{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:33:10Z","timestamp":1772119990333,"version":"3.50.1"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s11554-024-01558-x","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T03:01:53Z","timestamp":1727406113000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automated stenosis detection in coronary artery disease using yolov9c: Enhanced efficiency and accuracy in real-time applications"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9947-713X","authenticated-orcid":false,"given":"Muhammet","family":"Akg\u00fcl","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2453-1645","authenticated-orcid":false,"given":"Hasan \u0130brahim","family":"Kozan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0520-9888","authenticated-orcid":false,"given":"Hasan Ali","family":"Aky\u00fcrek","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2433-246X","authenticated-orcid":false,"given":"\u015eakir","family":"Ta\u015fdemir","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"issue":"16","key":"1558_CR1","doi-asserted-by":"publisher","first-page":"5233","DOI":"10.3390\/jcm12165233","volume":"12","author":"S Cacciatore","year":"2023","unstructured":"Cacciatore, S., et al.: Management of coronary artery disease in older adults: recent advances and gaps in evidence. J Clin Med 12(16), 5233 (2023). https:\/\/doi.org\/10.3390\/jcm12165233","journal-title":"J Clin Med"},{"key":"1558_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/s23031193","author":"M Trigka","year":"2023","unstructured":"Trigka, M., Dritsas, E.: Long-term coronary artery disease risk prediction with machine learning models. Sens. (Basel) (2023). https:\/\/doi.org\/10.3390\/s23031193","journal-title":"Sens. (Basel)"},{"key":"1558_CR3","doi-asserted-by":"crossref","unstructured":"Genders, T. S. S., Hunink, M. G. M.: Epidemiology of coronary artery disease. In: Clinical Applications of Cardiac CT, vol. 9788847025226, Chapter 1, pp. 3\u20136 (2012)","DOI":"10.1007\/978-88-470-2522-6_1"},{"key":"1558_CR4","doi-asserted-by":"publisher","unstructured":"Luo, C., Tong, Y.: Comprehensive study and review of coronary artery disease. In: Proceedings of SPIE\u2014The International Society for Optical Engineering, vol. 12611 (2023). https:\/\/doi.org\/10.1117\/12.2669657","DOI":"10.1117\/12.2669657"},{"key":"1558_CR5","doi-asserted-by":"publisher","unstructured":"Anic, M., Fotiadis, D., Potsika, V.: Convolutional neural networks for the segmentation of coronary arteries. In: Proceedings\u20142023 IEEE 23rd International Conference on Bioinformatics and Bioengineering, BIBE 2023, pp. 308\u2013314 (2023). https:\/\/doi.org\/10.1109\/BIBE60311.2023.00057","DOI":"10.1109\/BIBE60311.2023.00057"},{"key":"1558_CR6","unstructured":"Nair, A., Klingensmith, J. D., Vince, D. G.: Real-time plaque characterization and visualization with spectral analysis of intravascular ultrasound data. In: Jasjit, C. Y., Suri, S. Wilson, D.L., Laxminarayan, S. (ed) Studies in Health Technology and Informatics, vol. 113, pp. 300\u2013320 (2005)"},{"key":"1558_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.mehy.2016.04.045","volume":"93","author":"L Sun","year":"2016","unstructured":"Sun, L., et al.: Hydroxychloroquine, a promising choice for coronary artery disease? Med. Hypoth. 93, 5\u20137 (2016). https:\/\/doi.org\/10.1016\/j.mehy.2016.04.045","journal-title":"Med. Hypoth."},{"issue":"1","key":"1558_CR8","doi-asserted-by":"publisher","first-page":"15349","DOI":"10.1149\/10701.15349ecst","volume":"107","author":"AS Bhosale","year":"2022","unstructured":"Bhosale, A.S., Chandankhede, M., Dawande, P., Bankar, N., Dhopavkar, G.: Review article on coronary artery disease. ECS Trans. 107(1), 15349\u201315353 (2022). https:\/\/doi.org\/10.1149\/10701.15349ecst","journal-title":"ECS Trans."