{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T08:00:25Z","timestamp":1780300825713,"version":"3.54.0"},"reference-count":36,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Ultrason Imaging"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:p>\n                    Early detection of fetal cardiac diseases can dramatically improve neonatal outcomes by enabling timely intervention and informed clinical management. However, accurate diagnosis remains challenging due to the complexity of fetal heart structures in ultrasound images and the subtlety of congenital anomalies. To address these challenges, this work introduces Fetal Cardiac Disease Detection Using Ultrasound Imaging with Gegenbauer Graph Neural Networks (FCD-UI-GGNN). Ultrasound images are collected from the Fetal Phantom Ultrasound Dataset 23 (FPUS23) and pre-processed using Broad Collaborative Filtering (BCF) to resize images while preserving critical anatomical details. Fast Continual Multi-View Clustering (FCMVC) segments target vessel structures, and Gegenbauer Graph Neural Networks (GGNN) detects cardiac anomalies by modeling both local and global vessel relationships. The network weights are optimized using the Bitterling Fish Optimization Algorithm (BFOA) to improve accuracy. The framework is evaluated utilizing Accuracy, Precision,\n                    <jats:italic toggle=\"yes\">F<\/jats:italic>\n                    1-Score, Recall, and ROC analysis, achieving 98.78% Accuracy, 99.01% Recall, 98.36% Precision, and 99.12%\n                    <jats:italic toggle=\"yes\">F<\/jats:italic>\n                    1-Score. Validation on additional datasets, including FPUS23 and 4D Fetal Cardiac Ultrasound images, confirms robust generalization. These results demonstrate highly reliable and precise detection, supporting early clinical intervention for fetal cardiac anomalies.\n                  <\/jats:p>","DOI":"10.1177\/01617346251390849","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T06:56:42Z","timestamp":1766732202000},"page":"294-306","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Advanced Fetal Cardiac Disease Detection Using Optimized Gegenbauer Graph Neural Networks on Ultrasound Images to Facilitate Early Diagnosis and Clinical Assessment"],"prefix":"10.1177","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3250-8680","authenticated-orcid":false,"given":"Subasree","family":"Santhanaraman","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering(Cyber Security), Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1216-4403","authenticated-orcid":false,"given":"NalliyaGounder Kuppuswamy","family":"Sakthivel","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2657-8700","authenticated-orcid":false,"given":"Amit Kumar","family":"Tyagi","sequence":"additional","affiliation":[{"name":"Department of Fashion Technology, National Institute of Fashion Technology, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tharani","family":"Balamurugan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,12,25]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-025-01409-6"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3413681"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.echo.2023.12.013"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3553548"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3561848"},{"key":"e_1_3_2_7_2","article-title":"Automatic segmentation based on optimization U-net neural network (OU-netnn) for fetal cardiac ultrasound images","volume":"17","author":"Sekar S","year":"2024","unstructured":"Sekar S, Rufus HA. Automatic segmentation based on optimization U-net neural network (OU-netnn) for fetal cardiac ultrasound images. Int J Inf Technol. 2024;17:2477-84.","journal-title":"Int J Inf Technol"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781003493570-3"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107741"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.talanta.2024.126109"},{"key":"e_1_3_2_11_2","volume-title":"Artificial Intelligence in Congenital Heart Disease","author":"Toscano A","year":"2024","unstructured":"Toscano A, Moras P. Intelligence-based cardiology and Cardiac Surgery. In: Artificial Intelligence in Congenital Heart Disease. Cambridge, MA: Academic Press. 2024, pp.279-84."},{"key":"e_1_3_2_12_2","first-page":"100322","article-title":"An ensemble-based stage-prediction machine learning approach for classifying fetal disease","volume":"5","author":"Dash D","year":"2024","unstructured":"Dash D, Kumar M. An ensemble-based stage-prediction machine learning approach for classifying fetal disease. Heal Anal. 2024;5:100322.","journal-title":"Heal Anal"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-443-15688-5.00038-3"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3390688"},{"issue":"3","key":"e_1_3_2_15_2","article-title":"An improved ensemble model of hyper parameter tuned ML algorithms for fetal health prediction","volume":"16","author":"Talukder MSH","year":"2024","unstructured":"Talukder MSH, Akter S. An improved ensemble model of hyper parameter tuned ML algorithms for fetal health prediction. Int J Inf Technol. 2024;16(3):1831-40.","journal-title":"Int J Inf Technol"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2023.3348800"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-023-10055-6"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acvd.2023.10.127"},{"key":"e_1_3_2_19_2","volume-title":"Artificial Intelligence in Three-Dimensional and Fetal Echocardiography. InIntelligence-Based Cardiology and Cardiac Surgery","author":"Jone PN","year":"2024","unstructured":"Jone PN. Artificial Intelligence in Three-Dimensional and Fetal Echocardiography. InIntelligence-Based Cardiology and Cardiac Surgery. Cambridge, MA: Academic Press. 2024, pp.211-5."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42835-023-01682-x"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1002\/uog.27608"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108275"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12354"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12884-024-06916-y"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1002\/uog.27503"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107922"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3426068"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3370507"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3364755"},{"key":"e_1_3_2_30_2","first-page":"1847981","article-title":"Emerging feature extraction techniques for machine learning-based classification of carotid artery ultrasound images","volume":"1","author":"Latha S","year":"2022","unstructured":"Latha S, Muthu P, Dhanalakshmi S, Kumar R, Lai KW, Wu X. Emerging feature extraction techniques for machine learning-based classification of carotid artery ultrasound images. Comput Intell Neurosci. 2022;1:1847981.","journal-title":"Comput Intell Neurosci"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jare.2024.03.016"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3284315"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2024.3378599"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3388974"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3381069"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-18579-0"},{"key":"e_1_3_2_37_2","article-title":"Ultrasound Fetus Dataset","volume":"1","author":"Anitha A.","year":"2024","unstructured":"Anitha A. Ultrasound Fetus Dataset. 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