{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T02:48:32Z","timestamp":1782874112902,"version":"3.54.5"},"reference-count":104,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis. Comput. Ind. Biomed. Art"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.<\/jats:p>","DOI":"10.1186\/s42492-023-00147-2","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T03:24:43Z","timestamp":1697167483000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Application and prospects of AI-based radiomics in ultrasound diagnosis"],"prefix":"10.1186","volume":"6","author":[{"given":"Haoyan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheling","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyu","family":"Ru","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaqing","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2513-768X","authenticated-orcid":false,"given":"Kun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,13]]},"reference":[{"key":"147_CR1","doi-asserted-by":"publisher","unstructured":"Ma LF, Wang R, He Q, Huang LJ, Wei XY, Lu X et al (2022) Artificial intelligence-based ultrasound imaging technologies for hepatic diseases. iLIVER 1(4):252\u2013264. https:\/\/doi.org\/10.1016\/j.iliver.2022.11.001","DOI":"10.1016\/j.iliver.2022.11.001"},{"key":"147_CR2","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005","journal-title":"Med Image Anal"},{"issue":"3","key":"147_CR3","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","volume":"31","author":"C Janiesch","year":"2021","unstructured":"Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Markets 31(3):685\u2013695. https:\/\/doi.org\/10.1007\/s12525-021-00475-2","journal-title":"Electron Markets"},{"issue":"7553","key":"147_CR4","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"147_CR5","doi-asserted-by":"publisher","unstructured":"Ozsahin I, Sekeroglu B, Musa MS, Mustapha MT, Ozsahin DU (2020) Review on diagnosis of COVID-19 from chest CT images using artificial intelligence. Comput Math Methods Med 2020:9756518. https:\/\/doi.org\/10.1155\/2020\/9756518","DOI":"10.1155\/2020\/9756518"},{"issue":"6","key":"147_CR6","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.jcct.2021.03.006","volume":"15","author":"A Lin","year":"2021","unstructured":"Lin A, Kolossv\u00e1ry M, Motwani M, I\u0161gum I, Maurovich-Horvat P, Slomka PJ et al (2021) Artificial intelligence in cardiovascular CT: Current status and future implications. J Cardiovasc Comput Tomogr 15(6):462\u2013469. https:\/\/doi.org\/10.1016\/j.jcct.2021.03.006","journal-title":"J Cardiovasc Comput Tomogr"},{"issue":"12","key":"147_CR7","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.diii.2020.10.004","volume":"101","author":"D Blanc","year":"2020","unstructured":"Blanc D, Racine V, Khalil A, Deloche M, Broyelle JA, Hammouamri I et al (2020) Artificial intelligence solution to classify pulmonary nodules on CT. Diagn Interventional Imaging 101(12):803\u2013810. https:\/\/doi.org\/10.1016\/j.diii.2020.10.004","journal-title":"Diagn Interventional Imaging"},{"issue":"5","key":"147_CR8","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1002\/jmri.26878","volume":"51","author":"D Sheth","year":"2020","unstructured":"Sheth D, Giger ML (2020) Artificial intelligence in the interpretation of breast cancer on MRI. J Magn Reson Imaging: JMRI 51(5):1310\u20131324. https:\/\/doi.org\/10.1002\/jmri.26878","journal-title":"J Magn Reson Imaging: JMRI"},{"issue":"1","key":"147_CR9","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1148\/radiol.2020200292","volume":"298","author":"YL Jiang","year":"2021","unstructured":"Jiang YL, Edwards AV, Newstead GM (2021) Artificial intelligence applied to breast MRI for improved diagnosis. Radiology 298(1):38\u201346. https:\/\/doi.org\/10.1148\/radiol.2020200292","journal-title":"Radiology"},{"issue":"2","key":"147_CR10","doi-asserted-by":"publisher","first-page":"280","DOI":"10.2214\/AJR.18.20389","volume":"212","author":"M Codari","year":"2019","unstructured":"Codari M, Schiaffino S, Sardanelli F, Trimboli RM (2019) Artificial intelligence for breast MRI in 2008-2018: a systematic mapping review. AJR Am J Roentgenol 212(2):280\u2013292. https:\/\/doi.org\/10.2214\/AJR.18.20389","journal-title":"AJR Am J Roentgenol"},{"issue":"3","key":"147_CR11","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1148\/radiol.2020190283","volume":"295","author":"AM Rauschecker","year":"2020","unstructured":"Rauschecker AM, Rudie JD, Xie L, Wang JC, Duong MT, Botzolakis EJ et al (2020) Artificial intelligence system approaching neuroradiologist-level differential diagnosis accuracy at brain MRI. Radiology 295(3):626\u2013637. https:\/\/doi.org\/10.1148\/radiol.2020190283","journal-title":"Radiology"},{"issue":"9","key":"147_CR12","doi-asserted-by":"publisher","first-page":"2050","DOI":"10.1111\/liv.14555","volume":"40","author":"JW Wei","year":"2020","unstructured":"Wei JW, Jiang HY, Gu DS, Niu M, Fu FF, Han YQ et al (2020) Radiomics in liver diseases: Current progress and future opportunities. Liver Int 40(9):2050\u20132063. https:\/\/doi.org\/10.1111\/liv.14555","journal-title":"Liver Int"},{"issue":"2","key":"147_CR13","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1093\/gastro\/goaa011","volume":"8","author":"WM Hu","year":"2020","unstructured":"Hu WM, Yang HY, Xu HF, Mao YL (2020) Radiomics based on artificial intelligence in liver diseases: where are we?. Gastroenterol Rep 8(2):90\u201397. https:\/\/doi.org\/10.1093\/gastro\/goaa011","journal-title":"Gastroenterol Rep"},{"issue":"3","key":"147_CR14","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/S1665-2681(19)31268-2","volume":"14","author":"M Colombo","year":"2015","unstructured":"Colombo M (2015) Diagnosis of liver nodules within and outside screening programs. Ann Hepatol 14(3):304\u2013309","journal-title":"Ann