403 related articles for article (PubMed ID: 35177029)
1. Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience.
Han X; Luo N; Xu L; Cao J; Guo N; He Y; Hong M; Jia X; Wang Z; Yang Z
BMC Med Imaging; 2022 Feb; 22(1):28. PubMed ID: 35177029
[TBL] [Abstract][Full Text] [Related]
2. Deep learning powered coronary CT angiography for detecting obstructive coronary artery disease: The effect of reader experience, calcification and image quality.
Liu CY; Tang CX; Zhang XL; Chen S; Xie Y; Zhang XY; Qiao HY; Zhou CS; Xu PP; Lu MJ; Li JH; Lu GM; Zhang LJ
Eur J Radiol; 2021 Sep; 142():109835. PubMed ID: 34237493
[TBL] [Abstract][Full Text] [Related]
3. The influence of artificial intelligence assistance on the diagnostic performance of CCTA for coronary stenosis for radiologists with different levels of experience.
Han X; He Y; Luo N; Zheng D; Hong M; Wang Z; Yang Z
Acta Radiol; 2023 Feb; 64(2):496-507. PubMed ID: 35389276
[TBL] [Abstract][Full Text] [Related]
4. Interobserver variability among expert readers quantifying plaque volume and plaque characteristics on coronary CT angiography: a CLARIFY trial sub-study.
Jonas RA; Weerakoon S; Fisher R; Griffin WF; Kumar V; Rahban H; Marques H; Karlsberg RP; Jennings RS; Crabtree TR; Choi AD; Earls JP
Clin Imaging; 2022 Nov; 91():19-25. PubMed ID: 35986973
[TBL] [Abstract][Full Text] [Related]
5. Validation of the commercial coronary computed tomographic angiography artificial intelligence for coronary artery stenosis: a cross-sectional study.
Han Q; Jing F; Sun Z; Liu F; Zhang J; Wang J; Liang H
Quant Imaging Med Surg; 2023 Jun; 13(6):3789-3801. PubMed ID: 37284069
[TBL] [Abstract][Full Text] [Related]
6. Diagnostic performance of deep learning to exclude coronary stenosis on CT angiography in TAVI patients.
Mehier B; Mahmoudi K; Veugeois A; Masri A; Amabile N; Giudice CD; Paul JF
Int J Cardiovasc Imaging; 2024 May; 40(5):981-990. PubMed ID: 38461472
[TBL] [Abstract][Full Text] [Related]
7. CT Evaluation by Artificial Intelligence for Atherosclerosis, Stenosis and Vascular Morphology (CLARIFY): A Multi-center, international study.
Choi AD; Marques H; Kumar V; Griffin WF; Rahban H; Karlsberg RP; Zeman RK; Katz RJ; Earls JP
J Cardiovasc Comput Tomogr; 2021; 15(6):470-476. PubMed ID: 34127407
[TBL] [Abstract][Full Text] [Related]
8. Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis.
Han D; Liu J; Sun Z; Cui Y; He Y; Yang Z
Comput Methods Programs Biomed; 2020 Nov; 196():105651. PubMed ID: 32712571
[TBL] [Abstract][Full Text] [Related]
9. Do plaque-related factors affect the diagnostic performance of an artificial intelligence coronary-assisted diagnosis system? Comparison with invasive coronary angiography.
Xu J; Chen L; Wu X; Li C; Ai G; Liu Y; Tian B; Guo D; Fang Z
Eur Radiol; 2022 Mar; 32(3):1866-1878. PubMed ID: 34564743
[TBL] [Abstract][Full Text] [Related]
10. The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography.
Jonas RA; Barkovich E; Choi AD; Griffin WF; Riess J; Marques H; Chang HJ; Choi JH; Doh JH; Her AY; Koo BK; Nam CW; Park HB; Shin SH; Cole J; Gimelli A; Khan MA; Lu B; Gao Y; Nabi F; Nakazato R; Schoepf UJ; Driessen RS; Bom MJ; Thompson RC; Jang JJ; Ridner M; Rowan C; Avelar E; Généreux P; Knaapen P; de Waard GA; Pontone G; Andreini D; Guglielmo M; Al-Mallah MH; Jennings RS; Crabtree TR; Earls JP
Clin Imaging; 2022 Apr; 84():149-158. PubMed ID: 35217284
[TBL] [Abstract][Full Text] [Related]
11. Artificial intelligence evaluation of coronary computed tomography angiography for coronary stenosis classification and diagnosis.
