Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization. (November 2022)

• Record Type: Journal Article
• Authors: Johri, Amer M.; Singh, Krishna V.; Mantella, Laura E.; Saba, Luca; Sharma, Aditya; Laird, John R.; Utkarsh, Kumar; Singh, Inder M.; Gupta, Suneet; Kalra, Manudeep S.; Suri, Jasjit S.
• Is Part Of: Computers in biology and medicine. Volume 150(2022)
• Publication Date: 2022-11
• Subjects: Coronary artery disease prediction -- Carotid ultrasound -- Artificial intelligence -- Machine learning -- Deep learning -- Performance evaluation; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106018
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Objective: Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These techniques are ad-hoc, unreliable, not fully automated, and have variabilities. We, therefore, introduce AtheroEdge- MCDL AI (AE3.0DL ) windows-based platform using multiclass Deep Learning (DL) system. Methods: Data was collected on 500 patients having both carotid ultrasound and corresponding coronary angiography scores (CAS), measured as stenosis in coronary arteries and considered as the gold standard. A total of 39 covariates were used, clubbed into three clusters, namely (i) Office-based: age, gender, body mass index, smoker, hypertension, systolic blood pressure, and diastolic blood pressure; (ii) Laboratory-based: Hyperlipidemia, hemoglobin A1c, and estimated glomerular filtration rate; and (iii) Carotid ultrasound image phenotypes: maximum plaque height, total plaque area, and intra-plaque neovascularization. Baseline characteristics for four classes (target labels) having significant (p < 0.0001) values were calculated using Chi-square and ANOVA. For handling the cohort's imbalance in the risk classes, AE3.0DL used the synthetic minority over-sampling technique (SMOTE). AE3.0DL used Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) DL models and the performance (accuracy and area-under-the-curve) was computed using 10-fold cross-validation (90% training, 10% testing) frameworks. AE3.0DL was validated and benchmarked. Results: The AE3.0DL using RNN and LSTM showed an accuracy and AUC (p < 0.0001) pairs as (95.00% and 0.98 ), and (95.34% and 0.99 ), respectively, and showed an improvement of 32.93% and 9.94 % against CCVRC and ML, respectively. AE3.0DL runs in <1 s. Conclusion: DL algorithms are a powerful paradigm for coronary artery disease (CAD) risk prediction and CVD risk stratification.