Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography

Spinella, Giovanni; Fantazzini, Alice; Finotello, Alice; Vincenzi, Elena; Boschetti, Gian Antonio; Brutti, Francesca; Magliocco, Marco; Pane, Bianca; Basso, Curzio; Conti, Michele

doi:10.1007/s10278-023-00866-1

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…With the use of AI, the mean segmentation time per patient was reduced to 7.4 min as opposed to 25-40 min per patient with the human manual segmentation method (Figure 30) [200,201]. This finding is similar to a recent study that showed the feasibility of using AI to screen for AAA [202]. Spinella et al proposed a pipeline for the automatic segmentation of aortic lumen and thrombus and the calculation of maximal aortic aneurysm in 48 patients with AAA and 25 control patients.…”

Section: Ai/ml/dl In Abdominal Aortic Aneurysm and Aortic Dissectionsupporting

confidence: 83%

Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment

Sun,

Silberstein,

Vaccarezza

2024

JCDD

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Cardiovascular CT is being widely used in the diagnosis of cardiovascular disease due to the rapid technological advancements in CT scanning techniques. These advancements include the development of multi-slice CT, from early generation to the latest models, which has the capability of acquiring images with high spatial and temporal resolution. The recent emergence of photon-counting CT has further enhanced CT performance in clinical applications, providing improved spatial and contrast resolution. CT-derived fractional flow reserve is superior to standard CT-based anatomical assessment for the detection of lesion-specific myocardial ischemia. CT-derived 3D-printed patient-specific models are also superior to standard CT, offering advantages in terms of educational value, surgical planning, and the simulation of cardiovascular disease treatment, as well as enhancing doctor–patient communication. Three-dimensional visualization tools including virtual reality, augmented reality, and mixed reality are further advancing the clinical value of cardiovascular CT in cardiovascular disease. With the widespread use of artificial intelligence, machine learning, and deep learning in cardiovascular disease, the diagnostic performance of cardiovascular CT has significantly improved, with promising results being presented in terms of both disease diagnosis and prediction. This review article provides an overview of the applications of cardiovascular CT, covering its performance from the perspective of its diagnostic value based on traditional lumen assessment to the identification of vulnerable lesions for the prediction of disease outcomes with the use of these advanced technologies. The limitations and future prospects of these technologies are also discussed.

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Section: Ai/ml/dl In Abdominal Aortic Aneurysm and Aortic Dissectionsupporting

confidence: 83%

Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment

Sun,

Silberstein,

Vaccarezza

2024

JCDD

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Deep Learning Models for Aorta Segmentation in Computed Tomography Images: A Systematic Review And Meta-Analysis

Wang,

Tzeng,

Hong

et al. 2024

J. Med. Biol. Eng.

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Purpose This systematic review and meta-analysis was conducted to evaluate the usefulness of deep learning (DL) models for aorta segmentation in computed tomography (CT) images. Methods Adhering to 2020 PRISMA guidelines, we systematically searched PubMed, Embase, and Web of Science for studies published up to March 13, 2024, that used DL models for aorta segmentation in adults’ chest CT images. We excluded studies that did not use DL models, involved nonhuman subjects or aortic diseases (aneurysms and dissections), or lacked essential data for meta-analysis. Segmentation performance was evaluated primarily in terms of Dice scores. Subgroup analyses were performed to identify variations related to geographical location and methodology. Results Our review of 16 studies indicated that DL models achieve high segmentation accuracy, with a pooled Dice score of 96%. We further noted geographical variations in model performance but no significant publication bias, according to the Egger test. Conclusion DL models facilitate aorta segmentation in CT images, and they can therefore guide accurate, efficient, and standardized diagnosis and treatment planning for cardiovascular diseases. Future studies should address the current challenges to enhance model generalizability and evaluate clinical benefits and thus expand the application of DL models in clinical practice.

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One-year follow-up after active aortic aneurysm sac treatment with shape memory polymer devices during endovascular aneurysm repair

Holden,

Hill,

Khashram

et al. 2024

Journal of Vascular Surgery

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Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography

Cited by 5 publications

References 37 publications

Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment

Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment

Deep Learning Models for Aorta Segmentation in Computed Tomography Images: A Systematic Review And Meta-Analysis

One-year follow-up after active aortic aneurysm sac treatment with shape memory polymer devices during endovascular aneurysm repair

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