| 1 |
Åkesson J, Ostenfeld E, Carlsson M, Arheden H, Heiberg E. Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis. Sci Rep 2023;13:1216. [PMID: 36681759 DOI: 10.1038/s41598-023-28348-y] [Reference Citation Analysis]
|
| 2 |
Chan Y, Lin Y, Wang W, Hu W, Lin C, Yu S. Identifying the occlusion of left subclavian artery with stent based on chest MRI images. Multimed Tools Appl. [DOI: 10.1007/s11042-022-13735-w] [Reference Citation Analysis]
|
| 3 |
Wellnhofer E. Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging. Front Cardiovasc Med 2022;9. [DOI: 10.3389/fcvm.2022.890809] [Reference Citation Analysis]
|
| 4 |
Kotina E, Ploskikh V, Shirokolobov A. Digital Image Processing in Nuclear Medicine. Phys Part Nuclei 2022;53:535-40. [DOI: 10.1134/s1063779622020435] [Reference Citation Analysis]
|
| 5 |
Staszak M, Staszak K, Wieszczycka K, Bajek A, Roszkowski K, Tylkowski B. Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. WIREs Comput Mol Sci 2022;12. [DOI: 10.1002/wcms.1568] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
|
| 6 |
Naseri Jahfari A, Tax D, Reinders M, van der Bilt I. Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View. JMIR Med Inform 2022;10:e29434. [PMID: 35044316 DOI: 10.2196/29434] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 7 |
Paixão GMDM, Santos BC, Araujo RMD, Ribeiro MH, Moraes JLD, Ribeiro AL. Machine Learning na Medicina: Revisão e Aplicabilidade. Arquivos Brasileiros de Cardiologia 2022;118:95-102. [DOI: 10.36660/abc.20200596] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 8 |
Reddy CD. Big Data and AI in Cardiac Imaging. Trends of Artificial Intelligence and Big Data for E-Health 2022. [DOI: 10.1007/978-3-031-11199-0_5] [Reference Citation Analysis]
|
| 9 |
Nakamura T, Sasano T. Artificial intelligence and cardiology: Current status and perspective: Artificial Intelligence and Cardiology. J Cardiol 2021:S0914-5087(21)00336-1. [PMID: 34895982 DOI: 10.1016/j.jjcc.2021.11.017] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 10 |
Agibetov A, Kammerlander A, Duca F, Nitsche C, Koschutnik M, Donà C, Dachs T, Rettl R, Stria A, Schrutka L, Binder C, Kastner J, Agis H, Kain R, Auer-grumbach M, Samwald M, Hengstenberg C, Dorffner G, Mascherbauer J, Bonderman D. Convolutional Neural Networks for Fully Automated Diagnosis of Cardiac Amyloidosis by Cardiac Magnetic Resonance Imaging. JPM 2021;11:1268. [DOI: 10.3390/jpm11121268] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 11 |
Abbasov IB. Artificial intelligence in medical imaging. J Phys : Conf Ser 2021;2094:032008. [DOI: 10.1088/1742-6596/2094/3/032008] [Reference Citation Analysis]
|
| 12 |
Badano LP, Keller DM, Muraru D, Torlasco C, Parati G. Artificial intelligence and cardiovascular imaging: A win-win combination. Anatol J Cardiol 2020;24:214-23. [PMID: 33001058 DOI: 10.14744/AnatolJCardiol.2020.94491] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 13 |
Deepa D, Singh Y, Wang MC, Hu W. An automated method for detecting atrial fat using convolutional neural network. Proc Inst Mech Eng H 2021;:9544119211029745. [PMID: 34227422 DOI: 10.1177/09544119211029745] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
|
| 14 |
Naseri Jahfari A, Tax D, Reinders M, van der Bilt I. Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View (Preprint).. [DOI: 10.2196/preprints.29434] [Reference Citation Analysis]
|
| 15 |