},{"issue":"1","key":"1558_CR9","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.mpsur.2011.10.010","volume":"30","author":"RS Kutty","year":"2012","unstructured":"Kutty, R.S., Nair, S.K.: Surgery for coronary artery disease. Surg. (Oxf.) Rev. 30(1), 32\u201338 (2012). https:\/\/doi.org\/10.1016\/j.mpsur.2011.10.010","journal-title":"Surg. (Oxf.) Rev."},{"issue":"5","key":"1558_CR10","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1161\/HYPERTENSIONAHA.111.00275","volume":"61","author":"W Lieb","year":"2013","unstructured":"Lieb, W., et al.: Genetic predisposition to higher blood pressure increases coronary artery disease risk. Hypertension 61(5), 995\u20131001 (2013). https:\/\/doi.org\/10.1161\/HYPERTENSIONAHA.111.00275","journal-title":"Hypertension"},{"issue":"8","key":"1558_CR11","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1038\/s41591-022-01891-3","volume":"28","author":"C Tcheandjieu","year":"2022","unstructured":"Tcheandjieu, C., et al.: Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat. Med. 28(8), 1679\u20131692 (2022). https:\/\/doi.org\/10.1038\/s41591-022-01891-3","journal-title":"Nat. Med."},{"issue":"2","key":"1558_CR12","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.atherosclerosis.2013.02.039","volume":"228","author":"J van Setten","year":"2013","unstructured":"van Setten, J., et al.: Genome-wide association study of coronary and aortic calcification implicates risk loci for coronary artery disease and myocardial infarction. Atherosclerosis 228(2), 400\u2013405 (2013). https:\/\/doi.org\/10.1016\/j.atherosclerosis.2013.02.039","journal-title":"Atherosclerosis"},{"issue":"7288","key":"1558_CR13","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1038\/nature08873","volume":"464","author":"K Red-Horse","year":"2010","unstructured":"Red-Horse, K., Ueno, H., Weissman, I.L., Krasnow, M.A.: Coronary arteries form by developmental reprogramming of venous cells. Nature 464(7288), 549\u2013553 (2010). https:\/\/doi.org\/10.1038\/nature08873","journal-title":"Nature"},{"key":"1558_CR14","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.ijcard.2019.01.073","volume":"281","author":"F Dong","year":"2019","unstructured":"Dong, F., Chilian, W.M., Yin, L.: Knowns and unknowns of coronary artery development and anomalies. Int. J. Cardiol. 281, 40\u201341 (2019). https:\/\/doi.org\/10.1016\/j.ijcard.2019.01.073","journal-title":"Int. J. Cardiol."},{"issue":"19","key":"1558_CR15","doi-asserted-by":"publisher","first-page":"3758","DOI":"10.1111\/febs.13372","volume":"282","author":"Y Tang","year":"2015","unstructured":"Tang, Y., Zhang, Y., Chen, Y., Xiang, Y., Xie, Y.: Role of the microRNA, miR-206, and its target PIK3C2alpha in endothelial progenitor cell function\u2014potential link with coronary artery disease. FEBS J. 282(19), 3758\u20133772 (2015). https:\/\/doi.org\/10.1111\/febs.13372","journal-title":"FEBS J."},{"key":"1558_CR16","doi-asserted-by":"publisher","unstructured":"Bourekkadi, S. et al.: Analysis of the genetic predisposition to develop a Myocardial Infarction in a sample of Moroccan patients. In: E3S Web of Conferences, vol. 319, no. International Congress on Health Vigilance (VIGISAN 2021) (2021). https:\/\/doi.org\/10.1051\/e3sconf\/202131901013","DOI":"10.1051\/e3sconf\/202131901013"},{"issue":"1","key":"1558_CR17","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.21216","volume":"14","author":"H Basit","year":"2022","unstructured":"Basit, H., Kahn, A., Zaidi, S., Chadow, H., Khan, A.: A case of ST-elevation myocardial infarction with right bundle branch block, an ominous sign of critical coronary occlusion. Cureus 14(1), e21216 (2022). https:\/\/doi.org\/10.7759\/cureus.21216","journal-title":"Cureus"},{"issue":"11","key":"1558_CR18","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1177\/2047487317706585","volume":"24","author":"I Kormi","year":"2017","unstructured":"Kormi, I., et al.: Matrix metalloproteinase-8 and tissue inhibitor of matrix metalloproteinase-1 predict incident cardiovascular disease events and all-cause mortality in a population-based cohort. Eur. J. Prev. Cardiol. 