Machine Learning in Invasive and Noninvasive Coronary Angiography

[11] Cristea E(2022)Interobserver variability among expert readers quantifying plaque volume and plaque characteristics on coronary CT angiography: a CLARIFY trial sub-study Clin Imaging. 91 19-25

[12] Mintz GS(2018)Comparison of physician visual assessment with quantitative coronary angiography in assessment of stenosis severity in China JAMA Intern Med. 178 239-247

[13] Davies JE(2011)Cost-effectiveness of coronary computed tomography and cardiac stress imaging in the emergency department: a decision analytic model comparing diagnostic strategies for chest pain in patients at low risk of acute coronary syndromes JACC Cardiovasc Imaging. 4 549-556

[14] Sen S(2016)Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis Sci Rep. 6 26286-2396

[15] Dehbi HM(2018)Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study Lancet. 392 2388-856

[16] Al-Lamee R(2017)Computed tomographic perfusion to predict response to recanalization in ischemic stroke Ann Neurol. 81 849-118

[17] Petraco R(2017)Dermatologist-level classification of skin cancer with deep neural networks Nature. 542 115-2410

[18] Nijjer SS(2016)Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs JAMA. 316 2402-1986

[19] Nørgaard BL(2019)Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging Eur Heart J. 40 1975-e65

[20] Leipsic J(2015)Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography J Med Imaging (Bellingham). 2 014003-209

[11] Cristea E(2022)Interobserver variability among expert readers quantifying plaque volume and plaque characteristics on coronary CT angiography: a CLARIFY trial sub-study Clin Imaging. 91 19-25

[12] Mintz GS(2018)Comparison of physician visual assessment with quantitative coronary angiography in assessment of stenosis severity in China JAMA Intern Med. 178 239-247

[13] Davies JE(2011)Cost-effectiveness of coronary computed tomography and cardiac stress imaging in the emergency department: a decision analytic model comparing diagnostic strategies for chest pain in patients at low risk of acute coronary syndromes JACC Cardiovasc Imaging. 4 549-556

[14] Sen S(2016)Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis Sci Rep. 6 26286-2396

[15] Dehbi HM(2018)Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study Lancet. 392 2388-856

[16] Al-Lamee R(2017)Computed tomographic perfusion to predict response to recanalization in ischemic stroke Ann Neurol. 81 849-118

[17] Petraco R(2017)Dermatologist-level classification of skin cancer with deep neural networks Nature. 542 115-2410

[18] Nijjer SS(2016)Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs JAMA. 316 2402-1986

[19] Nørgaard BL(2019)Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging Eur Heart J. 40 1975-e65

[20] Leipsic J(2015)Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography J Med Imaging (Bellingham). 2 014003-209