Pre-surgical and Post-surgical Aortic Aneurysm Maximum Diameter Measurement: Full Automation by Artificial Intelligence

Adam, Chloé; Fabre, Dominique; Mougin, Justine; Zins, Marc; Azarine, Arshid; Ardon, Roberto; d’Assignies, Gaspard; Haulon, Stéphan

doi:10.1016/j.ejvs.2021.07.013

Cited by 36 publications

(8 citation statements)

References 13 publications

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“…More recently the work by Caradu et al 38 proposed a deep learning algorithm trained to segment preoperative infrarenal aortic aneurysm CT volumes effectively, with a mean DICE score of 0.95 on 100 scans. Adam et al 39 introduced an automated method named Augmented Radiology for Vascular Aneurysm (ARVA), trained on a large dataset of 489 CT volumes (a combination of both preoperative and postoperative scans), dedicated to segmenting the entire aorta from the ascending portion to the iliac arteries, with a mean DICE score of 0.95 on preoperative scans and 0.93 on postoperative scans, thus comparable to ours, but achieved with fewer training data. This study nevertheless confirms that the use of an initial variational method can reduce the need for larger datasets.…”

Section: Resultsmentioning

confidence: 61%

Evaluation of a hybrid pipeline for automated segmentation of solid lesions based on mathematical algorithms and deep learning

Burrows

Chen

Guo

et al. 2022

Sci Rep

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We evaluate the accuracy of an original hybrid segmentation pipeline, combining variational and deep learning methods, in the segmentation of CT scans of stented aortic aneurysms, abdominal organs and brain lesions. The hybrid pipeline is trained on 50 aortic CT scans and tested on 10. Additionally, we trained and tested the hybrid pipeline on publicly available datasets of CT scans of abdominal organs and MR scans of brain tumours. We tested the accuracy of the hybrid pipeline against a gold standard (manual segmentation) and compared its performance to that of a standard automated segmentation method with commonly used metrics, including the DICE and JACCARD and volumetric similarity (VS) coefficients, and the Hausdorff Distance (HD). Results. The hybrid pipeline produced very accurate segmentations of the aorta, with mean DICE, JACCARD and VS coefficients of: 0.909, 0.837 and 0.972 in thrombus segmentation and 0.937, 0.884 and 0.970 for stent and lumen segmentation. It consistently outperformed the standard automated method. Similar results were observed when the hybrid pipeline was trained and tested on publicly available datasets, with mean DICE scores of: 0.832 on brain tumour segmentation, and 0.894/0.841/0.853/0.847/0.941 on left kidney/right kidney/spleen/aorta/liver organ segmentation.

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Section: Resultsmentioning

confidence: 61%

Evaluation of a hybrid pipeline for automated segmentation of solid lesions based on mathematical algorithms and deep learning

Burrows

Chen

Guo

et al. 2022

Sci Rep

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“… 2 Nevertheless, several studies have recently demonstrated the interest in CNN to develop a fully automatic segmentation of AAA. 3 , 4 , 5 These studies showed good accuracy for the methods compared with human experts and demonstrated the feasibility of using AI for automatic measurement of the AAA maximal diameter. 4 , 5 , 6 Hence, CNN offers perspectives to develop applications oriented toward screening and identification of AAA and new tools to facilitate its anatomic characterization, which could improve preoperative planning and follow-up.…”

mentioning

confidence: 71%

“… 3 , 4 , 5 These studies showed good accuracy for the methods compared with human experts and demonstrated the feasibility of using AI for automatic measurement of the AAA maximal diameter. 4 , 5 , 6 Hence, CNN offers perspectives to develop applications oriented toward screening and identification of AAA and new tools to facilitate its anatomic characterization, which could improve preoperative planning and follow-up. As stated by the authors, in addition to the development of advanced imaging analysis, machine learning has the potential to build predictive models of patients' outcomes.…”

mentioning

confidence: 71%

Convolutional neural network for automatic detection and characterization of abdominal aortic aneurysm

Lareyre¹,

Adam²,

Carrier³

et al. 2023

Journal of Vascular Surgery Cases, Innovations and Techniques

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“…For this reason, the majority of AI techniques in AAA management are CTA-based. Adam et al employed a DL approach (Augmented Radiology for Vascular Aneurysm-ARVA) to detect an AAA and measure its maximal diameter in 489 CTA scans (118). ARVA outcomes were compared to a reference expert, demonstrating a median absolute difference of 1.2 mm, while the median absolute differences of another six experts compared to the same reference expert were 1-2 mm.…”

Section: Abdominal Aortic Aneurysmmentioning

confidence: 99%

Artificial intelligence in atherosclerotic disease: Applications and trends

Kampaktsis

Emfietzoglou²,

Shehhi³

et al. 2023

Front. Cardiovasc. Med.

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Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.

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Pre-surgical and Post-surgical Aortic Aneurysm Maximum Diameter Measurement: Full Automation by Artificial Intelligence

Cited by 36 publications

References 13 publications

Evaluation of a hybrid pipeline for automated segmentation of solid lesions based on mathematical algorithms and deep learning

Evaluation of a hybrid pipeline for automated segmentation of solid lesions based on mathematical algorithms and deep learning

Convolutional neural network for automatic detection and characterization of abdominal aortic aneurysm

Artificial intelligence in atherosclerotic disease: Applications and trends

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