Hepatol"},{"issue":"2","key":"147_CR15","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1055\/s-2007-1001882","volume":"11","author":"SE Smith","year":"2007","unstructured":"Smith SE, Salanitri J, Lisle D (2007) Ultrasound evaluation of soft tissue masses and fluid collections. Semin Musculoskelet Radiol 11(2):174\u2013191. https:\/\/doi.org\/10.1055\/s-2007-1001882","journal-title":"Semin Musculoskelet Radiol"},{"issue":"2","key":"147_CR16","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1243\/0954411991534834","volume":"213","author":"T Jansson","year":"1999","unstructured":"Jansson T, Persson HW, Lindstr\u00f6m K (1999) Estimation of blood perfusion using ultrasound. Proc Inst Mech Eng, Part H: J Eng Med 213(2):91\u2013106. https:\/\/doi.org\/10.1243\/0954411991534834","journal-title":"Proc Inst Mech Eng, Part H: J Eng Med"},{"issue":"5","key":"147_CR17","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.7150\/thno.18650","volume":"7","author":"RMS Sigrist","year":"2017","unstructured":"Sigrist RMS, Liau J, Kaffas AE, Chammas MC, Willmann JK (2017) Ultrasound elastography: review of techniques and clinical applications. Theranostics 7(5):1303\u20131329. https:\/\/doi.org\/10.7150\/thno.18650","journal-title":"Theranostics"},{"issue":"8","key":"147_CR18","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1007\/s00330-007-0623-0","volume":"17","author":"E Quaia","year":"2007","unstructured":"Quaia E (2007) Microbubble ultrasound contrast agents: an update. Eur Radiol 17(8):1995\u20132008. https:\/\/doi.org\/10.1007\/s00330-007-0623-0","journal-title":"Eur Radiol"},{"issue":"1","key":"147_CR19","doi-asserted-by":"publisher","first-page":"20474","DOI":"10.1038\/s41598-021-00018-x","volume":"11","author":"H Ye","year":"2021","unstructured":"Ye H, Hang J, Zhang MM, Chen XW, Ye XH, Chen J et al (2021) Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network. Sci Rep 11(1):20474. https:\/\/doi.org\/10.1038\/s41598-021-00018-x","journal-title":"Sci Rep"},{"issue":"6","key":"147_CR20","doi-asserted-by":"publisher","first-page":"1989","DOI":"10.21037\/gs-21-315","volume":"10","author":"WJ Zhou","year":"2021","unstructured":"Zhou WJ, Zhang YD, Kong WT, Zhang CX, Zhang B (2021) Preoperative prediction of axillary lymph node metastasis in patients with breast cancer based on radiomics of gray-scale ultrasonography. Gland Surg 10(6):1989\u20132001. https:\/\/doi.org\/10.21037\/gs-21-315","journal-title":"Gland Surg"},{"issue":"7","key":"147_CR21","doi-asserted-by":"publisher","first-page":"2156","DOI":"10.3390\/jcm9072156","volume":"9","author":"MR Kwon","year":"2020","unstructured":"Kwon MR, Shin JH, Park H, Cho H, Kim E, Hahn SY (2020) Radiomics based on thyroid ultrasound can predict distant metastasis of follicular thyroid carcinoma. J Clin Med 9(7):2156. https:\/\/doi.org\/10.3390\/jcm9072156","journal-title":"J Clin Med"},{"issue":"4\u20135","key":"147_CR22","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1177\/0161734620951216","volume":"42","author":"NH Meshram","year":"2020","unstructured":"Meshram NH, Mitchell CC, Wilbrand S, Dempsey RJ, Varghese T (2020) Deep learning for carotid plaque segmentation using a dilated U-net architecture. Ultrason Imaging 42(4\u20135):221\u2013230. https:\/\/doi.org\/10.1177\/0161734620951216","journal-title":"Ultrason Imaging"},{"issue":"4","key":"147_CR23","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1136\/gutjnl-2018-316204","volume":"68","author":"K Wang","year":"2019","unstructured":"Wang K, Lu X, Zhou H, Gao YY, Zheng J, Tong MH et al (2019) Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut 68(4):729\u2013741. https:\/\/doi.org\/10.1136\/gutjnl-2018-316204","journal-title":"Gut"},{"issue":"6","key":"147_CR24","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1177\/01617346211035315","volume":"43","author":"A Tahmasebi","year":"2021","unstructured":"Tahmasebi A, Qu EZ, Sevrukov A, Liu JB, Wang S, Lyshchik A et al (2021) Assessment of axillary lymph nodes for metastasis on ultrasound using artificial intelligence. Ultrason Imaging 43(6):329\u2013336. https:\/\/doi.org\/10.1177\/01617346211035315","journal-title":"Ultrason Imaging"},{"issue":"11","key":"147_CR25","doi-asserted-by":"publisher","first-page":"8743","DOI":"10.1007\/s00330-021-07934-6","volume":"31","author":"X Lu","year":"2021","unstructured":"Lu X, Zhou H, Wang K, Jin JY, Meng FK, Mu XJ et al (2021) Comparing radiomics models with different inputs for accurate diagnosis of significant fibrosis in chronic liver disease. Eur Radiol 31(11):8743\u20138754. https:\/\/doi.org\/10.1007\/s00330-021-07934-6","journal-title":"Eur Radiol"},{"key":"147_CR26","doi-asserted-by":"crossref","unstructured":"Zhou H, Wang K, Tian J (2020) The accurate non-invasive staging of liver fibrosis using deep learning radiomics based on transfer learning of shear wave elastography. In: Proceedings of the SPIE 11319, medical imaging 2020: ultrasonic imaging and tomography, SPIE, Houston, 16 March 2020","DOI":"10.1117\/12.2549425"},{"issue":"1","key":"147_CR27","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1186\/s12916-022-02258-8","volume":"20","author":"T Tong","year":"2022","unstructured":"Tong T, Gu JH, Xu D, Song L, Zhao QY, Cheng F et al (2022) Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis. BMC Med 20(1):74. https:\/\/doi.org\/10.1186\/s12916-022-02258-8","journal-title":"BMC Med"},{"issue":"9","key":"147_CR28","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1109\/TMI.2021.3078370","volume":"40","author":"C Chen","year":"2021","unstructured":"Chen C, Wang Y, Niu JW, Liu XF, Li QF, Gong XT (2021) Domain knowledge powered deep learning for breast cancer diagnosis based on contrast-enhanced ultrasound videos. IEEE Trans Med Imaging 40(9):2439\u20132451. https:\/\/doi.org\/10.1109\/TMI.2021.3078370","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"147_CR29","doi-asserted-by":"publisher","first-page":"2365","DOI":"10.1007\/s00330-019-06553-6","volume":"30","author":"D