TY - JOUR
T1 - Improving patient identification for advanced cardiac imaging through machine learning-integration of clinical and coronary CT angiography data
AU - Benjamins, Jan Walter
AU - Yeung, Ming Wai
AU - Maaniitty, Teemu
AU - Saraste, Antti
AU - Klén, Riku
AU - van der Harst, Pim
AU - Knuuti, Juhani
AU - Juarez-Orozco, Luis Eduardo
N1 - Funding Information:
The work of J.W. Benjamins and M.W. Yeung was supported by the Research Project CVON-AI (2018B017), which is financed by the PPP Allowance made available by Top Sector Life Sciences & Health to the Nederlandse Hartstichting to stimulate public-private partnerships. This work reflects only the author's view, not that of the funders. Stichting LSH-TKI or Hartstichting or the Ministry of Economic Affairs is not responsible for any use that may be made of the information it contains.
Publisher Copyright:
© 2021 The Authors
PY - 2021/7/15
Y1 - 2021/7/15
N2 - BACKGROUND: Standard computed tomography angiography (CTA) outputs a myriad of interrelated variables in the evaluation of suspected coronary artery disease (CAD). But an important proportion of obstructive lesions does not cause significant myocardial ischemia. Nowadays, machine learning (ML) allows integration of numerous variables through complex interdependencies that optimize classification and prediction at the individual level. We evaluated ML performance in integrating CTA and clinical variables to identify patients that demonstrate myocardial ischemia through PET and those who ultimately underwent early revascularization.METHODS AND RESULTS: 830 patients with CTA and selective PET were analyzed. Nine clinical and 58 CTA variables were integrated through ensemble-boosting ML to identify patients with ischemia and those who underwent early revascularization. ML performance was compared against expert CTA interpretation, calcium score and clinical variables. While ML using all CTA variables achieved an AUC = 0.85, it was outperformed by expert CTA interpretation (AUC = 0.87, p < 0.01 for comparison), comparable to ML integration of CTA variables with clinical variables. However, the best performance was achieved by ML integration of expert CTA interpretation and clinical variables for both dependent variables (AUCs = 0.91 and 0.90, p < 0.001).CONCLUSIONS: Machine learning integration of diagnostic CTA and clinical data may improve identification of patients with myocardial ischemia and those requiring early revascularization at the individual level. This could potentially aid in sparing the need for subsequent advanced imaging and better identifying patients in ultimate need for revascularization. While ML integrating all CTA variables did not outperform expert CTA interpretation, ML data integration from different sources consistently improves diagnostic performance.
AB - BACKGROUND: Standard computed tomography angiography (CTA) outputs a myriad of interrelated variables in the evaluation of suspected coronary artery disease (CAD). But an important proportion of obstructive lesions does not cause significant myocardial ischemia. Nowadays, machine learning (ML) allows integration of numerous variables through complex interdependencies that optimize classification and prediction at the individual level. We evaluated ML performance in integrating CTA and clinical variables to identify patients that demonstrate myocardial ischemia through PET and those who ultimately underwent early revascularization.METHODS AND RESULTS: 830 patients with CTA and selective PET were analyzed. Nine clinical and 58 CTA variables were integrated through ensemble-boosting ML to identify patients with ischemia and those who underwent early revascularization. ML performance was compared against expert CTA interpretation, calcium score and clinical variables. While ML using all CTA variables achieved an AUC = 0.85, it was outperformed by expert CTA interpretation (AUC = 0.87, p < 0.01 for comparison), comparable to ML integration of CTA variables with clinical variables. However, the best performance was achieved by ML integration of expert CTA interpretation and clinical variables for both dependent variables (AUCs = 0.91 and 0.90, p < 0.001).CONCLUSIONS: Machine learning integration of diagnostic CTA and clinical data may improve identification of patients with myocardial ischemia and those requiring early revascularization at the individual level. This could potentially aid in sparing the need for subsequent advanced imaging and better identifying patients in ultimate need for revascularization. While ML integrating all CTA variables did not outperform expert CTA interpretation, ML data integration from different sources consistently improves diagnostic performance.
KW - Computed tomography angiography
KW - Coronary artery disease
KW - Feature integration
KW - Machine learning
KW - Myocardial ischemia
KW - Positron emission tomography
UR - http://www.scopus.com/inward/record.url?scp=85106306834&partnerID=8YFLogxK
U2 - 10.1016/j.ijcard.2021.04.009
DO - 10.1016/j.ijcard.2021.04.009
M3 - Article
C2 - 33831505
SN - 0167-5273
VL - 335
SP - 130
EP - 136
JO - International Journal of Cardiology
JF - International Journal of Cardiology
ER -