Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks
| Autor: | Jae Y. Shin, R. Todd Hurst, Nima Tajbakhsh, Jianming Liang, Christopher B. Kendall |
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| Rok vydání: | 2017 |
| Předmět: |
Engineering
business.industry Deep learning Anova test Pattern recognition 02 engineering and technology Convolutional neural network Standard deviation 030218 nuclear medicine & medical imaging Clinical Practice 03 medical and health sciences 0302 clinical medicine Intima-media thickness Region of interest cardiovascular system 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing cardiovascular diseases Artificial intelligence Ultrasonography business |
| Zdroj: | Deep Learning for Medical Image Analysis |
| DOI: | 10.1016/b978-0-12-810408-8.00007-9 |
| Popis: | Cardiovascular disease (CVD) is the leading cause of death in the United States, yet it is largely preventable. But a critical part of prevention is identification of at-risk persons before adverse events. For predicting individual CVD risk, carotid intima–media thickness (CIMT), a noninvasive ultrasonography method, has proven to be valuable. However, each CIMT examination includes several ultrasonography videos, and interpreting each CIMT video involves 3 operations: (i) selecting 3 end-diastolic ultrasonographic frames (EUFs) in the video, (ii) localizing a region of interest (ROI) in each selected frame, and (iii) tracing the lumen–intima interface and the media–adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time-consuming, hindering the widespread utilization of CIMT in clinical practice. We present a new system based on convolutional neural networks to automate the entire process of CIMT video interpretation. The suggested system achieves a mean absolute error of 23.4 μm with a standard deviation of 17.3 μm for intima–media thickness measurements. The ANOVA test also yields p -values around 0.50, suggesting that there is a lack of evidence to show a difference among the CIMT measurements made by our system and those made by 3 experts. Our results suggest that the proposed system is robust against variability in CIMT measurements made by different experts. |
| Databáze: | OpenAIRE |
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