Attention-based hybrid deep learning models and its scientific validation for cardiovascular disease risk stratification

Background

Carotid plaque can be used to predict the risk of cardiovascular disease (CVD). Earlier machine learning solutions were not reliable, or accurate. The authors hypothesize that (i) attention-based unidirectional or bidirectional hybrid deep learning (HDL) is superior to non-attention-based unidirectional or bidirectional hybrid deep learning and (ii) attention-based bidirectional hybrid deep learning models are superior to attention-based unidirectional HDL paradigms. The proposed design, AtheroEdge™ 3.0_att-HDL (AtheroPoint™, Roseville, CA, USA), shows how effectively characteristics of the carotid plaque in attention-based hybrid deep learning systems predict the risk of CVD more accurately and reliably.

Methodology

The study involved 500 participants who underwent targeted carotid B-mode ultrasonography along with coronary angiography. Six hybrid models (four attention types) were used, totaling 6x4 = 24 models. These were benchmarked against the machine learning models. Mann-Whitney U test, Wilcoxon test, and paired T-test were used for the statistical and reliability tests. The scientific validation was performed using the unseen data. The area-under-the-curve and p-values were used for the performance evaluation of AtheroEdge™ 3.0_att-HDL.

Results

The best attention-based bidirectional HDL model showed a mean improvement of 36.11 %, 5.37 %, 5.37 %, and 1.04 % over Random Forest, unidirectional LSTM, bidirectional LSTM, and best attention-based unidirectional HDL models, respectively. As per the reliability and statistical test findings, the bidirectional AtheroEdge™ 3.0_att-HDL had a p-value of less than 0.001, while the unidirectional AtheroEdge™ 3.0_att-HDL also complied with regulations having a p-value < 0.005.

Conclusions

The hypothesis was scientifically validated, assessed for reliability and stability, and deemed suitable for clinical application.