Liu","year":"2020","unstructured":"Liu D, Liu F, Xie XY, Su LY, Liu M, Xie XH et al (2020) Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound. Eur Radiol 30(4):2365\u20132376. https:\/\/doi.org\/10.1007\/s00330-019-06553-6","journal-title":"Eur Radiol"},{"issue":"4","key":"147_CR30","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1159\/000505694","volume":"9","author":"F Liu","year":"2020","unstructured":"Liu F, Liu D, Wang K, Xie XH, Su LY, Kuang M et al (2020) Deep learning radiomics based on contrast-enhanced ultrasound might optimize curative treatments for very-early or early-stage hepatocellular carcinoma patients. Liver Cancer 9(4):397\u2013413. https:\/\/doi.org\/10.1159\/000505694","journal-title":"Liver Cancer"},{"issue":"3","key":"147_CR31","doi-asserted-by":"publisher","first-page":"3539","DOI":"10.1007\/s11227-021-04002-0","volume":"78","author":"XY Sun","year":"2022","unstructured":"Sun XY, Lu QL (2022) Contrast-enhanced ultrasound in optimization of treatment plans for diabetic nephropathy patients based on deep learning. J Supercomput 78(3):3539\u20133560. https:\/\/doi.org\/10.1007\/s11227-021-04002-0","journal-title":"J Supercomput"},{"key":"147_CR32","doi-asserted-by":"publisher","unstructured":"Meng ZL, Zhu YY, Fan X, Tian J, Nie F, Wang K (2022) CEUSegNet: a cross-modality lesion segmentation network for contrast-enhanced ultrasound. In: Proceedings of the IEEE 19th international symposium on biomedical imaging, IEEE, Kolkata, 28-31 March 2022. https:\/\/doi.org\/10.1109\/ISBI52829.2022.9761594","DOI":"10.1109\/ISBI52829.2022.9761594"},{"issue":"16","key":"147_CR33","doi-asserted-by":"publisher","first-page":"3589","DOI":"10.3390\/jcm10163589","volume":"10","author":"Y Iwasa","year":"2021","unstructured":"Iwasa Y, Iwashita T, Takeuchi Y, Ichikawa H, Mita N, Uemura S et al (2021) Automatic segmentation of pancreatic tumors using deep learning on a video image of contrast-enhanced endoscopic ultrasound. J Clin Med 10(16):3589. https:\/\/doi.org\/10.3390\/jcm10163589","journal-title":"J Clin Med"},{"key":"147_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.medengphy.2018.12.005","volume":"64","author":"Q Zhang","year":"2019","unstructured":"Zhang Q, Song S, Xiao Y, Chen S, Shi J, Zheng HR (2019) Dual-mode artificially-intelligent diagnosis of breast tumours in shear-wave elastography and B-mode ultrasound using deep polynomial networks. Med Eng Phys 64:1\u20136. https:\/\/doi.org\/10.1016\/j.medengphy.2018.12.005","journal-title":"Med Eng Phys"},{"key":"147_CR35","doi-asserted-by":"publisher","first-page":"109781","DOI":"10.1016\/j.ejrad.2021.109781","volume":"141","author":"M Jiang","year":"2021","unstructured":"Jiang M, Li CL, Chen RX, Tang SC, Lv WZ, Luo XM et al (2021) Management of breast lesions seen on US images: dual-model radiomics including shear-wave elastography may match performance of expert radiologists. Eur J Radiol 141:109781. https:\/\/doi.org\/10.1016\/j.ejrad.2021.109781","journal-title":"Eur J Radiol"},{"issue":"1","key":"147_CR36","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1109\/TUFFC.2021.3119251","volume":"69","author":"S Misra","year":"2022","unstructured":"Misra S, Jeon S, Managuli R, Lee S, Kim G, Yoon C et al (2022) Bi-modal transfer learning for classifying breast cancers via combined B-mode and ultrasound strain imaging. IEEE Trans Ultrason, Ferroelectr, Freq Control 69(1):222\u2013232. https:\/\/doi.org\/10.1109\/TUFFC.2021.3119251","journal-title":"IEEE Trans Ultrason, Ferroelectr, Freq Control"},{"issue":"6","key":"147_CR37","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1038\/s41551-021-00711-2","volume":"5","author":"XJ Qian","year":"2021","unstructured":"Qian XJ, Pei J, Zheng H, Xie XX, Yan L, Zhang H et al (2021) Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning. Nat Biomed Eng 5(6):522\u2013532. https:\/\/doi.org\/10.1038\/s41551-021-00711-2","journal-title":"Nat Biomed Eng"},{"issue":"12","key":"147_CR38","doi-asserted-by":"publisher","first-page":"742","DOI":"10.21037\/atm-19-4630","volume":"8","author":"Y Chen","year":"2020","unstructured":"Chen Y, Jiang JW, Shi J, Chang WY, Shi J, Chen M et al (2020) Dual-mode ultrasound radiomics and intrinsic imaging phenotypes for diagnosis of lymph node lesions. Ann Transl Med 8(12):742. https:\/\/doi.org\/10.21037\/atm-19-4630","journal-title":"Ann Transl Med"},{"key":"147_CR39","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.ejrad.2017.07.027","volume":"95","author":"Q Zhang","year":"2017","unstructured":"Zhang Q, Suo JF, Chang WY, Shi J, Chen M (2017) Dual-modal computer-assisted evaluation of axillary lymph node metastasis in breast cancer patients on both real-time elastography and B-mode ultrasound. Eur J Radiol 95:66\u201374. https:\/\/doi.org\/10.1016\/j.ejrad.2017.07.027","journal-title":"Eur J Radiol"},{"issue":"1","key":"147_CR40","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1186\/s12916-022-02469-z","volume":"20","author":"YY Zhu","year":"2022","unstructured":"Zhu YY, Meng ZL, Fan X, Duan Y, Jia YY, Dong TT et al (2022) Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy. BMC Med 20(1):269. https:\/\/doi.org\/10.1186\/s12916-022-02469-z","journal-title":"BMC Med"},{"issue":"5","key":"147_CR41","doi-asserted-by":"publisher","first-page":"2973","DOI":"10.1007\/s00330-019-06595-w","volume":"30","author":"LY Xue","year":"2020","unstructured":"Xue LY, Jiang ZY, Fu TT, Wang QM, Zhu YL, Dai M et al (2020) Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis. Eur Radiol 30(5):2973\u20132983. https:\/\/doi.org\/10.1007\/s00330-019-06595-w","journal-title":"Eur Radiol"},{"issue":"1","key":"147_CR42","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1186\/s12885-018-5003-4","volume":"18","author":"Z Yao","year":"2018","unstructured":"Yao Z, Dong Y, Wu GQ, Zhang Q, Yang DH, Yu JH et al (2018) Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images. BMC Cancer 18(1):1089. https:\/\/doi.org\/10.1186\/s12885-018-5003-4","journal-title":"BMC Cancer"},{"key":"147_CR43","doi-asserted-by":"publisher","first-page":"1012724","DOI":"10.3389\/fonc.2022.1012724","volume":"12","author":"Y Tao","year":"2022","unstructured":"Tao Y, Yu YY, Wu T, Xu XL, Dai Q, Kong HQ et al (2022) Deep learning for the diagnosis of suspicious thyroid nodules based on multimodal ultrasound images. Front Oncol 12:1012724. https:\/\/doi.org\/10.3389\/fonc.2022.1012724","journal-title":"Front Oncol"},{"issue":"1","key":"147_CR44","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/s12880-023-00982-y","volume":"23","author":"HX Yuan","year":"2023","unstructured":"Yuan HX, Wang CY, Tang CY, You QQ, Zhang Q, Wang WP (2023) Differential diagnosis of gallbladder neoplastic polyps and cholesterol polyps with radiomics of dual modal ultrasound: a pilot study. BMC Med Imaging 23(1):26. https:\/\/doi.org\/10.1186\/s12880-023-00982-y","journal-title":"BMC Med Imaging"},{"key":"147_CR45","doi-asserted-by":"publisher","first-page":"110281","DOI":"10.1016\/j.ejrad.2022.110281","volume":"151","author":"X Zhong","year":"2022","unstructured":"Zhong X, Peng JY, Xie YH, Shi YF, Long HY, Su LY et al (2022) A nomogram based on multi-modal ultrasound for prediction of microvascular invasion and recurrence of hepatocellular carcinoma. Eur J Radiol 151:110281. https:\/\/doi.org\/10.1016\/j.ejrad.2022.110281","journal-title":"Eur J Radiol"},{"key":"147_CR46","doi-asserted-by":"publisher","first-page":"106164","DOI":"10.1016\/j.compbiomed.2022.106164","volume":"150","author":"Z Xiang","year":"2022","unstructured":"Xiang Z, Zhuo QL, Zhao C, Deng XF, Zhu T, Wang TF et al (2022) Self-supervised multi-modal fusion network for multi-modal thyroid ultrasound image diagnosis. Comput Biol Med 150:106164. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106164","journal-title":"Comput Biol Med"},{"key":"147_CR47","doi-asserted-by":"publisher","first-page":"102137","DOI":"10.1016\/j.media.2021.102137","volume":"72","author":"RB Huang","year":"2021","unstructured":"Huang RB, Lin ZH, Dou HR, Wang J, Miao JZ, Zhou GQ et al (2021) AW3M: An auto-weighting and recovery framework for breast cancer diagnosis using multi-modal ultrasound. Med Image Anal 72:102137. https:\/\/doi.org\/10.1016\/j.media.2021.102137","journal-title":"Med Image Anal"},{"issue":"4","key":"147_CR48","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1109\/TMI.2022.3222541","volume":"42","author":"ZL Meng","year":"2023","unstructured":"Meng ZL, Zhu YY, Pang WJ, Tian J, Nie F, Wang K (2023) MSMFN: an ultrasound based multi-step modality fusion network for identifying the histologic subtypes of metastatic cervical lymphadenopathy. IEEE Trans Med Imaging 42(4):996\u20131008. https:\/\/doi.org\/10.1109\/TMI.2022.3222541","journal-title":"IEEE Trans Med Imaging"},{"key":"147_CR49","doi-asserted-by":"publisher","first-page":"109947","DOI":"10.1016\/j.asoc.2022.109947","volume":"133","author":"Y Gao","year":"2023","unstructured":"Gao Y, Fu XL, Chen YP, Guo CY, Wu J (2023) Post-pandemic healthcare for COVID-19 vaccine: Tissue-aware diagnosis of cervical lymphadenopathy via multi-modal ultrasound semantic segmentation. Appl Soft Comput 133:109947. https:\/\/doi.org\/10.1016\/j.asoc.2022.109947","journal-title":"Appl Soft Comput"},{"issue":"5","key":"147_CR50","doi-asserted-by":"publisher","first-page":"2520","DOI":"10.3390\/s23052520","volume":"23","author":"DA Mitrea","year":"2023","unstructured":"Mitrea DA, Brehar R, Nedevschi S, Lupsor-Platon M, Socaciu M, Badea R (2023) Hepatocellular carcinoma recognition from ultrasound images using combinations of conventional and deep learning techniques. Sensors 23(5):2520. https:\/\/doi.org\/10.3390\/s23052520","journal-title":"Sensors"},{"key":"147_CR51","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.clinimag.2022.03.005","volume":"86","author":"GY Yang","year":"2022","unstructured":"Yang GY, Zhang Y, Yu TZ, Chen MH, Chen PJ (2022) Exploratory study on the predictive value of ultrasound radiomics for cervical tuberculous lymphadenitis. Clin Imaging 86:61\u201366. https:\/\/doi.org\/10.1016\/j.clinimag.2022.03.005","journal-title":"Clin Imaging"},{"issue":"4","key":"147_CR52","doi-asserted-by":"publisher","first-page":"700","DOI":"10.3174\/ajnr.A6505","volume":"41","author":"MR Kwon","year":"2020","unstructured":"Kwon MR, Shin JH, Park H, Cho H, Hahn SY, Park KW (2020) Radiomics study of thyroid ultrasound for predicting BRAF mutation in papillary thyroid carcinoma: preliminary results. Am J Neuroradiol 41(4):700\u2013705. https:\/\/doi.org\/10.3174\/ajnr.A6505","journal-title":"Am J Neuroradiol"},{"issue":"11","key":"147_CR53","doi-asserted-by":"publisher","first-page":"2312","DOI":"10.1109\/TUFFC.2020.3002249","volume":"67","author":"C Baloescu","year":"2020","unstructured":"Baloescu C, Toporek G, Kim S, McNamara K, Liu R, Shaw MM et al (2020) Automated lung ultrasound B-line assessment using a deep learning algorithm. IEEE Trans Ultrason, Ferroelectr, Freq Control 67(11):2312\u20132320. https:\/\/doi.org\/10.1109\/TUFFC.2020.3002249","journal-title":"IEEE Trans Ultrason, Ferroelectr, Freq Control"},{"issue":"8","key":"147_CR54","doi-asserted-by":"publisher","first-page":"2676","DOI":"10.1109\/TMI.2020.2994459","volume":"39","author":"S Roy","year":"2020","unstructured":"Roy S, Menapace W, Oei S, Luijten B, Fini E, Saltori C et al (2020) Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans Med Imaging 39(8):2676\u20132687. https:\/\/doi.org\/10.1109\/TMI.2020.2994459","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"147_CR55","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.crad.2021.10.009","volume":"77","author":"JB Peng","year":"2022","unstructured":"Peng JB, Peng YT, Lin P, Wan D, Qin H, Li X et al (2022) Differentiating