A framework of deep learning networks provides expert-level accuracy for the detection and prognostication of pulmonary arterial hypertension

Abstract

Aims To test the hypothesis that deep learning (DL) networks reliably detect pulmonary arterial hypertension (PAH) and provide prognostic information. Methods and results Consecutive patients with PAH, right ventricular (RV) dilation (without PAH), and normal controls were included. An ensemble of deep convolutional networks incorporating echocardiographic views and estimated RV systolic pressure (RVSP) was trained to detect (invasively confirmed) PAH. In addition, DL-networks were trained to segment cardiac chambers and extracted geometric information throughout the cardiac cycle. The ability of DL parameters to predict all-cause mortality was assessed using Cox-proportional hazard analyses. Overall, 450 PAH patients, 308 patients with RV dilatation (201 with tetralogy of Fallot and 107 with atrial septal defects) and 67 normal controls were included. The DL algorithm achieved an accuracy and sensitivity of detecting PAH on a per patient basis of 97.6 and 100%, respectively. On univariable analysis, automatically determined right atrial area, RV area, RV fractional area change, RV inflow diameter and left ventricular eccentricity index (P < 0.001 for all) were significantly related to mortality. On multivariable analysis DL-based RV fractional area change (P < 0.001) and right atrial area (P = 0.003) emerged as independent predictors of outcome. Statistically, DL parameters were non-inferior to measures obtained manually by expert echocardiographers in predicting prognosis. Conclusion The study highlights the utility of DL algorithms in detecting PAH on routine echocardiograms irrespective of RV dilatation. The algorithms outperform conventional echocardiographic evaluation and provide prognostic information at expert-level. Therefore, DL methods may allow for improved screening and optimized management of PAH.

Automatic echocardiographic evaluation of the probability of pulmonary hypertension using machine learning

“…24 Studies by Gerhard-Paul Diller has shown that deep learning can not only accurately diagnose IPAH, but also predict the prognosis of patients. 14 But they need more echocardiographic information to build the model and were investigated in a more restricted patient groups. On the other hand, our ML model not only learned about the spatial features of the left and RVs in PH patients, but also their temporal features, such as the changes in the size of the two chambers during systole and diastole.…”

“…Although previous studies have suggested an improvement in diagnostic performance using ML models, such as ML algorithms based on Logit Boost can better identify precapillary PH and post‐capillary PH 24 . Studies by Gerhard‐Paul Diller has shown that deep learning can not only accurately diagnose IPAH, but also predict the prognosis of patients 14 . But they need more echocardiographic information to build the model and were investigated in a more restricted patient groups.…”

“…However, these models have to integrate multiple aspects of information, not taking the guideline-suggested signs for PH into consideration. 14 In addition, they analyzed patients with certain groups of PH, limiting the generalizability of their findings. Thus, in the current study, we aimed to construct models to automatically assess the probability of PH in a more generalized PH population.…”

“…Furthermore, detection and prognostication of PH could be improved using a deep learning echocardiography model. However, these models have to integrate multiple aspects of information, not taking the guideline‐suggested signs for PH into consideration 14 . In addition, they analyzed patients with certain groups of PH, limiting the generalizability of their findings.…”

Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases

“…Artificial intelligence could have many applications in CHD, such as improving detection and diagnosis, prediction of prognosis, and guiding therapy. 73 , 74 Other potential applications include prediction of the effect of drugs and/or interventions 75 , 76 and incorporation of “soft” outcomes such as exercise capacity and quality of life into the decision-making process. The clinical applications using AI on CMPs and congenital heart diseases are summarized in Table 5 .…”