Field M, Hardcastle N, Jameson M, Aherne N, Holloway L. Machine learning applications in radiation oncology. Phys Imaging Radiat Oncol 2021;19:13-24. [PMID: 34307915 DOI: 10.1016/j.phro.2021.05.007] [Cited by in Crossref: 16] [Cited by in F6Publishing: 21] [Article Influence: 8.0] [Reference Citation Analysis]
|
| 16 |
Ranka S, Reddy M, Noheria A. Artificial intelligence in cardiovascular medicine. Curr Opin Cardiol 2021;36:26-35. [PMID: 33060388 DOI: 10.1097/HCO.0000000000000812] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
|
| 17 |
Slart RHJA, Williams MC, Juarez-Orozco LE, Rischpler C, Dweck MR, Glaudemans AWJM, Gimelli A, Georgoulias P, Gheysens O, Gaemperli O, Habib G, Hustinx R, Cosyns B, Verberne HJ, Hyafil F, Erba PA, Lubberink M, Slomka P, Išgum I, Visvikis D, Kolossváry M, Saraste A. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. Eur J Nucl Med Mol Imaging 2021;48:1399-413. [PMID: 33864509 DOI: 10.1007/s00259-021-05341-z] [Cited by in Crossref: 21] [Cited by in F6Publishing: 24] [Article Influence: 10.5] [Reference Citation Analysis]
|
| 18 |
Birkhoff DC, van Dalen ASHM, Schijven MP. A Review on the Current Applications of Artificial Intelligence in the Operating Room. Surg Innov 2021;:1553350621996961. [PMID: 33625307 DOI: 10.1177/1553350621996961] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
|
| 19 |
Schuuring MJ, Išgum I, Cosyns B, Chamuleau SAJ, Bouma BJ. Routine Echocardiography and Artificial Intelligence Solutions. Front Cardiovasc Med 2021;8:648877. [PMID: 33708808 DOI: 10.3389/fcvm.2021.648877] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
|
| 20 |
Gordo C, Núñez-Córdoba JM, Mateo R. Root causes of adverse drug events in hospitals and artificial intelligence capabilities for prevention. J Adv Nurs 2021;77:3168-75. [PMID: 33624324 DOI: 10.1111/jan.14779] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 21 |
Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020;2020. [PMID: 32185396 DOI: 10.1093/database/baaa010] [Cited by in Crossref: 120] [Cited by in F6Publishing: 128] [Article Influence: 60.0] [Reference Citation Analysis]
|
| 22 |
Sushma TV, Sriraam N, Megha Arakeri P, Suresh S. Classification of Fetal Heart Ultrasound Images for the Detection of CHD. Innovative Data Communication Technologies and Application 2021. [DOI: 10.1007/978-981-15-9651-3_41] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 23 |
Managuli R, Brook M. Instrumentation. Transesophageal Echocardiography for Pediatric and Congenital Heart Disease 2021. [DOI: 10.1007/978-3-030-57193-1_2] [Reference Citation Analysis]
|
| 24 |
Khomtchouk BB, Tran DT, Vand KA, Might M, Gozani O, Assimes TL. Cardioinformatics: the nexus of bioinformatics and precision cardiology. Brief Bioinform 2020;21:2031-51. [PMID: 31802103 DOI: 10.1093/bib/bbz119] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 25 |
Ma C, Wang X, Wu J, Cheng X, Xia L, Xue F, Qiu L. Real-world big-data studies in laboratory medicine: Current status, application, and future considerations. Clinical Biochemistry 2020;84:21-30. [DOI: 10.1016/j.clinbiochem.2020.06.014] [Cited by in Crossref: 12] [Cited by in F6Publishing: 15] [Article Influence: 4.0] [Reference Citation Analysis]
|
| 26 |
Jokerst CE, Cummings KW. Advances in Imaging of Adult Congenital Heart Disease. Advances in Clinical Radiology 2020;2:37-63. [DOI: 10.1016/j.yacr.2020.04.003] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
