Key result
A deep convolution neural network algorithm for CTA images did not significantly improve diagnostic sensitivity (97.73% vs 95.45%) or accuracy (94.9% vs 92.86%) for abdominal aortic aneurysms (P>0.05).
Why the study?
To investigate the clinical value of CTA images processed by a deep convolution neural network segmentation denoising technique for diagnosing AAA and detecting disease changes.
Does a deep convolution neural network algorithm for CTA image segmentation and denoising improve diagnostic accuracy in patients with ruptured abdominal aortic aneurysm?
Observational (n=98)
Does a deep convolution neural network algorithm for CTA image segmentation and denoising improve diagnostic accuracy in patients with ruptured abdominal aortic aneurysm?
Absolute Event Rate: 97.73% vs 95.45%
p-value: p=> 0.05
AI-based segmentation and denoising of CTA images did not significantly improve the diagnostic sensitivity and accuracy for ruptured abdominal aortic aneurysms compared to standard CTA.
AI-enhanced CTA may aid ruptured AAA assessment; hypothesis-generating and requires prospective validation before practice change.
The purpose of this study was to investigate the clinical value of CT angiography (CTA) images processed by the segmentation denoising technique based on deep convolution neural network algorithm in the diagnosis of abdominal aortic aneurysm (AAA) and the detection of disease changes. A total of 98 patients with ruptured AAA were retrospectively selected as the study subjects. Patients were grouped according to whether the CTA images were optimized, the images receiving artificial intelligence segmentation and denoising were set as the observation group, and the CTA images without optimization were set as the control group. The detection and diagnosis effects of CTA images before and after the treatment were compared. The surgical results were used as the standard to analyze the diagnostic effect, and the maximum diameter measurement results of AAA and the proportion results of intraluminal thrombus (ILT) were compared. Although the sensitivity and accuracy of diagnosis in the observation group (97.73% and 94.9%) were higher than those in the control group (95.45% and 92.86%), there was no significant statistical significance ( P > 0.05 ). When the diameter of AAA was no less than 5 cm, all results showed that the coverage percentage of intraluminal thrombus (ILT) was over 50%. When the diameter of AAA was less than 5 cm, only 55.56% of the results showed that the percentage of ILT coverage was over 50%, with considerable differences ( P > 0.05 ). According to the results of the study, it was found that there was a certain relationship between the thrombus coverage of the abdominal aortic wall and the growth rate of AAA. The deep convolution neural network algorithm had a certain effect on the treatment of CTA, but it is not obvious. However, CTA had a better clinical diagnostic effect on AAA.
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Zheng et al. (2021) conducted an observational in Ruptured abdominal aortic aneurysm (AAA) (n=98). CTA images processed by deep convolution neural network segmentation and denoising vs. CTA images without optimization was evaluated on Diagnostic sensitivity (p=> 0.05). A deep convolution neural network algorithm for CTA images did not significantly improve diagnostic sensitivity (97.73% vs 95.45%) or accuracy (94.9% vs 92.86%) for abdominal aortic aneurysms (P>0.05).
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