24(11), 1136\u20131144 (2017). https:\/\/doi.org\/10.1177\/2047487317706585","journal-title":"Eur. J. Prev. Cardiol."},{"issue":"9","key":"1558_CR19","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1016\/j.bbadis.2014.05.009","volume":"1842","author":"MD Waltmann","year":"2014","unstructured":"Waltmann, M.D., Basford, J.E., Konaniah, E.S., Weintraub, N.L., Hui, D.Y.: Apolipoprotein E receptor-2 deficiency enhances macrophage susceptibility to lipid accumulation and cell death to augment atherosclerotic plaque progression and necrosis. Biochim. Biophys. Acta 1842(9), 1395\u20131405 (2014). https:\/\/doi.org\/10.1016\/j.bbadis.2014.05.009","journal-title":"Biochim. Biophys. Acta"},{"issue":"1","key":"1558_CR20","doi-asserted-by":"publisher","first-page":"158","DOI":"10.22271\/27069567.2021.v3.i1c.121","volume":"3","author":"DRA Nataraj","year":"2021","unstructured":"Nataraj, D.R.A., Shivaprasad, D.S., Methuku, D.V.K., Raja, D.S.B.: Study of prevalence of asymptomatic coronary artery disease in patients with diabetes mellitus by treadmill test. Int. J. Adv. Res. Med. 3(1), 158\u2013162 (2021). https:\/\/doi.org\/10.22271\/27069567.2021.v3.i1c.121","journal-title":"Int. J. Adv. Res. Med."},{"issue":"6","key":"1558_CR21","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1016\/j.ejrad.2016.03.006","volume":"85","author":"T Chadashvili","year":"2016","unstructured":"Chadashvili, T., Litmanovich, D., Hall, F., Slanetz, P.J.: Do breast arterial calcifications on mammography predict elevated risk of coronary artery disease? Eur. J. Radiol. 85(6), 1121\u20131124 (2016). https:\/\/doi.org\/10.1016\/j.ejrad.2016.03.006","journal-title":"Eur. J. Radiol."},{"key":"1558_CR22","doi-asserted-by":"publisher","unstructured":"Abdallah Mohamed, S., Ahmed Mohamed Fekry, E., Elsayed Mohamed Abd E.-H.: The role of breast arterial calcification on mammogram as a predictor for risk of coronary artery disease in women. Sci. J. Med. Schol. 1(3), 83\u201388 (2022). https:\/\/doi.org\/10.55675\/sjms.v1i3.18","DOI":"10.55675\/sjms.v1i3.18"},{"key":"1558_CR23","doi-asserted-by":"crossref","unstructured":"Thom, T., American Heart Association Statistics Committee and Stroke Statistics Subcommittee: Heart disease and stroke statistical-2006 update: a report from the American Heart Association Statistics Committee and Stroke statistics subcommittee. Circulation 113: e85-e151 (2006) [Online]. Available: https:\/\/cir.nii.ac.jp\/crid\/1574231875404477440","DOI":"10.1161\/CIRCULATIONAHA.105.171600"},{"key":"1558_CR24","unstructured":"Bonita, R., Beaglehole, R.: Trends in cerebrovascular disease mortality in New Zealand (in eng). N. Z. Med. J. 95(710): 411\u20134 (1982) [Online]. Available: https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/6955662"},{"key":"1558_CR25","unstructured":"Hoffman, E. D. Jr., Klees, B. S., Curtis, C. A.: Overview of the medicare and medicaid programs (in eng). Health Care Financ. Rev. 22(1): 175\u2013193 (2000). [Online]. Available: https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/25372783."},{"issue":"8","key":"1558_CR26","doi-asserted-by":"publisher","first-page":"e93","DOI":"10.1161\/CIR.0000000000001123","volume":"147","author":"CW Tsao","year":"2023","unstructured":"Tsao, C.W., et al.: Heart disease and stroke statistics-2023 update: a report from the american heart association. Circulation 147(8), e93\u2013e621 (2023). https:\/\/doi.org\/10.1161\/CIR.0000000000001123","journal-title":"Circulation"},{"key":"1558_CR27","unstructured":"Statistics N. C. F. H.: Multiple cause of death 2018\u20132021 on CDC WONDER Database. Accessed February (2024)"},{"issue":"1","key":"1558_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jacc.2017.04.052","volume":"70","author":"GA Roth","year":"2017","unstructured":"Roth, G.A., et al.: Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J. Am. Coll. Cardiol. 