Computational methods to automate the initial interpretation of lower extremity arterial Doppler and duplex carotid ultrasound studies

Luo, Xiao; Ara, Lena; Ding, Haoran; Rollins, David M.; Motaganahalli, Raghu L.; Sawchuk, Alan P.

doi:10.1016/j.jvs.2021.02.050

Cited by 13 publications

(7 citation statements)

References 27 publications

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“…Automatic analysis of images and videos is currently achieved by Computer Vision in numerous medical branches. In vascular diseases computational carotid plaque composition and stenosis analysis [5][6][7][8][9], automatic detection and characterization of ischemic brain lesion [10], 3-dimensional analysis of aneurysms morphology or post-endovascular repair endoleak surveillance [11][12][13][14][15], duplex scan (DS) or computed tomography angiography (CTA) and magnetic resonance imaging identification, localization and quantification of PAD disease [16][17][18] are examples of AI tools for optimizing surgical or endovascular strategies.…”

Section: Automatic Image Analysismentioning

confidence: 99%

“…CTA automatic peripheral vessel identification to localize and quantify disease has been reported in a study by Dai et al [17] where they used a convolutional neural network based on 17050 axial images in 265 PAD patients to classify above and below-knee artery stenosis with an accuracy greater then 90% in the majority of stenosis classes. Also, Doppler waveforms can be integrated in neural networks to detect and classify PAD with an accuracy of 0.69, 1, and 0.86 for mono, bi and triphasic waveforms, respectively [16].…”

Section: Peripheral Arterial Disease (Pad) and Aimentioning

confidence: 99%

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Current Applications and Future Perspectives of Artificial Intelligence in Vascular Surgery and Peripheral Artery Disease

Martelli,

Capoccia,

Di Francesco

et al. 2024

Preprint

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Artificial Intelligence (AI) made its first appearance in 1956, and since then it has progressively introduced itself in healthcare system and patients’ information and care. AI functions can be grouped under the following headings: Machine Learning (ML), Deep Learning (DL), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Computer Vision (CV). Chronic limb-threatening ischemia (CLTI) represents the last stage of peripheral artery disease (PAD), and has increased over recent years, together with the rise in prevalence of diabetes and population ageing. Nowadays AI grants the possibility of developing new diagnostic and treatment solutions in the vascular field, given the possibility of accessing clinical, biological, and imaging data. By assessing the vascular anatomy in every patient, as well as the burden of atherosclerosis, and classifying the level and degree of disease, sizing and planning the best endovascular treatment, defining the perioperative complications risk, integrating experiences and resources between different specialties, identifying latent PAD, thus offering evidence-based solutions and guiding surgeons in the choice of the best surgical technique, AI challenges the role of the physician’s experience in PAD treatment.

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Section: Automatic Image Analysismentioning

confidence: 99%

Section: Peripheral Arterial Disease (Pad) and Aimentioning

confidence: 99%

Current Applications and Future Perspectives of Artificial Intelligence in Vascular Surgery and Peripheral Artery Disease

Martelli,

Capoccia,

Di Francesco

et al. 2024

Preprint

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“…DUS provides a moderate to strong correlation with MRI [ 12 ]. In aortoiliac segments, DUS demonstrated 90% sensitivity, 85.0% specificity, 89.6% positive predictive value (PPV), 85.4% negative predictive value (NPV), and 88% accuracy [ 25 ]. To map the femoral arteries, the patient is positioned supine, and the procedure is repeated with the hip laterally rotated [ 20 ].…”

Section: Reviewmentioning

confidence: 99%

“…There is a strong degree of concordance between DUS and CTA in the right femoral (0.82 [0.69-0.95]) and left femoral (0.88 [0.76-1]) arteries [ 24 ]. In the femoropopliteal segments, DUS provided 94.8% sensitivity, 80.2% specificity, 94.1% PPV, 89.0% NPV, and 90.1% accuracy [ 25 ]. The best hemodynamic parameter to assess femoropopliteal stenosis is proximal PSV (>50%: proximal PSV=2.6, >70%: proximal PSV=3.3, >80%: proximal PSV=3.9) [ 26 ].…”

Section: Reviewmentioning

confidence: 99%

Peripheral Arterial Disease: A Narrative Review

Parwani¹,

Ahmed²,

Mahawar³

et al. 2023

Cureus

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Peripheral arterial disease (PAD) describes the partial or complete occlusion of blood flow in the distal arteries of the body. A decreased arterial patency may occur due to a reduction in the elasticity or diameter of the vessel. The goal of interventions is to decrease incidence and reduce complications by identifying and minimizing the primary causes. This paper discusses PAD affecting the aortoiliac, common femoral, and femoropopliteal arteries. In a significant portion of the population, PAD may lack usual symptoms such as limb pain, claudication, and diminished pulses. Imaging techniques become crucial to ensuring timely diagnosis, monitoring treatment effectiveness, and preventing recurrence. Duplex ultrasound (DUS) is a cheap and non-invasive preliminary technique to detect atherosclerotic plaques and grade arterial stenosis. Magnetic resonance angiography (MRA) provides the added advantage of minimizing artifacts. Digital subtraction angiography (DSA) remains the gold standard for grading the degree of stenosis but is only employed second-line to DUS or MRA due to the high dose of nephrotoxic contrast. Computed tomography angiography (CTA) is able to overcome the anatomical limitations of DUS and MRA and proves to be a suitable alternative to DSA in patients with renal disease. Preventative measures involve monitoring blood pressure, cholesterol levels, and tobacco usage. First-line treatment options include endovascular procedures as well as surgical interventions in cases of significant arterial involvement. Endovascular treatments involve the use of balloon angioplasty, drug-coated balloons, and drug-coated stents, to name a few, that serve as minimally invasive techniques to manage PAD. Surgical procedures, although more complex, are considered gold-standard treatment options for long and intricate lesions. Endovascular methods are generally preferred over surgical options as the complication risk is severely reduced and the rates of reintervention are comparable to surgical options.

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“…A reliable and useful indicator of atherosclerosis is the so-called intima-media (IM) thickness, defined as the distance from the lumenintima (LI) to the media-adventitia (MA) interface. Most studies have been devoted to the improvement of early atherosclerosis diagnosis; in this respect, three main issues are considered: detection [95,[102][103][104][105][106][107], segmentation [108][109][110][111][112][113][114][115], and classification [116][117][118][119][120][121][122][123][124][125][126][127][128].…”

Section: Arteriesmentioning

confidence: 99%

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Micucci

Iula

2022

Electronics

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Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.

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Computational methods to automate the initial interpretation of lower extremity arterial Doppler and duplex carotid ultrasound studies

Cited by 13 publications

References 27 publications

Current Applications and Future Perspectives of Artificial Intelligence in Vascular Surgery and Peripheral Artery Disease

Current Applications and Future Perspectives of Artificial Intelligence in Vascular Surgery and Peripheral Artery Disease

Peripheral Arterial Disease: A Narrative Review

Recent Advances in Machine Learning Applied to Ultrasound Imaging

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