|
| 27 |
Azadani PN, Miller RJH, Sharir T, Diniz MA, Hu LH, Otaki Y, Gransar H, Liang JX, Eisenberg E, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Impact of Early Revascularization on Major Adverse Cardiovascular Events in Relation to Automatically Quantified Ischemia. JACC Cardiovasc Imaging 2021;14:644-53. [PMID: 32828784 DOI: 10.1016/j.jcmg.2020.05.039] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 4.7] [Reference Citation Analysis]
|
| 28 |
Navarro VM, Wasserman EA, Slomka P. Taking pigeons to heart: Birds proficiently diagnose human cardiac disease. Learn Behav 2020;48:9-21. [PMID: 31965462 DOI: 10.3758/s13420-020-00410-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 0.7] [Reference Citation Analysis]
|
| 29 |
Li S, Wang Z, Visser LC, Wisner ER, Cheng H. Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs. Vet Radiol Ultrasound 2020;61:611-8. [PMID: 32783354 DOI: 10.1111/vru.12901] [Cited by in Crossref: 13] [Cited by in F6Publishing: 15] [Article Influence: 4.3] [Reference Citation Analysis]
|
| 30 |
Retson TA, Masutani EM, Golden D, Hsiao A. Clinical Performance and Role of Expert Supervision of Deep Learning for Cardiac Ventricular Volumetry: A Validation Study. Radiol Artif Intell 2020;2:e190064. [PMID: 32797119 DOI: 10.1148/ryai.2020190064] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
|
| 31 |
Tang F, Bai C, Zhao XX, Yuan WF. Artificial Intelligence and Myocardial Contrast Enhancement Pattern. Curr Cardiol Rep 2020;22:77. [PMID: 32632670 DOI: 10.1007/s11886-020-01306-0] [Reference Citation Analysis]
|
| 32 |
Bachtiger P, Plymen CM, Pabari PA, Howard JP, Whinnett ZI, Opoku F, Janering S, Faisal AA, Francis DP, Peters NS. Artificial Intelligence, Data Sensors and Interconnectivity: Future Opportunities for Heart Failure. Card Fail Rev 2020;6:e11. [PMID: 32514380 DOI: 10.15420/cfr.2019.14] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.7] [Reference Citation Analysis]
|
| 33 |
Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, Kapa S, Lerman A, Luong C, Medina-Inojosa JR, Noseworthy PA, Pellikka PA, Redfield MM, Roger VL, Sandhu GS, Senecal C, Friedman PA. Artificial Intelligence in Cardiology: Present and Future. Mayo Clin Proc 2020;95:1015-39. [PMID: 32370835 DOI: 10.1016/j.mayocp.2020.01.038] [Cited by in Crossref: 81] [Cited by in F6Publishing: 48] [Article Influence: 27.0] [Reference Citation Analysis]
|
| 34 |
Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, Marwick TH. Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review. J Am Coll Cardiol 2019;73:1317-35. [PMID: 30898208 DOI: 10.1016/j.jacc.2018.12.054] [Cited by in Crossref: 221] [Cited by in F6Publishing: 244] [Article Influence: 73.7] [Reference Citation Analysis]
|
| 35 |
Seetharam K, Shrestha S, Sengupta PP. Artificial Intelligence in Cardiac Imaging. US Cardiology Review 2020;13:110-6. [DOI: 10.15420/usc.2019.19.2] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 3.3] [Reference Citation Analysis]
|
| 36 |
Rogers MA, Aikawa E. Cardiovascular calcification: artificial intelligence and big data accelerate mechanistic discovery. Nat Rev Cardiol. 2019;16:261-274. [PMID: 30531869 DOI: 10.1038/s41569-018-0123-8] [Cited by in Crossref: 79] [Cited by in F6Publishing: 88] [Article Influence: 26.3] [Reference Citation Analysis]
|
| 37 |