70(1), 1\u201325 (2017). https:\/\/doi.org\/10.1016\/j.jacc.2017.04.052","journal-title":"J. Am. Coll. Cardiol."},{"issue":"8","key":"1558_CR29","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1177\/2047487314547652","volume":"22","author":"R Ferrari","year":"2015","unstructured":"Ferrari, R., et al.: Geographical variations in the prevalence and management of cardiovascular risk factors in outpatients with CAD: data from the contemporary CLARIFY registry. Eur. J. Prev. Cardiol. 22(8), 1056\u20131065 (2015). https:\/\/doi.org\/10.1177\/2047487314547652","journal-title":"Eur. J. Prev. Cardiol."},{"issue":"9859","key":"1558_CR30","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1016\/S0140-6736(12)61728-0","volume":"380","author":"R Lozano","year":"2012","unstructured":"Lozano, R., et al.: Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380(9859), 2095\u20132128 (2012). https:\/\/doi.org\/10.1016\/S0140-6736(12)61728-0","journal-title":"Lancet"},{"key":"1558_CR31","doi-asserted-by":"publisher","unstructured":"Mortality, G. B. D.: C. Causes of death, \"Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990\u20132013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 385(9963): 117\u201371 (2015). https:\/\/doi.org\/10.1016\/S0140-6736(14)61682-2","DOI":"10.1016\/S0140-6736(14)61682-2"},{"key":"1558_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fcvm.2020.00001","volume":"7","author":"C Martin-Isla","year":"2020","unstructured":"Martin-Isla, C., et al.: Image-based cardiac diagnosis with machine learning: a review. Front. Cardiovasc. Med. 7, 1 (2020). https:\/\/doi.org\/10.3389\/fcvm.2020.00001","journal-title":"Front. Cardiovasc. Med."},{"key":"1558_CR33","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2320\/1\/012025","author":"L Yi","year":"2022","unstructured":"Yi, L., Jinguo, L., Yongjie, Z., Ping, M.: Detection of self-explosive insulators in aerial images based on improved YOLO v4. J. Phys. Conf. Ser. (2022). https:\/\/doi.org\/10.1088\/1742-6596\/2320\/1\/012025","journal-title":"J. Phys. Conf. Ser."},{"key":"1558_CR34","doi-asserted-by":"publisher","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y. M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 7464\u20137475 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.00721","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"1558_CR35","doi-asserted-by":"publisher","first-page":"75385","DOI":"10.1109\/access.2022.3192034","volume":"10","author":"K Liu","year":"2022","unstructured":"Liu, K.: STBi-YOLO: a real-time object detection method for lung nodule recognition. IEEE Access 10, 75385\u201375394 (2022). https:\/\/doi.org\/10.1109\/access.2022.3192034","journal-title":"IEEE Access"},{"key":"1558_CR36","doi-asserted-by":"publisher","unstructured":"Soeprobowati, T. R., Noor Chotimah, S., Warsito, B., Surarso, B., Warsito, B., Triadi Putranto, T.: Chronic kidney disease diagnosis system using sequential backward feature selection and artificial neural network. In: E3S Web of Conferences, vol. 317, no. The 6th International Conference on Energy, Environment, Epidemiology, and Information System (ICENIS 2021) (2021). https:\/\/doi.org\/10.1051\/e3sconf\/202131705030","DOI":"10.1051\/e3sconf\/202131705030"},{"key":"1558_CR37","doi-asserted-by":"publisher","unstructured":"Liu, H., Bamba, A. L., Gan, Y., Liu, H., Bamba, A. L., Gan, Y.: Uncertainty measurement and confidence calibration for calcium detection in optical coherence images. In: Proceedings Volume 12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII (2023). https:\/\/doi.org\/10.1117\/12.2652944","DOI":"10.1117\/12.2652944"},{"issue":"2","key":"1558_CR38","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1002\/acr2.11218","volume":"3","author":"H Lee","year":"2021","unstructured":"Lee, H., et al.: Identification of acute giant cell arteritis in real-world data using administrative claims-based algorithms. ACR Open Rheumatol. 