Diagnostic Value of Coronary Computed Tomography Angiography Image under Automatic Segmentation Algorithm for Restenosis after Coronary Stenting
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Published on: September 22, 2023
Xinrong He1, Juan Zhao2, Yunpeng Xu1
1Department of Cardiovascular Medicine 7, Xianyang Hospital of Yan'an University, Xianyang 712000, Shaanxi, China.
Insights
A new method combining convolutional neural network (CNN) and automatic segmentation significantly improves the diagnosis of coronary artery restenosis after stenting. This advanced coronary computed tomography angiography (CTA) analysis offers higher accuracy and better patient outcomes.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Medical Diagnostics
Background:
- Coronary artery stenting is a common procedure for coronary heart disease (CHD).
- Restenosis, or re-narrowing of the artery, is a significant complication after stenting.
- Accurate diagnosis of restenosis is crucial for effective patient management.
Purpose of the Study:
- To explore the diagnostic efficacy of coronary computed tomography angiography (CTA) for restenosis after coronary stenting.
- To evaluate a novel diagnostic method combining a convolutional neural network (CNN) algorithm with an automatic segmentation algorithm for vascular similarity features.
- To compare the diagnostic performance of the improved method against conventional CTA diagnosis.
Main Methods:
- 130 patients with coronary artery disease were divided into a control group (conventional CTA) and an observation group (CTA with improved automatic segmentation).
- The study utilized coronary angiography (CAG) as the gold standard for diagnostic criteria.
- A convolutional neural network (CNN) algorithm was combined with an automatic segmentation algorithm based on region growth of vascular similarity features.
Main Results:
- The CNN algorithm achieved an accuracy of 87.89%.
- The improved automatic segmentation algorithm demonstrated a significantly lower average voxel error (1.8921 HU/voxel) compared to the traditional algorithm (7.10091 HU/voxel) (p < 0.05).
- The observation group showed significantly higher diagnostic sensitivity (91.43%), specificity (86.76%), positive predictive value (88.89%), negative predictive value (89.66%), and overall accuracy (89.23%) compared to the control group (p < 0.05).
Conclusions:
- The combination of the CNN algorithm and the region growth algorithm using vascular similarity features provides accurate segmentation for CTA images.
- This enhanced diagnostic approach significantly improves the detection of restenosis after coronary stenting.
- The developed method offers a more effective diagnostic tool for patients with coronary artery disease post-stenting.
Abstract:
The diagnostic efficacy of coronary computed tomography angiography (CTA) images of coronary arteries in restenosis after coronary stenting based on the combination of the convolutional neural network (CNN) algorithm and the automatic segmentation algorithm for region growth of vascular similarity features was explored to provide a more effective diagnostic method for patients. 130 patients with coronary artery disease were randomly selected as the research objects, and they were averagely classified into the control group (conventional coronary CTA image diagnosis) and the observation group (coronary CTA image diagnosis based on an improved automatic segmentation algorithm). Based on the diagnostic criteria of coronary angiography (CAG), the efficacy of two kinds of coronary CTA images on the postoperative subsequent visit of coronary heart disease (CHD) stenting was evaluated. The results showed that the accuracy of the CNN algorithm was 87.89%, and the average voxel error of the improved algorithm was signally lower than that of the traditional algorithm (1.8921 HU/voxel vs. 7.10091 HU/voxel) (p < 0.05). The average score of the coronary CTA image in the observation group was higher than that in the control group (2.89 ± 0.11 points vs. 2.01 ± 0.73 points) (p < 0.05). The diagnostic sensitivity (91.43%), specificity (86.76%), positive predictive value (88.89%), negative predictive value (89.66%), and accuracy (89.23%) of the observation group were higher than those of the control group (p < 0.05). In conclusion, the region growth algorithm under the CNN algorithm and vascular similarity features had an accurate segmentation effect, which was helpful for the diagnosis of CTA image in restenosis after coronary stenting.
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