Deep learning with convolutional neural network for estimation of the characterisation of coronary plaques: Validation using IB-IVUS
| Autor: | Tomokazu Sato, Yasutaka Baba, Seitaro Oda, T. Yoshiura, S. Arao, J. Hiratsuka, T. Okimoto, Yoshinori Funama, T. Masuda, Kazuo Awai, Takeshi Nakaura, N. Noda |
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| Rok vydání: | 2022 |
| Předmět: |
medicine.medical_specialty
Acute coronary syndrome business.industry Deep learning Coronary computed tomography angiography medicine.disease Coronary Vessels Convolutional neural network Plaque Atherosclerotic Deep Learning Region of interest Radiologist 2 Coronary plaque Hounsfield scale medicine Humans Radiology Nuclear Medicine and imaging Neural Networks Computer Radiology Artificial intelligence business Retrospective Studies |
| Zdroj: | Radiography. 28:61-67 |
| ISSN: | 1078-8174 |
| DOI: | 10.1016/j.radi.2021.07.024 |
| Popis: | Introduction Deep learning approaches have shown high diagnostic performance in image classifications, such as differentiation of malignant tumors and calcified coronary plaque. However, it is unknown whether deep learning is useful for characterizing coronary plaques without the presence of calcification using coronary computed tomography angiography (CCTA). The purpose of this study was to compare the diagnostic performance of deep learning with a convolutional neural network (CNN) with that of radiologists in the estimation of coronary plaques. Methods We retrospectively enrolled 178 patients (191 coronary plaques) who had undergone CCTA and integrated backscatter intravascular ultrasonography (IB-IVUS) studies. IB-IVUS diagnosed 81 fibrous and 110 fatty or fibro-fatty plaques. We manually captured vascular short-axis images of the coronary plaques as Portable Network Graphics (PNG) images (150 × 150 pixels). The display window level and width were 100 and 700 Hounsfield units (HU), respectively. The deep-learning system (CNN; GoogleNet Inception v3) was trained on 153 plaques; its performance was tested on 38 plaques. The area under the curve (AUC) obtained by receiver operating characteristic analysis of the deep learning system and by two board-certified radiologists was compared. Results With the CNN, the AUC and the 95% confidence interval were 0.83 and 0.69–0.96, respectively; for radiologist 1 they were 0.61 and 0.42–0.80; for radiologist 2 they were 0.68 and 0.51–0.86, respectively. The AUC for CNN was significantly higher than for radiologists 1 (p = 0.04); for radiologist 2 it was not significantly different (p = 0.22). Conclusion DL-CNN performed comparably to radiologists for discrimination between fatty and fibro-fatty plaque on CCTA images. Implications for practice The diagnostic performance of the CNN and of two radiologists in the assessment of 191 ROIs on CT images of coronary plaques whose type corresponded with their IB-IVUS characterization was comparable. |
| Databáze: | OpenAIRE |
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