scholarly journals CT Image Feature Diagnosis on the Basis of Deep Learning Algorithm for Preoperative Patients and Complications of Transcatheter Aortic Valve Implantation

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiong Zheng ◽  
Zhang Qian ◽  
Xiaofang Wang ◽  
Zhen Zhang ◽  
Lei Liu
Keyword(s):  
Deep Learning ◽  
Aortic Valve ◽  
Learning Algorithm ◽  
Average Diameter ◽  
Ct Image ◽  
Transcatheter Aortic Valve ◽  
Deep Learning Algorithm ◽  
Aortic Valve Implantation ◽  
Valve Implantation ◽  
Short Diameter

This work was aimed to explore the role of CT angiography information provided by deep learning algorithm in the diagnosis and complications of the disease focusing on congenital aortic valve disease and severe aortic valve stenosis. 120 patients who underwent ultrasound cardiography for aortic stenosis and underwent transcatheter aortic valve implantation (TAVI) in hospital were selected as the research objects. Patients received CT examination of deep learning algorithm within one week. The measurement methods were long and short diameter method, area method, and perimeter method. The deep learning algorithm was used to measure the long and short diameter, area, and perimeter of the target area before CT image processing. The results showed that the average diameter of long and short diameter measurement was 95% CI (0.84, 0.92), the average diameter of perimeter measurement was 95% CI (0.68, 0.87), and the average diameter of area measurement was 95% CI (0.72, 0.91). Among the 52 patients, 35 cases were hypertension (67%), 13 cases were diabetes (25%), 6 cases were chronic renal insufficiency (Cr > 2 mg/dL) (11%) (2 cases were treated with hemodialysis, 3.8%), 11 patients had chronic pulmonary disease (21%), 9 patients had cerebrovascular disease (17.3%) and atrial flutter and atrial fibrillation. Deep learning can achieve excellent results in CT image processing, and it was of great significance for the diagnosis of TAVI patients, improving the success rate of treatment and the prognosis of patients.

scholarly journals Enabling Automated Device Size Selection for Transcatheter Aortic Valve Implantation

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Patricio Astudillo ◽  
Peter Mortier ◽  
Johan Bosmans ◽  
Ole De Backer ◽  
Peter de Jaegere ◽  
...  
Keyword(s):  
Deep Learning ◽  
Aortic Valve ◽  
Transcatheter Aortic Valve Implantation ◽  
Aortic Annulus ◽  
Deep Convolutional Neural Networks ◽  
Size Selection ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Implantation ◽  
Valve Implantation ◽  
Device Size

The number of transcatheter aortic valve implantation (TAVI) procedures is expected to increase significantly in the coming years. Improving efficiency will become essential for experienced operators performing large TAVI volumes, while new operators will require training and may benefit from accurate support. In this work, we present a fast deep learning method that can predict aortic annulus perimeter and area automatically from aortic annular plane images. We propose a method combining two deep convolutional neural networks followed by a postprocessing step. The models were trained with 355 patients using modern deep learning techniques, and the method was evaluated on another 118 patients. The method was validated against an interoperator variability study of the same 118 patients. The differences between the manually obtained aortic annulus measurements and the automatic predictions were similar to the differences between two independent observers (paired diff. of 3.3 ± 16.8 mm2 vs. 1.3 ± 21.1 mm2 for the area and a paired diff. of 0.6 ± 1.7 mm vs. 0.2 ± 2.5 mm for the perimeter). The area and perimeter were used to retrieve the suggested prosthesis sizes for the Edwards Sapien 3 and the Medtronic Evolut device retrospectively. The automatically obtained device size selections accorded well with the device sizes selected by operator 1. The total analysis time from aortic annular plane to prosthesis size was below one second. This study showed that automated TAVI device size selection using the proposed method is fast, accurate, and reproducible. Comparison with the interobserver variability has shown the reliability of the strategy, and embedding this tool based on deep learning in the preoperative planning routine has the potential to increase the efficiency while ensuring accuracy.

scholarly journals OS-LALM-OGM Algorithm-Based Computed Tomography Image for Characteristics and Comorbidities of Patients before Transcatheter Aortic Valve Implantation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wenjie Sun ◽  
Jin He ◽  
Xianzhen Tan ◽  
Yi Wang ◽  
Fei Chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Aortic Valve ◽  
Transcatheter Aortic Valve Implantation ◽  
Signal To Noise Ratio ◽  
Clinical Manifestations ◽  
Ct Image ◽  
Transcatheter Aortic Valve ◽  
Inner Diameter ◽  
Aortic Valve Implantation ◽  
Valve Implantation

Based on the ordered subsets (OS), a linear augmentation Lagrangian method (OS-LALM) was constructed, which was then combined with the optimized gradient method (OGM) to construct the OS-LALM-OGM, so as to discuss application of the computed tomography (CT) images based on OS-LALM-OGM in evaluation of clinical manifestations and complications of patients before transcatheter aortic valve implantation (TAVI). The OS-LALM-OGM was compared with the filtered back projection (FBP) and OS-LALM. In addition, it was applied to evaluate the conditions of 128 patients before TAVI. It was found that the peak signal-to-noise ratio (PSNR) of OS-LALM-OGM was greater than that of the FBP and OS-LALM when the number of iterations was 5, 20, and 40, while the root mean square error (RMSD) was the opposite ( P < 0.05 ). The proportion of dyspnea was the highest, 38.28%, followed by angina (19.53%) and fainting (21.09%). The long diameter of the annulus and the average inner diameter of the annulus measured by the CT image based on the OS-LALM-OGM algorithm were greatly larger than the inner diameter of the aortic annulus measured by the CT based on the FBP algorithm ( P < 0.05 ); the evaluation sensitivity (95.24%) and specificity (85.85%) of CT based on the OS-LALM-OGM algorithm were obviously greater than those of X-ray, which were 84.43% and 76.77%, respectively ( P < 0.05 ). In short, the OS-LALM-OGM proposed had a relatively excellent effect on CT image reconstruction. The CT image based on the OS-LALM-OGM algorithm showed a better evaluation performance for patients before TAVI than the traditional FBP algorithm, showing higher sensitivity and specificity.

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