The Official Journal of EuroPCR and the European Association of Percutaneous Coronary Interventions (EAPCI)

Detection of Optical Coherence Tomography–Defined Thin-Cap Fibroatheroma in the Coronary Artery Using Deep Learning

DOI: 10.4244/EIJ-D-19-00487

1. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
2. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
3. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea, Korea, Rupublic of
4. Biomedical Engineering Research Center, Asan Institute for Life Sciences, Seoul, Korea
5. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
6. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
7. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
8. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
9. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
10. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
11. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
12. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
13. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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Aims:. To develop a deep learning model for classifying frames with vs. without optical coherence tomography (OCT)-derived thin-cap fibroatheroma (TCFA). 

Methods and results:. Total 602 coronary lesions from 602 angina patients were randomized into training and test sets at a 4:1 ratio. A DenseNet model was developed to classify OCT frames with or without OCT-derived TCFA. Gradient-weighted class activation mapping was used to visualize the area of attention. In the training sample (35,678 frames of 480 lesions), the model with 5-fold cross-validation had an overall accuracy of 91.6±1.7%, sensitivity of 88.7±3.4%, and specificity of 91.8±2.0% (averaged AUC=0.96±0.01) in predicting the presence of TCFA. In the test samples (9,722 frames of 122 lesions), the overall accuracy at the frame level was 92.8% within the lesion (AUC=0.96) and 91.3% in the entire OCT pullback. The correlation between the %TCFA burdens per vessel predicted by the model compared with that identified by experts was significant (r=0.87, p<0.001). The region of attention was localized at the site of the thin cap in 93.4% of TCFA-containing frames. Total computational time per a pullback was 2.1 ± 0.3 seconds. 

Conclusions:. Deep learning algorithm can accurately detect an OCT-TCFA with a high reproducibility. The time-saving computerized process may assist clinicians to easily recognize high-risk lesions and to make decisions in the catheterization laboratory.

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