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Year Number of Results
2019 1
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2023 5
2024 2

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Page 1
Deep Learning for Head and Neck CT Angiography: Stenosis and Plaque Classification.
Fu F, Shan Y, Yang G, Zheng C, Zhang M, Rong D, Wang X, Lu J. Fu F, et al. Radiology. 2023 May;307(3):e220996. doi: 10.1148/radiol.220996. Epub 2023 Mar 7. Radiology. 2023. PMID: 36880944
The consistency between radiologists and the DL-assisted algorithm on plaque classification was 85.6% (320 of 374 cases [95% CI: 83.2, 88.6]) on a per-vessel basis. Moreover, the artificial intelligence model assisted in visual assessment, such as increasing confidence in …
The consistency between radiologists and the DL-assisted algorithm on plaque classification was 85.6% (320 of 374 cases [95% CI: 83.2 …
Comparison of two computed tomography perfusion post-processing software to assess infarct volume in patients with acute ischemic stroke.
Liu J, Wang J, Wu J, Gu S, Yao Y, Li J, Li Y, Ren H, Luo T. Liu J, et al. Front Neurosci. 2023 Apr 25;17:1151823. doi: 10.3389/fnins.2023.1151823. eCollection 2023. Front Neurosci. 2023. PMID: 37179549 Free PMC article.
OBJECTIVES: We used two automated software commonly employed in clinical practice-Olea Sphere (Olea) and Shukun-PerfusionGo (PerfusionGo)-to compare the diagnostic utility and volumetric agreement of computed tomography perfusion (CTP)-predicted final infarct volume (FIV) …
OBJECTIVES: We used two automated software commonly employed in clinical practice-Olea Sphere (Olea) and Shukun-PerfusionGo (Perfusio …
Diagnosis of coronary artery disease in patients with type 2 diabetes mellitus based on computed tomography and pericoronary adipose tissue radiomics: a retrospective cross-sectional study.
Dong X, Li N, Zhu C, Wang Y, Shi K, Pan H, Wang S, Shi Z, Geng Y, Wang W, Zhang T. Dong X, et al. Cardiovasc Diabetol. 2023 Jan 23;22(1):14. doi: 10.1186/s12933-023-01748-0. Cardiovasc Diabetol. 2023. PMID: 36691047 Free PMC article. Clinical Trial.

Of these models, Model 2 had higher diagnostic efficacy for CAD than Model 1 (p < 0.001, 95% CI [0.129-0.350]). However, Model 4 did not improve the effectiveness of the identification of CAD compared to Model 2 (p = 0.776); similarly, the AUC did not significantly diff

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Of these models, Model 2 had higher diagnostic efficacy for CAD than Model 1 (p < 0.001, 95% CI [0.129-0.350]). However, Model 4 d