Yang W, Feinstein JA, Marsden AL. Computational Modeling and Personalized Surgery. 3-Dimensional Modeling in Cardiovascular Disease 2020. [DOI: 10.1016/b978-0-323-65391-6.00012-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
|
| 38 |
Fay CD. Computer-Aided Design and Manufacturing (CAD/CAM) for Bioprinting. Methods Mol Biol 2020;2140:27-41. [PMID: 32207104 DOI: 10.1007/978-1-0716-0520-2_3] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
|
| 39 |
Chang AC. Artificial Intelligence in Subspecialties. Intelligence-Based Medicine 2020. [DOI: 10.1016/b978-0-12-823337-5.00008-1] [Reference Citation Analysis]
|
| 40 |
Lenchik L, Heacock L, Weaver AA, Boutin RD, Cook TS, Itri J, Filippi CG, Gullapalli RP, Lee J, Zagurovskaya M, Retson T, Godwin K, Nicholson J, Narayana PA. Automated Segmentation of Tissues Using CT and MRI: A Systematic Review. Acad Radiol 2019;26:1695-706. [PMID: 31405724 DOI: 10.1016/j.acra.2019.07.006] [Cited by in Crossref: 45] [Cited by in F6Publishing: 47] [Article Influence: 11.3] [Reference Citation Analysis]
|
| 41 |
Dorado-díaz PI, Sampedro-gómez J, Vicente-palacios V, Sánchez PL. Aplicaciones de la inteligencia artificial en cardiología: el futuro ya está aquí. Revista Española de Cardiología 2019;72:1065-75. [DOI: 10.1016/j.recesp.2019.05.016] [Cited by in Crossref: 26] [Cited by in F6Publishing: 15] [Article Influence: 6.5] [Reference Citation Analysis]
|
| 42 |
Liu Z, Feng Y, Yang X. Right Ventricle Segmentation of Cine MRI Using Residual U-net Convolutinal Networks. 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) 2019. [DOI: 10.1109/pdcat46702.2019.00072] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
|
| 43 |
Oikonomou EK, West HW, Antoniades C. Cardiac Computed Tomography: Assessment of Coronary Inflammation and Other Plaque Features. ATVB 2019;39:2207-19. [DOI: 10.1161/atvbaha.119.312899] [Cited by in Crossref: 19] [Cited by in F6Publishing: 19] [Article Influence: 4.8] [Reference Citation Analysis]
|
| 44 |
Liu Z, Yang X. A Squeeze Convolutional Network For MRI Right Ventricle Segmentation. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2019. [DOI: 10.1109/bibm47256.2019.8983003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
|
| 45 |
Garcia EV, Slomka P, Moody JB, Germano G, Ficaro EP. Quantitative Clinical Nuclear Cardiology, Part 1: Established Applications. J Nucl Cardiol 2020;27:189-201. [PMID: 31654215 DOI: 10.1007/s12350-019-01906-6] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 46 |
Dorado-Díaz PI, Sampedro-Gómez J, Vicente-Palacios V, Sánchez PL. Applications of Artificial Intelligence in Cardiology. The Future is Already Here. Rev Esp Cardiol (Engl Ed) 2019;72:1065-75. [PMID: 31611150 DOI: 10.1016/j.rec.2019.05.014] [Cited by in Crossref: 21] [Cited by in F6Publishing: 27] [Article Influence: 5.3] [Reference Citation Analysis]
|
| 47 |
Lareyre F, Adam C, Carrier M, Dommerc C, Mialhe C, Raffort J. A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation. Sci Rep 2019;9:13750. [PMID: 31551507 DOI: 10.1038/s41598-019-50251-8] [Cited by in Crossref: 32] [Cited by in F6Publishing: 32] [Article Influence: 8.0] [Reference Citation Analysis]
|
| 48 |
Garcia EV, Slomka P, Moody JB, Germano G, Ficaro EP. Quantitative Clinical Nuclear Cardiology, Part 1: Established Applications. J Nucl Med 2019;60:1507-16. [PMID: 31375569 DOI: 10.2967/jnumed.119.229799] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.0] [Reference Citation Analysis]
|
| 49 |
