Detection of optical coherence tomography-defined thin-cap fibroatheroma in the coronary artery using deep learning.
EuroIntervention
; 16(5): 404-412, 2020 Aug 28.
Article
em En
| MEDLINE
| ID: mdl-31718998
ABSTRACT
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 ANDRESULTS:
A total of 602 coronary lesions from 602 angina patients were randomised into training and test sets in a 41 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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doença da Artéria Coronariana
/
Placa Aterosclerótica
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article