Machine learning prediction of right ventricular volume and ejection fraction from two-dimensional echocardiography in patients with pulmonary regurgitation.

Son Q Duong, Calista Dominy, Naveen Arivazhagan, David M Barris, Kali Hopkins, Kenan W D Stern, Nadine Choueiter, David Ezon, Jennifer Cohen, Mark K Friedberg, Ali N Zaidi, Girish N Nadkarni
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Abstract

Right ventricular (RV) end-diastolic volume (RVEDV) and ejection fraction (RVEF) by cardiac MRI (cMRI) guide management in chronic pulmonary regurgitation (PR). Two-dimensional echocardiography suboptimally correlate with RV volumes. This study tested whether combination of guideline-directed RV measures in a machine learning (ML) framework improves quantitative assessment of RVEDV and RVEF. RV measurements were obtained on subjects with > mild PR who had cMRI and echocardiogram within 90 days. A gradient-boosted trees algorithm predicted cMRI RV dilation (RVEDV > 160 ml/m2) and RV dysfunction (RVEF<47%), first with "guideline-only" measures, and then with "expanded-features" to include 44 total echocardiographic, clinical, and demographic variables. Model performance was compared to clinician visual assessment. Of 232 studies (56% tetralogy of Fallot, 20% pulmonary stenosis), the median age was 21.5 years, 21 (9%) had RV dilation, and 42 (18%) had RV dysfunction. For RV dilation prediction, the guideline-only model area under the receiver operating characteristic (AUROC)=0.68, and expanded-features model AUROC=0.85. At 90% sensitivity, the expanded-features model had 73% specificity, 25% positive predictive value (PPV), and 99% negative predictive value (NPV) This was similar to clinician performance (sensitivity 81%, specificity 81%, PPV 29%, NPV 98%). For prediction of RV dysfunction, the guideline-only AUROC= 0.71, additional features did not improve the model, and clinicians outperformed the model. In patients with PR, a ML model combining guidelines for RV assessment with demographic and additional echocardiographic parameters may effectively rule-out those with significant RV dilation at clinical thresholds for intervention, and performs similarly to expert clinicians.

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Machine learning prediction of right ventricular volume and ejection fraction from two-dimensional echocardiography in patients with pulmonary regurgitation. A case of constrictive pericarditis with aortic insufficiency: the role of cardiac magnetic resonance imaging. Non-invasive derivation of instantaneous free-wave ratio from invasive coronary angiography using a new deep learning artificial intelligence model and comparison with human operators' performance. Predicting the need for calcium modification techniques using computed tomography coronary angiography. Reliability of spectral Doppler in handheld ultrasonographic device.
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