Litjens G, Ciompi F, Wolterink JM, de Vos BD, Leiner T, Teuwen J, Išgum I. State-of-the-Art Deep Learning in Cardiovascular Image Analysis. JACC: Cardiovascular Imaging 2019;12:1549-65. [DOI: 10.1016/j.jcmg.2019.06.009] [Cited by in Crossref: 131] [Cited by in F6Publishing: 141] [Article Influence: 32.8] [Reference Citation Analysis]
|
| 50 |
Di Sopra L, Piccini D, Coppo S, Stuber M, Yerly J. An automated approach to fully self‐gated free‐running cardiac and respiratory motion‐resolved 5D whole‐heart MRI. Magn Reson Med 2019;82:2118-32. [DOI: 10.1002/mrm.27898] [Cited by in Crossref: 32] [Cited by in F6Publishing: 35] [Article Influence: 8.0] [Reference Citation Analysis]
|
| 51 |
Seetharam K, Shrestha S, Sengupta PP. Artificial Intelligence in Cardiovascular Medicine. Curr Treat Options Cardiovasc Med 2019;21:25. [PMID: 31089906 DOI: 10.1007/s11936-019-0728-1] [Cited by in Crossref: 33] [Cited by in F6Publishing: 39] [Article Influence: 8.3] [Reference Citation Analysis]
|
| 52 |
Al-haddad R, Ismailani US, Rotstein BH. Current and Future Cardiovascular PET Radiopharmaceuticals. PET Clinics 2019;14:293-305. [DOI: 10.1016/j.cpet.2018.12.010] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
|
| 53 |
Robinson AA, Bourque JM. Emerging Techniques for Cardiovascular PET. Cardiovasc Innov Appl 2019;4:13-24. [PMID: 34552704 DOI: 10.15212/cvia.2019.0004] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
|
| 54 |
Seetharam K, Shresthra S, Mills JD, Sengupta PP. Artificial Intelligence in Nuclear Cardiology: Adding Value to Prognostication. Curr Cardiovasc Imaging Rep 2019;12. [DOI: 10.1007/s12410-019-9490-8] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 4.0] [Reference Citation Analysis]
|
| 55 |
Wolterink JM. Left ventricle segmentation in the era of deep learning. J Nucl Cardiol 2020;27:988-91. [PMID: 30834498 DOI: 10.1007/s12350-019-01674-3] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 56 |
Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies 2019;28:73-81. [DOI: 10.1080/13645706.2019.1575882] [Cited by in Crossref: 109] [Cited by in F6Publishing: 66] [Article Influence: 27.3] [Reference Citation Analysis]
|
| 57 |
Abidov A, Chehab O. Cardiovascular risk assessment models: Have we found the perfect solution yet? J Nucl Cardiol 2020;27:2375-85. [PMID: 30793251 DOI: 10.1007/s12350-019-01642-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
|
| 58 |
He J, Baxter SL, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25:30-36. [PMID: 30617336 DOI: 10.1038/s41591-018-0307-0] [Cited by in Crossref: 597] [Cited by in F6Publishing: 627] [Article Influence: 149.3] [Reference Citation Analysis]
|
| 59 |
Eid M, Spearman JV, van Assen M, De Santis D, Sahbaee P, Landreth SP, Jacobs B, De Cecco CN. Machine Learning and Artificial Intelligence in Cardiovascular Imaging. Contemporary Medical Imaging 2019. [DOI: 10.1007/978-1-60327-237-7_68] [Reference Citation Analysis]
|
| 60 |
Thoenes M, Bramlage P, Zamorano P, Messika-Zeitoun D, Wendt D, Kasel M, Kurucova J, Steeds RP. Patient screening for early detection of aortic stenosis (AS)-review of current practice and future perspectives. J Thorac Dis 2018;10:5584-94. [PMID: 30416809 DOI: 10.21037/jtd.2018.09.02] [Cited by in Crossref: 26] [Cited by in F6Publishing: 28] [Article Influence: 5.2] [Reference Citation Analysis]
|
| 61 |