infected focal liver lesions from malignant mimickers: value of ultrasound-based radiomics. Clin Radiol 77(2):104\u2013113. https:\/\/doi.org\/10.1016\/j.crad.2021.10.009","journal-title":"Clin Radiol"},{"key":"147_CR56","doi-asserted-by":"publisher","first-page":"963925","DOI":"10.3389\/fonc.2022.963925","volume":"12","author":"JJ Liu","year":"2022","unstructured":"Liu JJ, Wang XC, Hu MS, Zheng Y, Zhu L, Wang W et al (2022) Development of an ultrasound-based radiomics nomogram to preoperatively predict Ki-67 expression level in patients with breast cancer. Front Oncol 12:963925. https:\/\/doi.org\/10.3389\/fonc.2022.963925","journal-title":"Front Oncol"},{"issue":"3","key":"147_CR57","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/s11517-019-02099-3","volume":"58","author":"PK Jain","year":"2020","unstructured":"Jain PK, Gupta S, Bhavsar A, Nigam A, Sharma N (2020) Localization of common carotid artery transverse section in B-mode ultrasound images using faster RCNN: a deep learning approach. Med Biol Eng Comput 58(3):471\u2013482. https:\/\/doi.org\/10.1007\/s11517-019-02099-3","journal-title":"Med Biol Eng Comput"},{"issue":"3","key":"147_CR58","doi-asserted-by":"publisher","first-page":"e210110","DOI":"10.1148\/ryai.210110","volume":"4","author":"H Dadoun","year":"2022","unstructured":"Dadoun H, Rousseau AL, de Kerviler E, Correas JM, Tissier AM, Joujou F et al (2022) Deep learning for the detection, localization, and characterization of focal liver lesions on abdominal US images. Radiol: Artif Intell 4(3):e210110. https:\/\/doi.org\/10.1148\/ryai.210110","journal-title":"Radiol: Artif Intell"},{"issue":"1","key":"147_CR59","doi-asserted-by":"publisher","first-page":"75","DOI":"10.3233\/XST-200775","volume":"29","author":"L Zhang","year":"2021","unstructured":"Zhang L, Zhuang Y, Hua Z, Han L, Li C, Chen K et al (2021) Automated location of thyroid nodules in ultrasound images with improved YOLOV3 network. J X-ray Sci Technol 29(1):75\u201390. https:\/\/doi.org\/10.3233\/XST-200775","journal-title":"J X-ray Sci Technol"},{"issue":"9","key":"147_CR60","doi-asserted-by":"publisher","first-page":"2723","DOI":"10.1016\/j.ultrasmedbio.2021.05.023","volume":"47","author":"R Zhou","year":"2021","unstructured":"Zhou R, Azarpazhooh MR, Spence JD, Hashemi S, Ma W, Cheng XY et al (2021) Deep learning-based carotid plaque segmentation from B-mode ultrasound images. Ultrasound Med Biol 47(9):2723\u20132733. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2021.05.023","journal-title":"Ultrasound Med Biol"},{"key":"147_CR61","doi-asserted-by":"publisher","first-page":"104721","DOI":"10.1016\/j.compbiomed.2021.104721","volume":"136","author":"PK Jain","year":"2021","unstructured":"Jain PK, Sharma N, Giannopoulos AA, Saba L, Nicolaides A, Suri JS (2021) Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound. Comput Biol Med 136:104721. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104721","journal-title":"Comput Biol Med"},{"issue":"4","key":"147_CR62","doi-asserted-by":"publisher","first-page":"729","DOI":"10.3390\/healthcare10040729","volume":"10","author":"YT Zhang","year":"2022","unstructured":"Zhang YT, Xian M, Cheng HD, Shareef B, Ding JR, Xu F et al (2022) BUSIS: a benchmark for breast ultrasound image segmentation. Healthcare 10(4):729. https:\/\/doi.org\/10.3390\/healthcare10040729","journal-title":"Healthcare"},{"issue":"4","key":"147_CR63","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1109\/JBHI.2019.2960821","volume":"24","author":"WX Liao","year":"2020","unstructured":"Liao WX, He P, Hao J, Wang XY, Yang RL, An D et al (2020) Automatic identification of breast ultrasound image based on supervised block-based region segmentation algorithm and features combination migration deep learning model. IEEE J Biomed Health Inf 24(4):984\u2013993. https:\/\/doi.org\/10.1109\/JBHI.2019.2960821","journal-title":"IEEE J Biomed Health Inf"},{"key":"147_CR64","doi-asserted-by":"publisher","first-page":"63482","DOI":"10.1109\/access.2020.2982390","volume":"8","author":"V Kumar","year":"2020","unstructured":"Kumar V, Webb J, Gregory A, Meixner DD, Knudsen JM, Callstrom M et al (2020) Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. IEEE Access 8:63482\u201363496. https:\/\/doi.org\/10.1109\/access.2020.2982390","journal-title":"IEEE Access"},{"issue":"15","key":"147_CR65","doi-asserted-by":"publisher","first-page":"4175","DOI":"10.3390\/s20154175","volume":"20","author":"Z Akkus","year":"2020","unstructured":"Akkus Z, Kim BH, Nayak R, Gregory A, Alizad A, Fatemi M (2020) Fully automated segmentation of bladder sac and measurement of detrusor wall thickness from transabdominal ultrasound images. Sensors 20(15):4175. https:\/\/doi.org\/10.3390\/s20154175","journal-title":"Sensors"},{"key":"147_CR66","doi-asserted-by":"publisher","first-page":"106017","DOI":"10.1016\/j.compbiomed.2022.106017","volume":"149","author":"PK Jain","year":"2022","unstructured":"Jain PK, Sharma N, Kalra MK, Johri A, Saba L, Suri JS (2022) Far wall plaque segmentation and area measurement in common and internal carotid artery ultrasound using U-series architectures: An unseen Artificial Intelligence paradigm for stroke risk assessment. Comput Biol Med 149:106017. