Artificial Intelligence-Based Spiral CT 3D Reconstruction in Transcatheter Aortic Valve Implantation

Zhang, Kunpeng; Gao, Yan; Lv, Junwei; Li, Jian; Liu, Jingli

doi:10.1155/2022/5794681

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…In this setting, machine learning algorithms can optimize the prediction of the outcome [33]. Moreover, CT 3D-reconstruction model based on deep convolutional neural networks (DCNN) may provide better image quality and accuracy of traditional model-based iterative reconstructions [34].…”

Section: Discussionmentioning

confidence: 99%

Aortic valve and vascular calcium score in pre-TAVI CT: correlation with early post-procedural complications

et al. 2023

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Purpose The aim of our study was to evaluate the prevalence of early complications after Transcatheter Aortic Valve Implantation (TAVI) and their correlation with the Calcium Score (CS) of the aortic valve, aorta and ilio-femoral arteries derived from pre-procedural computed tomography (CT). Materials and methods We retrospectively reviewed 226 patients (100 males, mean age 79.4 ± 6.7 years) undergoing 64-slice CT for pre-TAVI evaluation from January 2018 to April 2021. The population was divided into CS quartiles. Results Overall, 173 patients underwent TAVI procedure, of whom 61% presented paravalvular leak after the procedure, 28% presented bleeding or vascular complications, 25% presented atrioventricular block, and 8% developed acute kidney injury. The prevalence of paravalvular leak and vascular complications was higher in the upper CS quartiles for aortic valve and ilio-femoral arteries. Conclusions Aortic valve and vascular CS could help to predict post-TAVI early complications.

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Section: Discussionmentioning

confidence: 99%

Aortic valve and vascular calcium score in pre-TAVI CT: correlation with early post-procedural complications

et al. 2023

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“…AI’s ability to process and analyze complex, multi-layered data efficiently makes it essential for real-time processing in digital immersive environments, ensuring seamless user experiences. With increasing quality and resolution in medical imaging, 3D reconstruction of organs comes within clinical reach [ 94 ]. Medical imaging provides many 1D (ECG) and 2D views of the 3D heart (CT/MRI/ECHO DICOM), leaving the 3D interpretation to medical experts.…”

Section: Ethical Implications Of Ai In Interventional Cardiologymentioning

confidence: 99%

Advancements in artificial intelligence-driven techniques for interventional cardiology

Rudnicka,

Pręgowska,

Glądys

et al. 2024

Cardiol J.

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This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.

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Application of artificial intelligence-assisted image diagnosis software based on volume data reconstruction technique in medical imaging practice teaching

Wang,

Huai,

et al. 2024

BMC Med Educ

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Background In medical imaging courses, due to the complexity of anatomical relationships, limited number of practical course hours and instructors, how to improve the teaching quality of practical skills and self-directed learning ability has always been a challenge for higher medical education. Artificial intelligence-assisted diagnostic (AISD) software based on volume data reconstruction (VDR) technique is gradually entering radiology. It converts two-dimensional images into three-dimensional images, and AI can assist in image diagnosis. However, the application of artificial intelligence in medical education is still in its early stages. The purpose of this study is to explore the application value of AISD software based on VDR technique in medical imaging practical teaching, and to provide a basis for improving medical imaging practical teaching. Methods Totally 41 students majoring in clinical medicine in 2017 were enrolled as the experiment group. AISD software based on VDR was used in practical teaching of medical imaging to display 3D images and mark lesions with AISD. Then annotations were provided and diagnostic suggestions were given. Also 43 students majoring in clinical medicine from 2016 were chosen as the control group, who were taught with the conventional film and multimedia teaching methods. The exam results and evaluation scales were compared statistically between groups. Results The total skill scores of the test group were significantly higher compared with the control group (84.51 ± 3.81 vs. 80.67 ± 5.43). The scores of computed tomography (CT) diagnosis (49.93 ± 3.59 vs. 46.60 ± 4.89) and magnetic resonance (MR) diagnosis (17.41 ± 1.00 vs. 16.93 ± 1.14) of the experiment group were both significantly higher. The scores of academic self-efficacy (82.17 ± 4.67) and self-directed learning ability (235.56 ± 13.50) of the group were significantly higher compared with the control group (78.93 ± 6.29, 226.35 ± 13.90). Conclusions Applying AISD software based on VDR to medical imaging practice teaching can enable students to timely obtain AI annotated lesion information and 3D images, which may help improve their image reading skills and enhance their academic self-efficacy and self-directed learning abilities.

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Artificial Intelligence-Based Spiral CT 3D Reconstruction in Transcatheter Aortic Valve Implantation

Cited by 4 publications

References 32 publications

Aortic valve and vascular calcium score in pre-TAVI CT: correlation with early post-procedural complications

Aortic valve and vascular calcium score in pre-TAVI CT: correlation with early post-procedural complications

Advancements in artificial intelligence-driven techniques for interventional cardiology

Application of artificial intelligence-assisted image diagnosis software based on volume data reconstruction technique in medical imaging practice teaching

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