Cai Q, Wang J, Li H, Li C, Wu X, Lu X. Measurement of Left Ventricular Volumes and Ejection Fraction in Patients with Regional Wall Motion Abnormalities Using an Automated 3D Quantification Algorithm. Ultrasound Med Biol 2018;44:2274-82. [PMID: 30122311 DOI: 10.1016/j.ultrasmedbio.2018.07.015] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.6] [Reference Citation Analysis]
|
| 62 |
Shaw LJ. Can a Machine Learn Better Than Humans? JACC: Cardiovascular Imaging 2018;11:1010-1. [DOI: 10.1016/j.jcmg.2017.07.025] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.6] [Reference Citation Analysis]
|
| 63 |
Masumoto H, Tabuchi H, Nakakura S, Ishitobi N, Miki M, Enno H. Deep-learning Classifier With an Ultrawide-field Scanning Laser Ophthalmoscope Detects Glaucoma Visual Field Severity. Journal of Glaucoma 2018;27:647-52. [DOI: 10.1097/ijg.0000000000000988] [Cited by in Crossref: 34] [Cited by in F6Publishing: 35] [Article Influence: 6.8] [Reference Citation Analysis]
|
| 64 |
Juarez-Orozco LE, Knol RJJ, Sanchez-Catasus CA, Martinez-Manzanera O, van der Zant FM, Knuuti J. Machine learning in the integration of simple variables for identifying patients with myocardial ischemia. J Nucl Cardiol 2020;27:147-55. [PMID: 29790017 DOI: 10.1007/s12350-018-1304-x] [Cited by in Crossref: 29] [Cited by in F6Publishing: 19] [Article Influence: 5.8] [Reference Citation Analysis]
|
| 65 |
Mazzanti M, Shirka E, Gjergo H, Hasimi E. Imaging, Health Record, and Artificial Intelligence: Hype or Hope? Curr Cardiol Rep 2018;20. [DOI: 10.1007/s11886-018-0990-y] [Cited by in Crossref: 14] [Cited by in F6Publishing: 11] [Article Influence: 2.8] [Reference Citation Analysis]
|
| 66 |
Taslakian B, Pires A, Halpern D, Babb JS, Axel L. Stylus/tablet user input device for MRI heart wall segmentation: efficiency and ease of use. Eur Radiol 2018;28:4586-97. [DOI: 10.1007/s00330-018-5435-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.6] [Reference Citation Analysis]
|
| 67 |
Selvarajah A, Bennamoun M, Playford D, Chow BJW, Dwivedi G. Application of Artificial Intelligence in Coronary Computed Tomography Angiography. Curr Cardiovasc Imaging Rep 2018;11. [DOI: 10.1007/s12410-018-9453-5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 0.8] [Reference Citation Analysis]
|
| 68 |
Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: are we there yet? Heart 2018;104:1156-64. [PMID: 29352006 DOI: 10.1136/heartjnl-2017-311198] [Cited by in Crossref: 227] [Cited by in F6Publishing: 237] [Article Influence: 45.4] [Reference Citation Analysis]
|
| 69 |
Loh BCS, Then PHH. Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions. Mhealth 2017;3:45. [PMID: 29184897 DOI: 10.21037/mhealth.2017.09.01] [Cited by in Crossref: 20] [Cited by in F6Publishing: 20] [Article Influence: 3.3] [Reference Citation Analysis]
|
| 70 |
Albà X, Lekadir K, Pereañez M, Medrano-Gracia P, Young AA, Frangi AF. Automatic initialization and quality control of large-scale cardiac MRI segmentations. Med Image Anal 2018;43:129-41. [PMID: 29073531 DOI: 10.1016/j.media.2017.10.001] [Cited by in Crossref: 35] [Cited by in F6Publishing: 25] [Article Influence: 5.8] [Reference Citation Analysis]
|
| 71 |
Rajiah P, Abbara S. CT coronary imaging–a fast evolving world. QJM: An International Journal of Medicine 2018;111:595-604. [DOI: 10.1093/qjmed/hcx175] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 1.2] [Reference Citation Analysis]
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