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106017","journal-title":"Comput Biol Med"},{"issue":"8","key":"147_CR67","doi-asserted-by":"publisher","first-page":"2967","DOI":"10.1109\/JBHI.2021.3060163","volume":"25","author":"R Zhou","year":"2021","unstructured":"Zhou R, Guo FM, Azarpazhooh MR, Hashemi S, Cheng XY, Spence JD et al (2021) Deep learning-based measurement of total plaque area in B-mode ultrasound images. IEEE J Biomed Health Inf 25(8):2967\u20132977. https:\/\/doi.org\/10.1109\/JBHI.2021.3060163","journal-title":"IEEE J Biomed Health Inf"},{"issue":"1","key":"147_CR68","doi-asserted-by":"publisher","first-page":"9","DOI":"10.23736\/S0392-9590.21.04771-4","volume":"41","author":"PK Jain","year":"2022","unstructured":"Jain PK, Sharma N, Saba L, Paraskevas KI, Kalra MK, Johri A et al (2022) Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study. Int Angiol 41(1):9\u201323. https:\/\/doi.org\/10.23736\/S0392-9590.21.04771-4","journal-title":"Int Angiol"},{"key":"147_CR69","doi-asserted-by":"publisher","first-page":"101784","DOI":"10.1016\/j.artmed.2019.101784","volume":"103","author":"M del Mar Vila","year":"2020","unstructured":"del Mar Vila M, Remeseiro B, Grau M, Elosua R, Betriu \u00c0, Fernandez-Giraldez E et al (2020) Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation. Artif Intell Med 103:101784. https:\/\/doi.org\/10.1016\/j.artmed.2019.101784","journal-title":"Artif Intell Med"},{"key":"147_CR70","doi-asserted-by":"publisher","first-page":"105333","DOI":"10.1016\/j.compbiomed.2022.105333","volume":"144","author":"KM Meiburger","year":"2022","unstructured":"Meiburger KM, Marzola F, Zahnd G, Faita F, Loizou CP, Lain\u00e9 N et al (2022) Carotid Ultrasound Boundary Study (CUBS): Technical considerations on an open multi-center analysis of computerized measurement systems for intima-media thickness measurement on common carotid artery longitudinal B-mode ultrasound scans. Comput Biol Med 144:105333. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105333","journal-title":"Comput Biol Med"},{"issue":"162","key":"147_CR71","doi-asserted-by":"publisher","first-page":"20190715","DOI":"10.1098\/rsif.2019.0715","volume":"17","author":"RJ Cunningham","year":"2020","unstructured":"Cunningham RJ, Loram ID (2020) Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks. J R Soc, Interface 17(162):20190715. https:\/\/doi.org\/10.1098\/rsif.2019.0715","journal-title":"J R Soc, Interface"},{"issue":"7","key":"147_CR72","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1007\/s11548-022-02636-w","volume":"17","author":"T Leblanc","year":"2022","unstructured":"Leblanc T, Lalys F, Tollenaere Q, Kaladji A, Lucas A, Simon A (2022) Stretched reconstruction based on 2D freehand ultrasound for peripheral artery imaging. Int J Comput Assisted Radiol Surg 17(7):1281\u20131288. https:\/\/doi.org\/10.1007\/s11548-022-02636-w","journal-title":"Int J Comput Assisted Radiol Surg"},{"key":"147_CR73","doi-asserted-by":"publisher","unstructured":"Tang SY, Yang X, Shajudeen P, Sears C, Taraballi F, Weiner B et al (2021) A CNN-based method to reconstruct 3-D spine surfaces from US images in vivo. Med Image Anal 74:102221. https:\/\/doi.org\/10.1016\/j.media.2021.102221","DOI":"10.1016\/j.media.2021.102221"},{"key":"147_CR74","doi-asserted-by":"crossref","unstructured":"Ahn SS, Ta K, Lu A, Stendahl JC, Sinusas AJ, Duncan JS (2020) Unsupervised motion tracking of left ventricle in echocardiography. In: Proceedings of the SPIE 11319, medical imaging 2020: ultrasonic imaging and tomography, SPIE, Houston, 16 March 2020","DOI":"10.1117\/12.2549572"},{"issue":"12","key":"147_CR75","doi-asserted-by":"publisher","first-page":"1989","DOI":"10.1007\/s11548-020-02265-1","volume":"15","author":"S Yagasaki","year":"2020","unstructured":"Yagasaki S, Koizumi N, Nishiyama Y, Kondo R, Imaizumi T, Matsumoto N et al (2020) Estimating 3-dimensional liver motion using deep learning and 2-dimensional ultrasound images. Int J Comput Assisted Radiol Surg 15(12):1989\u20131995. https:\/\/doi.org\/10.1007\/s11548-020-02265-1","journal-title":"Int J Comput Assisted Radiol Surg"},{"issue":"12","key":"147_CR76","doi-asserted-by":"publisher","first-page":"2565","DOI":"10.1109\/TUFFC.2020.2976809","volume":"67","author":"E Evain","year":"2020","unstructured":"Evain E, Faraz K, Grenier T, Garcia D, De Craene M, Bernard O (2020) A pilot study on convolutional neural networks for motion estimation from ultrasound images. IEEE Trans Ultrason, Ferroelectr, Freq Control 67(12):2565\u20132573. https:\/\/doi.org\/10.1109\/TUFFC.2020.2976809","journal-title":"IEEE Trans Ultrason, Ferroelectr, Freq Control"},{"key":"147_CR77","doi-asserted-by":"publisher","first-page":"102461","DOI":"10.1016\/j.media.2022.102461","volume":"79","author":"JM Liang","year":"2022","unstructured":"Liang JM, Yang X, Huang YH, Li HM, He SC, Hu XD et al (2022) Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis. Med Image Anal 79:102461. https:\/\/doi.org\/10.1016\/j.media.2022.102461","journal-title":"Med Image Anal"},{"issue":"11","key":"147_CR78","doi-asserted-by":"publisher","first-page":"1674","DOI":"10.3390\/medicina58111674","volume":"58","author":"Q Guo","year":"2022","unstructured":"Guo Q, Dong ZW, Jiang LX, Zhang L, Li ZY, Wang DM (2022) Assessing whether morphological changes in axillary lymph node have already occurred prior to metastasis in breast cancer patients by ultrasound. Medicina 58(11):1674. https:\/\/doi.org\/10.3390\/medicina58111674","journal-title":"Medicina"},{"issue":"4","key":"147_CR79","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1097\/CM9.0000000000001329","volume":"134","author":"TF Yu","year":"2021","unstructured":"Yu TF, He W, Gan CG, Zhao MC, Zhu Q, Zhang W et al (2021) Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study. Chin Med J 134(4):415\u2013424. https:\/\/doi.org\/10.1097\/CM9.0000000000001329","journal-title":"Chin Med J"},{"issue":"1","key":"147_CR80","doi-asserted-by":"publisher","first-page":"5645","DOI":"10.1038\/s41467-021-26023-2","volume":"12","author":"YQ Shen","year":"2021","unstructured":"Shen YQ, Shamout FE, Oliver JR, Witowski J, Kannan K, Park J et al (2021) Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun 12(1):5645. https:\/\/doi.org\/10.1038\/s41467-021-26023-2","journal-title":"Nat Commun"},{"key":"147_CR81","doi-asserted-by":"publisher","unstructured":"Sultan LR, Schultz SM, Cary TW, Sehgal CM (2018) Machine learning to improve breast cancer diagnosis by multimodal ultrasound. In: Proceedings of the 2018 IEEE international ultrasonics