Front Cardiovasc Med. 2023;10:1167468.

Deep learning-based prediction for significant coronary artery stenosis on coronary computed tomography angiography in asymptomatic populations.

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제1저자 이희선, 강봉균 · 교신저자 조정희, 김민주

PMID 37416918 PMC10320158 DOI 10.3389/fcvm.2023.1167468 IF 3 순환기내과

소속 (4)

Department of Internal Medicine, School of Medicine, Seoul National University, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea.
Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea.
Institute of New Media and Communications, Seoul National University, Seoul, Republic of Korea.
Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.

Abstract

BACKGROUND Although coronary computed tomography angiography (CCTA) is currently utilized as the frontline test to accurately diagnose coronary artery disease (CAD) in clinical practice, there are still debates regarding its use as a screening tool for the asymptomatic population. Using deep learning (DL), we sought to develop a prediction model for significant coronary artery stenosis on CCTA and identify the individuals who would benefit from undergoing CCTA among apparently healthy asymptomatic adults.

METHODS We retrospectively reviewed 11,180 individuals who underwent CCTA as part of routine health check-ups between 2012 and 2019. The main outcome was the presence of coronary artery stenosis of ≥70% on CCTA. We developed a prediction model using machine learning (ML), including DL. Its performance was compared with pretest probabilities, including the pooled cohort equation (PCE), CAD consortium, and updated Diamond-Forrester (UDF) scores.

RESULTS In the cohort of 11,180 apparently healthy asymptomatic individuals (mean age 56.1 years; men 69.8%), 516 (4.6%) presented with significant coronary artery stenosis on CCTA. Among the ML methods employed, a neural network with multi-task learning (19 selected features), one of the DL methods, was selected due to its superior performance, with an area under the curve (AUC) of 0.782 and a high diagnostic accuracy of 71.6%. Our DL-based model demonstrated a better prediction than the PCE (AUC, 0.719), CAD consortium score (AUC, 0.696), and UDF score (AUC, 0.705). Age, sex, HbA1c, and HDL cholesterol were highly ranked features. Personal education and monthly income levels were also included as important features of the model.

CONCLUSION We successfully developed the neural network with multi-task learning for the detection of CCTA-derived stenosis of ≥70% in asymptomatic populations. Our findings suggest that this model may provide more precise indications for the use of CCTA as a screening tool to identify individuals at a higher risk, even in asymptomatic populations, in clinical practice.

Keywords

computed tomographic angiographycoronary artery diseasecoronary stenosisdeep learningdiagnostic screening programsneural networks

Figures

**Figure 1** Schematic flowchart of the study population. AUC, area under the curve; ROC, receiver operating characteristic; SHAP, SHapley Additive ExPlanations.

**Figure 2** Schematic diagram for the prediction model development. This is a schematic diagram of a newly developed DL-based prediction model for significant coronary artery stenosis on CCTA in 11,180 asymptomatic populations who underwent a routine health check-up. Using various parameters that physicians can readily access in clinical practice, the DL-based model could provide more precise indications of CCTA for the purpose of screening. CCTA, coronary computed tomography angiography; CHD, coronary heart disease; DL, deep learning.

**Figure 3** Increase of predictive value upon forward feature selection. Features in Y-axis are arranged by forward selection in order. The bar graph, generated from the validation dataset, shows how performance gains when variables are added to the model, on a logarithmic scale. Error bar shows its 95% confidence interval. Scatter dot plot shows an increase in ROC-AUC of the test dataset when using top-k features, indicating that performance saturates shortly after adding a few variables to the model. CVD, cardiovascular disease; ECG, electrocardiography; eGFR, estimated glomerular filtration rate; HbA1, glycated hemoglobin; HDL, high-density lipoprotein; OH, hydroxy; SBP, systolic blood pressure; WBC, white blood cells; other abbreviations as Figure 1.

**Figure 4** Outcome distribution of individuals with obstructive and non-obstructive CHD. The X-axis represents the probability of coronary artery stenosis, and the Y-axis represents the number of individuals in the test set. The blue and orange bars indicate individuals predicted to have coronary artery stenosis of <30% and ≥70%, respectively.

**Figure 5** Comparison of predictive performances among a neural network-based model, other DL-based models, and clinical risk scoring models with selected features. Both plots show the ROC-AUC of various models with selected features. (A) Our neural network-based prediction model demonstrates significantly better performance than previously known clinical scoring systems, with ROC-AUC of 0.782, even in asymptomatic populations. (B) Among the machine learning-based models, a neural network with multi-task shows the best performance to predict significant coronary artery stenosis on CCTA. CAD, coronary artery disease; DL, deep learning; XGBoost, eXtreme gradient boosting; other abbreviations as Figures 1, 2.

**Figure 6** SHAP value of a neural network-based model. The bar graph shows how each feature contributes to the model decision in test set. As the contribution of each input feature is described as an absolute value, the sign of the effect of each variable is not represented. Abbreviations as Figures 1, 3.

그림 출처: PubMed Central 공개 논문 (PMC10320158)

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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