{"key":"84_CR22","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1148\/109.3.649","volume":"109","author":"AM Coussement","year":"1973","unstructured":"Coussement AM, Gooding CA. Objective radiographic assessment of pulmonary vascularity in children. Radiology. 1973;109:649\u201354.","journal-title":"Radiology"},{"key":"84_CR23","doi-asserted-by":"publisher","unstructured":"Khalil M, Khalil A, Ngom A. A comprehensive study of vision transformers in image classification tasks [Preprint]. arXiv:2312.01232; [posted 2023 Dec 2]. https:\/\/doi.org\/10.48550\/arXiv.2312.01232.","DOI":"10.48550\/arXiv.2312.01232"},{"key":"84_CR24","doi-asserted-by":"crossref","unstructured":"Wanto A, Yuhandri Y, Okfalisa O.\u00a0Optimization accuracy of CNN model by utilizing CLAHE parameters in image classification problems.\u00a02023 International Conference on Networking, Electrical Engineering, Computer Science, and Technology (IConNECT); 2023 Aug 25\u201326; Bandar Lampung, Indonesia. IEEE; 2023. p. 253\u20138.","DOI":"10.1109\/IConNECT56593.2023.10327100"},{"key":"84_CR25","doi-asserted-by":"publisher","first-page":"5481","DOI":"10.5194\/gmd-15-5481-2022","volume":"15","author":"TO Hodson","year":"2022","unstructured":"Hodson TO. Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geosci Model Dev. 2022;15:5481\u20137.","journal-title":"Geosci Model Dev"},{"key":"84_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00492-0","volume":"8","author":"C Shorten","year":"2021","unstructured":"Shorten C, Khoshgoftaar TM, Furht B. Text data augmentation for deep learning. J Big Data. 2021;8:101.","journal-title":"J Big Data"},{"key":"84_CR27","unstructured":"SuperSecureHuman. xray-reg. Hugging Face; [ 7 December 2025]. Available from: https:\/\/huggingface.co\/spaces\/SuperSecureHuman\/xray-reg."}],"container-title":["Journal of Cardiovascular Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s44348-026-00084-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s44348-026-00084-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s44348-026-00084-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T23:02:11Z","timestamp":1787266931000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s44348-026-00084-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,21]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["84"],"URL":"https:\/\/doi.org\/10.1186\/s44348-026-00084-7","relation":{},"ISSN":["2586-7210","2586-7296"],"issn-type":[{"value":"2586-7210","type":"print"},{"value":"2586-7296","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,21]]},"assertion":[{"value":"16 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The study was approved by the Institutional Ethics Committee of Amrita Institute of Medical Sciences (No. ECASM-AIMS-2023\u2013555). The waiver for informed consent was granted as the radiographs were anonymized.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"25"}}