symposium, IEEE, Kobe, 22-25 October 2018. https:\/\/doi.org\/10.1109\/ultsym.2018.8579953","DOI":"10.1109\/ultsym.2018.8579953"},{"issue":"1","key":"147_CR82","doi-asserted-by":"publisher","first-page":"e200125","DOI":"10.1148\/ryai.2020200125","volume":"3","author":"B Wang","year":"2021","unstructured":"Wang B, Perronne L, Burke C, Adler RS (2021) Artificial intelligence for classification of soft-tissue masses at US. Radiol: Artif Intell 3(1):e200125. https:\/\/doi.org\/10.1148\/ryai.2020200125","journal-title":"Radiol: Artif Intell"},{"issue":"3","key":"147_CR83","doi-asserted-by":"publisher","first-page":"035008","DOI":"10.1088\/1361-6560\/ac4c47","volume":"67","author":"XL Wu","year":"2022","unstructured":"Wu XL, Li MY, Cui XW, Xu GP (2022) Deep multimodal learning for lymph node metastasis prediction of primary thyroid cancer. Phys Med Biol 67(3):035008. https:\/\/doi.org\/10.1088\/1361-6560\/ac4c47","journal-title":"Phys Med Biol"},{"issue":"1","key":"147_CR84","doi-asserted-by":"publisher","first-page":"e0262291","DOI":"10.1371\/journal.pone.0262291","volume":"17","author":"F Destrempes","year":"2022","unstructured":"Destrempes F, Gesnik M, Chayer B, Roy-Cardinal MH, Olivi\u00e9 D, Giard JM et al (2022) Quantitative ultrasound, elastography, and machine learning for assessment of steatosis, inflammation, and fibrosis in chronic liver disease. PLoS One 17(1):e0262291. https:\/\/doi.org\/10.1371\/journal.pone.0262291","journal-title":"PLoS One"},{"issue":"6","key":"147_CR85","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1089\/thy.2018.0380","volume":"29","author":"B Zhang","year":"2019","unstructured":"Zhang B, Tian J, Pei SF, Chen YB, He X, Dong YH et al (2019) Machine learning-assisted system for thyroid nodule diagnosis. Thyroid 29(6):858\u2013867. https:\/\/doi.org\/10.1089\/thy.2018.0380","journal-title":"Thyroid"},{"issue":"4","key":"147_CR86","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1109\/JBHI.2019.2950994","volume":"24","author":"PL Qin","year":"2020","unstructured":"Qin PL, Wu K, Hu YS, Zeng JC, Chai XF (2020) Diagnosis of benign and malignant thyroid nodules using combined conventional ultrasound and ultrasound elasticity imaging. IEEE J Biomed Health Inf 24(4):1028\u20131036. https:\/\/doi.org\/10.1109\/JBHI.2019.2950994","journal-title":"IEEE J Biomed Health Inf"},{"issue":"3","key":"147_CR87","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1089\/thy.2020.0305","volume":"31","author":"CK Zhao","year":"2021","unstructured":"Zhao CK, Ren TT, Yin YF, Shi H, Wang HX, Zhou BY et al (2021) A comparative analysis of two machine learning-based diagnostic patterns with thyroid imaging reporting and data system for thyroid nodules: diagnostic performance and unnecessary biopsy rate. Thyroid 31(3):470\u2013481. https:\/\/doi.org\/10.1089\/thy.2020.0305","journal-title":"Thyroid"},{"issue":"10","key":"147_CR88","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1007\/s11548-018-1796-5","volume":"13","author":"TT Liu","year":"2018","unstructured":"Liu TT, Ge XF, Yu JH, Guo Y, Wang YY, Wang WP et al (2018) Comparison of the application of B-mode and strain elastography ultrasound in the estimation of lymph node metastasis of papillary thyroid carcinoma based on a radiomics approach. Int J Comput Assisted Radiol Surg 13(10):1617\u20131627. https:\/\/doi.org\/10.1007\/s11548-018-1796-5","journal-title":"Int J Comput Assisted Radiol Surg"},{"issue":"3","key":"147_CR89","doi-asserted-by":"publisher","first-page":"318","DOI":"10.14366\/usg.20058","volume":"40","author":"SY Park","year":"2021","unstructured":"Park SY, Kang BJ (2021) Combination of shear-wave elastography with ultrasonography for detection of breast cancer and reduction of unnecessary biopsies: a systematic review and meta-analysis. Ultrasonography 40(3):318\u2013332. https:\/\/doi.org\/10.14366\/usg.20058","journal-title":"Ultrasonography"},{"issue":"3","key":"147_CR90","doi-asserted-by":"publisher","first-page":"315","DOI":"10.5152\/dir.2021.20018","volume":"27","author":"CX Li","year":"2021","unstructured":"Li CX, Li JJ, Tan T, Chen K, Xu Y, Wu R (2021) Application of ultrasonic dual-mode artificially intelligent architecture in assisting radiologists with different diagnostic levels on breast masses classification. Diagn Interv Radiol 27(3):315\u2013322. https:\/\/doi.org\/10.5152\/dir.2021.20018","journal-title":"Diagn Interv Radiol"},{"issue":"31","key":"147_CR91","doi-asserted-by":"publisher","first-page":"e26823","DOI":"10.1097\/MD.0000000000026823","volume":"100","author":"MY Kim","year":"2021","unstructured":"Kim MY, Kim SY, Kim YS, Kim ES, Chang JM (2021) Added value of deep learning-based computer-aided diagnosis and shear wave elastography to b-mode ultrasound for evaluation of breast masses detected by screening ultrasound. Medicine 100(31):e26823. https:\/\/doi.org\/10.1097\/MD.0000000000026823","journal-title":"Medicine"},{"issue":"1","key":"147_CR92","doi-asserted-by":"publisher","first-page":"3","DOI":"10.14366\/usg.14034","volume":"34","author":"YE Chung","year":"2015","unstructured":"Chung YE, Kim KW (2015) Contrast-enhanced ultrasonography: advance and current status in abdominal imaging. Ultrasonography 34(1):3\u201318. https:\/\/doi.org\/10.14366\/usg.14034","journal-title":"Ultrasonography"},{"issue":"51","key":"147_CR93","doi-asserted-by":"publisher","first-page":"e28147","DOI":"10.1097\/MD.0000000000028147","volume":"100","author":"MJ Qu","year":"2021","unstructured":"Qu MJ, Jia ZH, Sun LP, Wang H (2021) Diagnostic accuracy of three-dimensional contrast-enhanced ultrasound for focal liver lesions: A protocol for systematic review and meta-analysis. Medicine 100(51):e28147. https:\/\/doi.org\/10.1097\/MD.0000000000028147","journal-title":"Medicine"},{"issue":"1","key":"147_CR94","doi-asserted-by":"publisher","first-page":"14","DOI":"10.22328\/2079-5343-2020-12-1-14-23","volume":"12","author":"RA