Coronary interventions - Mini focus: Plaque characteristics

Detection of optical coherence tomography-defined thin-cap fibroatheroma in the coronary artery using deep learning

EuroIntervention 2020;16:404-412. DOI: 10.4244/EIJ-D-19-00487

Hyun-Seok Min
Hyun-Seok Min1, PhD; Ji Hyeong Yoo1, BS; Soo-Jin Kang1, MD; June-Goo Lee2, PhD; Hyungjoo Cho1, BS; Pil Hyung Lee1, MD; Jung-Min Ahn1, MD; Duk-Woo Park1, MD; Seung-Whan Lee1, MD; Young-Hak Kim1, MD; Cheol Whan Lee1, MD; Seong-Wook Park1, MD; Seung-Jung Park1, MD
1. Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea; 2. Biomedical Engineering Research Center, Asan Institute for Life Sciences, Seoul, Korea

Aims: The aim of this study was to develop a deep learning model for classifying frames with versus without optical coherence tomography (OCT)-derived thin-cap fibroatheroma (TCFA).

Methods and results: A total of 602 coronary lesions from 602 angina patients were randomised into training and test sets in 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 visualise the area of attention. In the training sample (35,678 frames of 480 lesions), the model with fivefold 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 burden 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 localised at the site of the thin cap in 93.4% of TCFA-containing frames. Total computational time per pullback was 2.1±0.3 seconds.

Conclusions: A deep learning algorithm can accurately detect an OCT-TCFA with high reproducibility. The time-saving computerised process may assist clinicians to recognise high-risk lesions easily and to make decisions in the catheterisation laboratory.

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