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Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules.
Huang W, Deng H, Li Z, Xiong Z, Zhou T, Ge Y, Zhang J, Jing W, Geng Y, Wang X, Tu W, Dong P, Liu S, Fan L. Huang W, et al. Front Oncol. 2023 Aug 17;13:1255007. doi: 10.3389/fonc.2023.1255007. eCollection 2023. Front Oncol. 2023. PMID: 37664069 Free PMC article.
RESULTS: The model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which …
RESULTS: The model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed …
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. Yi Y, et al. Acad Radiol. 2022 Apr;29 Suppl 4:S49-S58. doi: 10.1016/j.acra.2021.10.027. Epub 2021 Dec 9. Acad Radiol. 2022. PMID: 34895831
For vessel-based analysis, the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of conventional CTA were 74.3% (95% CI: 64.9%-82.0%), 85.6% (95% CI: 77.0%-91.4%), 84.3% (95% CI: 75.2%-90.7%), 76.1% (95% …
For vessel-based analysis, the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of con …
Coronary artery calcium score quantification using a deep-learning algorithm.
Wang W, Wang H, Chen Q, Zhou Z, Wang R, Wang H, Zhang N, Chen Y, Sun Z, Xu L. Wang W, et al. Clin Radiol. 2020 Mar;75(3):237.e11-237.e16. doi: 10.1016/j.crad.2019.10.012. Epub 2019 Nov 11. Clin Radiol. 2020. PMID: 31718789
The Agatston score categories and cardiac risk stratification displayed excellent agreement between the two methods, with kappa = 0.77 (95% confidence interval [CI]=0.73-0.81); however, a 13% reclassification rate was observed. ...
The Agatston score categories and cardiac risk stratification displayed excellent agreement between the two methods, with kappa = 0.77 (95% …
MRI-based traditional radiomics and computer-vision nomogram for predicting lymphovascular space invasion in endometrial carcinoma.
Long L, Sun J, Jiang L, Hu Y, Li L, Tan Y, Cao M, Lan X, Zhang J. Long L, et al. Diagn Interv Imaging. 2021 Jul-Aug;102(7-8):455-462. doi: 10.1016/j.diii.2021.02.008. Epub 2021 Mar 23. Diagn Interv Imaging. 2021. PMID: 33741266 Free article.
RESULTS: For predicting LVSI, the AUC values of Model 1 in the training and test cohorts were 0.79 (95% confidence interval [CI]: 0.702-0.889; accuracy: 65.9%; sensitivity: 88.8%; specificity: 57.8%) and 0.75 (95% CI: 0.585-0.914; accuracy: 69.5%; sensitivity: 85.7% …
RESULTS: For predicting LVSI, the AUC values of Model 1 in the training and test cohorts were 0.79 (95% confidence interval [CI]: 0.7 …
Identification of high-risk carotid plaque by using carotid perivascular fat density on computed tomography angiography.
Zhang S, Yu X, Gu H, Kang B, Guo N, Wang X. Zhang S, et al. Eur J Radiol. 2022 May;150:110269. doi: 10.1016/j.ejrad.2022.110269. Epub 2022 Mar 18. Eur J Radiol. 2022. PMID: 35349933 Free article.

After adjusting for hyperlipidemia, statin use, antiplatelet use, calcification, degree of luminal stenosis, maximum plaque thickness, and ulceration, PFD was found to be strongly associated with cerebrovascular symptoms (OR, 1.13; 95% CI, 1.07-1.19; p < 0.001). Receive

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After adjusting for hyperlipidemia, statin use, antiplatelet use, calcification, degree of luminal stenosis, maximum plaque thickness, and u …
Establishment of a non-invasive prediction model for the risk of oesophageal variceal bleeding using radiomics based on CT.
Liu H, Sun J, Liu G, Liu X, Zhou Q, Zhou J. Liu H, et al. Clin Radiol. 2022 May;77(5):368-376. doi: 10.1016/j.crad.2022.01.046. Epub 2022 Mar 1. Clin Radiol. 2022. PMID: 35241274
Integration of the radiomics, CT, and clinical features model showed a better performance in predicting the risk of OVB, with an AUC of 0.89 (95% confidence interval [CI], 0.84-0.94) in the training dataset and 0.78 (95% CI, 0.68-0.87) in the validation dataset. ...
Integration of the radiomics, CT, and clinical features model showed a better performance in predicting the risk of OVB, with an AUC of 0.89 …
Automated total and vessel-specific coronary artery calcium (CAC) quantification on chest CT: direct comparison with CAC scoring on non-contrast cardiac CT.
Yu J, Qian L, Sun W, Nie Z, Zheng D, Han P, Shi H, Zheng C, Yang F. Yu J, et al. BMC Med Imaging. 2022 Oct 14;22(1):177. doi: 10.1186/s12880-022-00907-1. BMC Med Imaging. 2022. PMID: 36241978 Free PMC article.

RESULTS: The AI-based algorithm showed moderate reliability for the number of involved vessels in comparison to measures on cardiac CT (kappa = 0.75, 95% CI 0.70-0.79, P < 0.001) and an assignment agreement of 76%. Considerable coronary arteries with CAC were not identi

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RESULTS: The AI-based algorithm showed moderate reliability for the number of involved vessels in comparison to measures on cardiac CT (kapp …
13 results