Kadyrleev","year":"2021","unstructured":"Kadyrleev RA, Busko EA, Kostromina EV, Shevkunov LN, Kozubova KV, Bagnenko SS (2021) Diagnostic algorithm of solid kidney lesions with contrast-enhanced ultrasound. Diagn Radiol Radiother 12(1):14\u201323","journal-title":"Diagn Radiol Radiother"},{"issue":"10","key":"147_CR95","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s11934-018-0836-6","volume":"19","author":"AN Ashrafi","year":"2018","unstructured":"Ashrafi AN, Nassiri N, Gill IS, Gulati M, Park D, de Castro Abreu AL (2018) Contrast-enhanced transrectal ultrasound in focal therapy for prostate cancer. Curr Urol Rep 19(10):87. https:\/\/doi.org\/10.1007\/s11934-018-0836-6","journal-title":"Curr Urol Rep"},{"issue":"4","key":"147_CR96","doi-asserted-by":"publisher","first-page":"e490-e509","DOI":"10.1016\/j.clbc.2020.03.002","volume":"20","author":"SC Zhou","year":"2020","unstructured":"Zhou SC, Le J, Zhou J, Huang YX, Qian L, Chang C (2020) The role of contrast-enhanced ultrasound in the diagnosis and pathologic response prediction in breast cancer: a meta-analysis and systematic review. Clin Breast Cancer 20(4):e490\u2013e509. https:\/\/doi.org\/10.1016\/j.clbc.2020.03.002","journal-title":"Clin Breast Cancer"},{"key":"147_CR97","doi-asserted-by":"publisher","first-page":"122245","DOI":"10.1016\/j.talanta.2021.122245","volume":"228","author":"F Maghsoudinia","year":"2021","unstructured":"Maghsoudinia F, Tavakoli MB, Samani RK, Hejazi SH, Sobhani T, Mehradnia F et al (2021) Folic acid-functionalized gadolinium-loaded phase transition nanodroplets for dual-modal ultrasound\/magnetic resonance imaging of hepatocellular carcinoma. Talanta 228:122245. https:\/\/doi.org\/10.1016\/j.talanta.2021.122245","journal-title":"Talanta"},{"issue":"8","key":"147_CR98","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1109\/TBME.2022.3148120","volume":"69","author":"HM Lin","year":"2022","unstructured":"Lin HM, Chen Y, Xie SY, Yu MM, Deng DQ, Sun T et al (2022) A dual-modal imaging method combining ultrasound and electromagnetism for simultaneous measurement of tissue elasticity and electrical conductivity. IEEE Trans Biomed Eng 69(8):2499\u20132511. https:\/\/doi.org\/10.1109\/TBME.2022.3148120","journal-title":"IEEE Trans Biomed Eng"},{"key":"147_CR99","doi-asserted-by":"publisher","first-page":"100380","DOI":"10.1016\/j.pacs.2022.100380","volume":"27","author":"YC Zhang","year":"2022","unstructured":"Zhang YC, Wang LD (2022) Adaptive dual-speed ultrasound and photoacoustic computed tomography. Photoacoustics 27:100380. https:\/\/doi.org\/10.1016\/j.pacs.2022.100380","journal-title":"Photoacoustics"},{"issue":"13","key":"147_CR100","doi-asserted-by":"publisher","first-page":"3714","DOI":"10.3390\/s20133714","volume":"20","author":"M Han","year":"2020","unstructured":"Han M, Choi W, Ahn J, Ryu H, Seo Y, Kim C (2020) In vivo dual-modal photoacoustic and ultrasound imaging of sentinel lymph nodes using a solid-state dye laser system. Sensors 20(13):3714. https:\/\/doi.org\/10.3390\/s20133714","journal-title":"Sensors"},{"issue":"3","key":"147_CR101","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1109\/TMI.2021.3122240","volume":"41","author":"YC Zhang","year":"2022","unstructured":"Zhang YC, Wang Y, Lai PX, Wang LD (2022) Video-rate dual-modal wide-beam harmonic ultrasound and photoacoustic computed tomography. IEEE Trans Med Imaging 41(3):727\u2013736. https:\/\/doi.org\/10.1109\/TMI.2021.3122240","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"147_CR102","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1021\/acsbiomaterials.0c01635","volume":"7","author":"CMC Aimaca\u00f1a","year":"2021","unstructured":"Aimaca\u00f1a CMC, Perez DAQ, Pinto SR, Debut A, Attia MF, Santos-Oliveira R et al (2021) Polytetrafluoroethylene-like nanoparticles as a promising contrast agent for dual modal ultrasound and X-ray bioimaging. ACS Biomater Sci Eng 7(3):1181\u20131191. https:\/\/doi.org\/10.1021\/acsbiomaterials.0c01635","journal-title":"ACS Biomater Sci Eng"},{"issue":"6","key":"147_CR103","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.1002\/smll.201302252","volume":"10","author":"HT Ke","year":"2014","unstructured":"Ke HT, Yue XL, Wang JR, Xing S, Zhang Q, Dai ZF et al (2014) Gold nanoshelled liquid perfluorocarbon nanocapsules for combined dual modal ultrasound\/CT imaging and photothermal therapy of cancer. Small 10(6):1220\u20131227. https:\/\/doi.org\/10.1002\/smll.201302252","journal-title":"Small"},{"key":"147_CR104","doi-asserted-by":"publisher","first-page":"106706","DOI":"10.1016\/j.ultras.2022.106706","volume":"122","author":"YX Song","year":"2022","unstructured":"Song YX, Zheng J, Lei L, Ni ZP, Zhao BL, Hu Y (2022) CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthesized data. Ultrasonics 122:106706. https:\/\/doi.org\/10.1016\/j.ultras.2022.106706","journal-title":"Ultrasonics"}],"container-title":["Visual Computing for Industry, Biomedicine, and Art"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42492-023-00147-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42492-023-00147-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42492-023-00147-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T11:03:13Z","timestamp":1700478193000},"score":1,"resource":{"primary":{"URL":"https:\/\/vciba.springeropen.com\/articles\/10.1186\/s42492-023-00147-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,13]]},"references-count":104,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["147"],"URL":"https:\/\/doi.org\/10.1186\/s42492-023-00147-2","relation":{},"ISSN":["2524-4442"],"issn-type":[{"value":"2524-4442","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,13]]},"assertion":[{"value":"5 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2023","order":3,"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":"Competing interests"}}],"article-number":"20"}}