3(2), 72\u201378 (2021). https:\/\/doi.org\/10.1002\/acr2.11218","journal-title":"ACR Open Rheumatol."},{"key":"1558_CR39","doi-asserted-by":"publisher","unstructured":"Li, Q., Guo, B., Jin, H., Guo, D., Ma, Z., Kang, C.: Application of object detection algorithm based on deep learning in classification of wild ginseng grades. In: IEEE Conference Publication. IEEE Xplore. 2023 2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR) (2023). https:\/\/doi.org\/10.1109\/AIHCIR61661.2023.00024","DOI":"10.1109\/AIHCIR61661.2023.00024"},{"key":"1558_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/a17030119","author":"Y Li","year":"2024","unstructured":"Li, Y., Yoshimura, T., Horima, Y., Sugimori, H.: A preprocessing method for coronary artery stenosis detection based on deep learning. Algorithms (2024). https:\/\/doi.org\/10.3390\/a17030119","journal-title":"Algorithms"},{"key":"1558_CR41","doi-asserted-by":"publisher","unstructured":"Hu, X. et al.: Researches advanced in application of medical image analysis based on deep learning. In: Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022) (2022). https:\/\/doi.org\/10.1117\/12.2641098","DOI":"10.1117\/12.2641098"},{"key":"1558_CR42","doi-asserted-by":"publisher","DOI":"10.1051\/0004-6361\/202345976","author":"K Grishin","year":"2023","unstructured":"Grishin, K., Mei, S., Ili\u0107, S.: YOLO\u2013CL: galaxy cluster detection in the SDSS with deep machine learning. Astron. Astrophys. (2023). https:\/\/doi.org\/10.1051\/0004-6361\/202345976","journal-title":"Astron. Astrophys."},{"issue":"1","key":"1558_CR43","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1186\/1297-9686-43-40","volume":"43","author":"M Saatchi","year":"2011","unstructured":"Saatchi, M., et al.: Accuracies of genomic breeding values in American angus beef cattle using K-means clustering for cross-validation. Gen. Select. Evolut. 43(1), 40 (2011). https:\/\/doi.org\/10.1186\/1297-9686-43-40","journal-title":"Gen. Select. Evolut."},{"key":"1558_CR44","doi-asserted-by":"publisher","first-page":"7171498","DOI":"10.1155\/2020\/7171498","volume":"2020","author":"X Pan","year":"2020","unstructured":"Pan, X., et al.: A comprehensive review of natural products against liver fibrosis: flavonoids, quinones, lignans, phenols, and acids. Evid. Based Complem. Alternat. Med. 2020, 7171498 (2020). https:\/\/doi.org\/10.1155\/2020\/7171498","journal-title":"Evid. Based Complem. Alternat. Med."},{"key":"1558_CR45","doi-asserted-by":"publisher","unstructured":"Wang, C.-Y., Yeh, I.-H., Liao, H.-Y. M.: YOLOv9: learning what you want to learn using programmable gradient information (2024). https:\/\/doi.org\/10.48550\/arXiv.2402.13616. arXiv preprint arXiv:2402.13616","DOI":"10.48550\/arXiv.2402.13616"},{"issue":"1","key":"1558_CR46","doi-asserted-by":"publisher","first-page":"7582","DOI":"10.1038\/s41598-021-87174-2","volume":"11","author":"VV Danilov","year":"2021","unstructured":"Danilov, V.V., et al.: Real-time coronary artery stenosis detection based on modern neural networks. Sci. Rep. 11(1), 7582 (2021). https:\/\/doi.org\/10.1038\/s41598-021-87174-2","journal-title":"Sci. Rep."},{"key":"1558_CR47","doi-asserted-by":"publisher","unstructured":"Danilov, V. K., Kutikhin, A., Gerget, O., Frangi, A., Ovcharenko, E.: Angiographic dataset for stenosis detection. Mendeley Data. https:\/\/doi.org\/10.17632\/ydrm75xywg.2","DOI":"10.17632\/ydrm75xywg.2"},{"issue":"6","key":"1558_CR48","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1161\/CIRCULATIONAHA.107.699579","volume":"117","author":"RB D'Agostino Sr","year":"2008","unstructured":"D\u2019Agostino, R.B., Sr., et al.: General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 117(6), 743\u2013753 (2008). https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.107.699579","journal-title":"Circulation"},{"issue":"1","key":"1558_CR49","doi-asserted-by":"publisher","first-page":"160035","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3(1), 