Lee DY; Chang CC; Ko CF; Lee YH; Tsai YL; Chou RH; Chang TY; Guo SM; Huang PH
Eur J Clin Invest; 2024 Jan; 54(1):e14089. PubMed ID: 37668089
[TBL] [Abstract][Full Text] [Related]
12. Performance of an Artificial Intelligence-based Application for the Detection of Plaque-based Stenosis on Monoenergetic Coronary CT Angiography: Validation by Invasive Coronary Angiography.
Yi Y; Xu C; Guo N; Sun J; Lu X; Yu S; Wang Y; Vembar M; Jin Z; Wang Y
Acad Radiol; 2022 Apr; 29 Suppl 4():S49-S58. PubMed ID: 34895831
[TBL] [Abstract][Full Text] [Related]
13. Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study.
Yang W; Chen C; Yang Y; Chen L; Yang C; Gong L; Wang J; Shi F; Wu D; Yan F
Radiol Med; 2023 Mar; 128(3):307-315. PubMed ID: 36800112
[TBL] [Abstract][Full Text] [Related]
14. Coronary computed tomography angiography analysis using artificial intelligence for stenosis quantification and stent segmentation: a multicenter study.
Meng Q; Yu P; Yin S; Li X; Chang Y; Xu W; Wu C; Xu N; Zhang H; Wang Y; Shen H; Zhang R; Zhang Q
Quant Imaging Med Surg; 2023 Oct; 13(10):6876-6886. PubMed ID: 37869330
[TBL] [Abstract][Full Text] [Related]
15. Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial.
Budoff MJ; Dowe D; Jollis JG; Gitter M; Sutherland J; Halamert E; Scherer M; Bellinger R; Martin A; Benton R; Delago A; Min JK
J Am Coll Cardiol; 2008 Nov; 52(21):1724-32. PubMed ID: 19007693
[TBL] [Abstract][Full Text] [Related]
16. Diagnostic Accuracy of Noninvasive 64-row Computed Tomographic Coronary Angiography (CCTA) Compared with Myocardial Perfusion Imaging (MPI): The PICTURE Study, A Prospective Multicenter Trial.
Budoff MJ; Li D; Kazerooni EA; Thomas GS; Mieres JH; Shaw LJ
Acad Radiol; 2017 Jan; 24(1):22-29. PubMed ID: 27771227
[TBL] [Abstract][Full Text] [Related]
17. Contemporary Discrepancies of Stenosis Assessment by Computed Tomography and Invasive Coronary Angiography.
Song YB; Arbab-Zadeh A; Matheson MB; Ostovaneh MR; Vavere AL; Dewey M; Rochitte C; Niinuma H; Laham R; Schuijf JD; Cox C; Brinker J; di Carli M; Lima JAC; Miller JM
Circ Cardiovasc Imaging; 2019 Feb; 12(2):e007720. PubMed ID: 30764641
[TBL] [Abstract][Full Text] [Related]
18. Coronary computed tomographic angiography in patients suspected of coronary artery disease: impact of observer experience on diagnostic performance and interobserver reproducibility.
Ovrehus KA; Munkholm H; Bøttcher M; Bøtker HE; Nørgaard BL
J Cardiovasc Comput Tomogr; 2010; 4(3):186-94. PubMed ID: 20451487
[TBL] [Abstract][Full Text] [Related]
19. Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease.
Chen M; Wang X; Hao G; Cheng X; Ma C; Guo N; Hu S; Tao Q; Yao F; Hu C
Br J Radiol; 2020 Sep; 93(1113):20191028. PubMed ID: 32101464
[TBL] [Abstract][Full Text] [Related]
20. Computer-aided CT coronary artery stenosis detection: comparison with human reading and quantitative coronary angiography.
Rief M; Kranz A; Hartmann L; Roehle R; Laule M; Dewey M
Int J Cardiovasc Imaging; 2014 Dec; 30(8):1621-7. PubMed ID: 25117643
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]