Development of a convolutional neural network to detect abdominal aortic aneurysms

Camara, Justin; Tomihama, Roger T.; Pop, Andrew; Shedd, Matthew P.; Dobrowski, Brandon S.; Knox, Cole; Abou-Zamzam, Ahmed M.; Kiang, Sharon C.

doi:10.1016/j.jvscit.2022.04.003

Cited by 18 publications

(14 citation statements)

References 31 publications

(39 reference statements)

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“…21 22,23 Other groups have proposed models for the diagnosis of abdominal aortic aneurysm (AAA) with diagnostic accuracy of up to 99%. [25][26][27] These models collectively highlight the potential for machine learning applications in vascular surgery. A similarity of these prior studies is that each model used CNN-based architecture.…”

Section: Resultsmentioning

confidence: 99%

Volumetric Analysis of Acute Uncomplicated Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model

Krebs,

Imran,

Fazzone

et al. 2024

Preprint

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Introduction: Machine learning techniques have shown excellent performance in 3D medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) utilizing SVS/STS-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between auTBAD patients based on rate of aortic growth. Methods: Patients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two CT scans were analyzed: (1) the baseline CTA at index admission, and (2) either the most recent surveillance CTA, or the most recent CTA prior to an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase >5mm/year) and no/slow growth (diameter increase <5mm/year). Deidentified images were imported into an open-source software package for medical image analysis and randomly partitioned into training(80%), validation(10%), and testing(10%) cohorts. Training datasets were manually segmented based on SVS/STS criteria. A custom segmentation framework was used to generate the predicted segmentation output and aortic zone volumes. Results: Of 59 patients identified for inclusion, rapid growth was observed in 33 (56%) patients and no/slow growth was observed in 26 (44%) patients. There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (p>0.05 for all). Median duration between baseline and interval CT was 1.07 years (IQR 0.38-2.57). Post-discharge aortic intervention was performed in 13 (22%) of patients at a mean of 1.5+/- 1.2 years, with no difference between groups (p>0.05). In both groups, all zones of the thoracic and abdominal aorta increased in volume over time, with the largest relative increase in Zone 5 with a median 24% increase (IQR 4.4-37). Baseline zone 3 volumes were larger in the no/slow growth (6v3) than the rapid growth group (5v3) (p=0.03). There were no other differences in baseline zone volumes between groups (p>0.05 for all). Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in Zones 4 (0.82), 5(0.88), and 9(0.91). Conclusions: To our knowledge this is the first description of an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open-source, trained model demonstrates high concordance to the manually segmented aortas with the strongest performance in zones 4, 5, and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between rapid growth and no/slow growth patients.

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Section: Resultsmentioning

confidence: 99%

Volumetric Analysis of Acute Uncomplicated Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model

Krebs,

Imran,

Fazzone

et al. 2024

Preprint

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“…DL technologies were utilised in 76 studies (60·3%) using, as source of information, medical records (23), administrative, and genome database (2 and 1, respectively), clinical parameters (14), biological data (2), imaging and device-derived data (69 and 2, respectively) with the aim of identification (22), classification (4), medical image segmentation (44), prediction (22) and to improve understanding of pathophysiological processes (2).…”

Section: Resultsmentioning

confidence: 99%

Source of data for artificial intelligence applications in vascular surgery - a scoping review

Powezka,

Slater,

Wall

et al. 2023

Preprint

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BackgroundApplications of Artificial Intelligence (AI) are gaining traction in healthcare, including vascular surgery. While most healthcare data is in the form of narrative text or audio recordings, natural language processing (NLP) offers the ability to extract structured information from unstructured medical records. This can be used to develop more accurate risk prediction models, and to identify patients for trials to improve recruitment. The goals of this scoping review were to determine the source of data used to develop AI-based algorithms with emphasis on natural language processing, establish their application in different areas of vascular surgery and identify a target audience of published journals.MethodsA literature search was carried out using PubMed, EMBASE, and Google Scholar database from January 1996 to March 2023. Following screening, 342 peer-reviewed articles met the eligibility criteria.FindingsAmong the 342 articles meeting eligibility criteria, the majority (191) were published after 2020. NLP algorithms were described in 34 papers, while 115 and 193 papers focused on machine learning (ML) and deep learning (DL), respectively. Two-thirds (67.25%) were published in non-clinical journals. The AI-based algorithms found widest application in research related to aorta and its branches (126 articles), followed by carotid disease (85), and peripheral arterial disease (65). Image-based data were utilised in 216 articles, while 153 and 85 papers relied on medical records, and clinical parameters. The AI algorithms were used for predictive modelling (123 papers), medical image segmentation (118), and to aid identification, detection, and diagnosis (103). Only 18 publications aimed to enhance the understanding of vascular disease pathophysiology.InterpretationUtilisation of different data sources and AI technologies depends on the purpose of the undertaken research. Despite the abundance of available of textual data, the NLP is disproportionally underutilised AI sub-domain.FundingNone.

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“…As described in our prior work, for the initial CNN model development, axial reconstructions from all selected CT scans were exported in noncompressed JPEG format at preset window widths and levels. 11 All axial reconstruction images were resized to 512 × 512 pixels. A total of 6175 axial images containing infrarenal AAAs were sorted.…”

Section: Methodsmentioning

confidence: 99%

“…The output of the model is a binary classifier that automatically recognizes the presence or absence of an AAA on computed tomography angiograms (CTAs) of the abdomen and pelvis. 11 The AI model accuracy will be analyzed on simulated real-world confounding variables such as data set size (segmented, balanced, or unbalanced), aneurysm size, extra-abdominal extension, dissections, and mural thrombus.…”

mentioning

confidence: 99%

Machine learning analysis of confounding variables of a convolutional neural network specific for abdominal aortic aneurysms

Tomihama

Camara²,

Kiang³

2023

JVS-Vascular Science

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Development of a convolutional neural network to detect abdominal aortic aneurysms

Cited by 18 publications

References 31 publications

Volumetric Analysis of Acute Uncomplicated Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model

Volumetric Analysis of Acute Uncomplicated Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model

Source of data for artificial intelligence applications in vascular surgery - a scoping review

Machine learning analysis of confounding variables of a convolutional neural network specific for abdominal aortic aneurysms

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