160035 (2016). https:\/\/doi.org\/10.1038\/sdata.2016.35","journal-title":"Sci. Data"},{"key":"1558_CR50","doi-asserted-by":"publisher","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779\u2013788 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"1558_CR51","doi-asserted-by":"publisher","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263\u20137271 (2017). https:\/\/doi.org\/10.1109\/cvpr.2017.690","DOI":"10.1109\/cvpr.2017.690"},{"key":"1558_CR52","doi-asserted-by":"publisher","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580\u2013587 (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.81","DOI":"10.1109\/CVPR.2014.81"},{"issue":"02","key":"1558_CR53","doi-asserted-by":"publisher","first-page":"2","DOI":"10.4172\/2168-9784.1000240","volume":"06","author":"S Sugawa","year":"2017","unstructured":"Sugawa, S.: Significance of screening the general population for potential cardiovascular diseases with a combination assay of B-type natriuretic peptide and high sensitive troponin I. J. Med. Diagnos. Methods 06(02), 2 (2017). https:\/\/doi.org\/10.4172\/2168-9784.1000240","journal-title":"J. Med. Diagnos. Methods"},{"key":"1558_CR54","doi-asserted-by":"publisher","unstructured":"Phan, Q. B., Nguyen, T. T.: A novel approach for PV cell fault detection using YOLOv8 and particle swarm optimization. In: 2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 634\u2013638 (2023). https:\/\/doi.org\/10.1109\/MWSCAS57524.2023.10406139","DOI":"10.1109\/MWSCAS57524.2023.10406139"},{"key":"1558_CR55","doi-asserted-by":"publisher","DOI":"10.3390\/s23073691","author":"P Ruiz-Ponce","year":"2023","unstructured":"Ruiz-Ponce, P., Ortiz-Perez, D., Garcia-Rodriguez, J., Kiefer, B.: POSEIDON: a data augmentation tool for small object detection datasets in maritime environments. Sens. (Basel) (2023). https:\/\/doi.org\/10.3390\/s23073691","journal-title":"Sens. (Basel)"},{"key":"1558_CR56","doi-asserted-by":"publisher","first-page":"37257","DOI":"10.1109\/access.2022.3160424","volume":"10","author":"IS Mohamed","year":"2022","unstructured":"Mohamed, I.S., Chuan, L.K.: PAE: portable appearance extension for multiple object detection and tracking in traffic scenes. IEEE Access 10, 37257\u201337268 (2022). https:\/\/doi.org\/10.1109\/access.2022.3160424","journal-title":"IEEE Access"},{"key":"1558_CR57","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12102323","author":"H Lou","year":"2023","unstructured":"Lou, H., et al.: DC-YOLOv8: small-size object detection algorithm based on camera sensor. Electronics. (2023). https:\/\/doi.org\/10.3390\/electronics12102323","journal-title":"Electronics."},{"key":"1558_CR58","doi-asserted-by":"publisher","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp. 2961\u20132969 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"key":"1558_CR59","doi-asserted-by":"publisher","DOI":"10.3390\/s22155817","author":"H Liu","year":"2022","unstructured":"Liu, H., Sun, F., Gu, J., Deng, L.: SF-YOLOv5: a lightweight small object detection algorithm based on improved feature fusion mode. Sens. (Basel) (2022). https:\/\/doi.org\/10.3390\/s22155817","journal-title":"Sens. (Basel)"},{"key":"1558_CR60","doi-asserted-by":"publisher","unstructured":"Guarin, D., Wong, J., Ramirez-Zamora, A.: An artificial intelligence video-based assessment of upper-limb bradykinesia in Parkinson\u2019s disease (2023). https:\/\/doi.org\/10.21203\/rs.3.rs-3092935\/v1","DOI":"10.21203\/rs.3.rs-3092935\/v1"},{"key":"1558_CR61","doi-asserted-by":"publisher","unstructured":"Su R., Huang W., Ma H., Song X., Hu J.: SGE NET: video object detection with squeezed gru and information entropy map. In: IEEE Conference Publication | IEEE Xplore,\" 2021 IEEE International Conference on Image Processing (ICIP) (2021). https:\/\/doi.org\/10.1109\/ICIP42928.2021.9506081","DOI":"10.1109\/ICIP42928.2021.9506081"},{"key":"1558_CR62","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.mri.2022.09.006","volume":"94","author":"J Qiu","year":"2022","unstructured":"Qiu, J., et al.: Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: a preliminary study based on deep learning. Magn. Reson. Imaging 94, 105\u2013111 (2022). https:\/\/doi.org\/10.1016\/j.mri.2022.09.006","journal-title":"Magn. Reson. Imaging"},{"issue":"2","key":"1558_CR63","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1002\/jmri.28795","volume":"59","author":"JS Hong","year":"2024","unstructured":"Hong, J.S., et al.: Deep learning detection and segmentation of brain arteriovenous malformation on magnetic resonance angiography. J. Magn. Reson. Imaging 59(2), 587\u2013598 (2024). https:\/\/doi.org\/10.1002\/jmri.28795","journal-title":"J. Magn. Reson. Imaging"},{"key":"1558_CR64","doi-asserted-by":"publisher","unstructured":"Osama M., Kumar R., Shahid M.: Empowering Cardiologists with deep learning YOLOv8 model for accurate coronary artery stenosis detection in angiography images. In: 2023 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, pp. 1\u20136 (2023). https:\/\/doi.org\/10.1109\/ICICAT57735.2023.10263760","DOI":"10.1109\/ICICAT57735.2023.10263760"},{"issue":"6","key":"1558_CR65","doi-asserted-by":"publisher","first-page":"e27678","DOI":"10.1016\/j.heliyon.2024.e27678","volume":"10","author":"Y Tan","year":"2024","unstructured":"Tan, Y., et al.: Image detection of aortic dissection complications based on multi-scale feature fusion. Heliyon 10(6), e27678 (2024). https:\/\/doi.org\/10.1016\/j.heliyon.2024.e27678","journal-title":"Heliyon"},{"issue":"15","key":"1558_CR66","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.3390\/diagnostics14151672","volume":"14","author":"TL Lin","year":"2024","unstructured":"Lin, T.L., et al.: Assessing the efficacy of the spectrum-aided vision enhancer (SAVE) to detect acral lentiginous melanoma, melanoma in situ, nodular melanoma, and superficial spreading melanoma. Diagnos. (Basel) 14(15), 1672 (2024). https:\/\/doi.org\/10.3390\/diagnostics14151672","journal-title":"Diagnos. (Basel)"},{"key":"1558_CR67","doi-asserted-by":"publisher","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018). https:\/\/doi.org\/10.48550\/arXiv.1804.02767. arXiv preprint arXiv:1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"issue":"11","key":"1558_CR68","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"ZQ Zhao","year":"2019","unstructured":"Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: a review. IEEE. Trans. Neural. Netw. Learn. Syst. 30(11), 3212\u20133232 (2019). https:\/\/doi.org\/10.1109\/TNNLS.2018.2876865","journal-title":"IEEE. Trans. Neural. Netw. Learn. Syst."},{"key":"1558_CR69","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., et al.: Microsoft coco: common objects in context. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part V 13. Springer, pp. 740\u2013755 (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1558_CR70","doi-asserted-by":"publisher","unstructured":"Liu, W., et al.: Ssd: single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14. Springer, pp. 21\u201337 (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01558-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01558-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01558-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T11:26:07Z","timestamp":1728991567000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01558-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,27]]},"references-count":70,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1558"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01558-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4774572\/v1","asserted-by":"object"}]},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,27]]},"assertion":[{"value":"20 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2024","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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"177"}}