Machine Learning and Deep Neural Networks

Eberhard, Matthias; Alkadhi, Hatem

doi:10.1097/rti.0000000000000482

Cited by 25 publications

(5 citation statements)

References 28 publications

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“…These protocols may need artificial intelligence to combine all information efficiently. 19 A major limitation of this study is the restricted IDR, with a maximum of 1.8 g I/s due to the per protocol injection rate of 6 mL/s. This restricted IDR was a limiting factor for patients in the higher body weight group, who were also scanned with a high kV.…”

Section: Discussionmentioning

confidence: 99%

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Patient-tailored Contrast Delivery Protocols for Computed Tomography Coronary Angiography

Boogert

Lopes

Lobé

et al. 2021

Journal of Thoracic Imaging

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Purpose:The first objective of this study was to evaluate the efficacy of a patient-tailored contrast delivery protocol for coronary computed tomography angiography (CTCA), in terms of diagnostic coronary attenuation and total iodine load (TIL), by adjusting the iodine delivery rate (IDR) via dilution for body weight and tube voltage (kV), as compared with a protocol with a fixed bolus of contrast in a clinical setting. The secondary objective was to assess the association between the test-bolus data and luminal attenuation in CTCA.Materials and Methods: Patients who underwent CTCA with fixed IDR contrast delivery (cohort 1) or with IDR adjusted for body weight and kV settings (70 to 120 kV) (cohort 2) were included, and compared for intravascular luminal attenuation and TIL. The association between intravascular luminal attenuation and testbolus scan data was investigated with linear regression. Results:In cohort 1 (176 patients), the mean luminal attenuation differed markedly between kV categories, whereas in cohort 2 (154 patients), there were no marked differences. The mean TIL reduced significantly (20.1 ± 1.2 g in cohort 1, 17.7 ± 3.0 g in cohort 2, P < 0.001). The peak height of the test-bolus scan was independently associated with luminal attenuation in the ascending aorta, with a 0.58 HU increase per HU peak-height increase (SE = 0.18, P < 0.001). Conclusion:Clinical implementation of a patient-tailored contrast delivery protocol for CTCA, adjusted for body weight and kV, improves luminal attenuation and significantly reduces the TIL. The peak height of the test-bolus scan is associated with luminal attenuation in the ascending aorta in the CTCA scan.

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Section: Discussionmentioning

confidence: 99%

“…The test-bolus scan data can be used, in addition to other patient characteristics and CT scanner settings, to further improve the precision of patient-specific contrast delivery protocols. These protocols may need artificial intelligence to combine all information efficiently 19…”

Section: Discussionmentioning

confidence: 99%

Patient-tailored Contrast Delivery Protocols for Computed Tomography Coronary Angiography

Boogert

Lopes

Lobé

et al. 2021

Journal of Thoracic Imaging

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“…AI algorithms may detect sub-optimal or even non-diagnostic images due to motion artifact, insufficient coverage, inadequate contrast enhancement, or sub-optimal image quality in general, helping technicians to correct these errors before finishing the imaging examination [40] The requirement of contrast medium is a limitation of CTA as well as a determining factor in the success of the examination. In this respect, Korporaal et al evaluated the robustness of an ML-based algorithm to optimize the scan timing tailored to each patient.…”

Section: Ai In Pre-acquisition and Acquisitionmentioning

confidence: 99%

Artificial intelligence in computed tomography plaque characterization: A review

Cau

Flanders

Mannelli

et al. 2021

European Journal of Radiology

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Cardiovascular disease (CVD) is associated with high mortality around the world. Prevention and early diagnosis are key targets in reducing the socio-economic burden of CVD.Artificial intelligence (AI) has experienced a steady growth due to technological innovations that have to lead to constant development. Several AI algorithms have been applied to various aspects of CVD in order to improve the quality of image acquisition and reconstruction and, at the same time adding information derived from the images to create strong predictive models. In computed tomography angiography (CTA), AI can offer solutions for several parts of plaque analysis, including an automatic assessment of the degree of stenosis and characterization of plaque morphology. A growing body of evidence demonstrates a correlation between some type of plaques, so-called high-risk plaque or vulnerable plaque, and cardiovascular events, independent of the degree of stenosis. The radiologist must apprehend and participate actively in developing and implementing AI in current clinical practice.In this current overview on the existing AI literature, we describe the strengths, limitations, recent applications, and promising developments of employing AI to plaque characterization with CT.

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“…AI algorithms can be applied to improve parameters relating to image acquisition, contrast medium injection, and radiation dose optimisation in the acquisition of thoracic CT [100].…”

Section: Computed Tomography Pulmonary Angiography (Ctpa)mentioning

confidence: 99%

From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation?

Gopalan

Gibbs

2020

Diagnostics

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Imaging plays a cardinal role in the diagnosis and management of diseases of the pulmonary circulation. Behind the picture itself, every digital image contains a wealth of quantitative data, which are hardly analysed in current routine clinical practice and this is now being transformed by radiomics. Mathematical analyses of these data using novel techniques, such as vascular morphometry (including vascular tortuosity and vascular volumes), blood flow imaging (including quantitative lung perfusion and computational flow dynamics), and artificial intelligence, are opening a window on the complex pathophysiology and structure–function relationships of pulmonary vascular diseases. They have the potential to make dramatic alterations to how clinicians investigate the pulmonary circulation, with the consequences of more rapid diagnosis and a reduction in the need for invasive procedures in the future. Applied to multimodality imaging, they can provide new information to improve disease characterization and increase diagnostic accuracy. These new technologies may be used as sophisticated biomarkers for risk prediction modelling of prognosis and for optimising the long-term management of pulmonary circulatory diseases. These innovative techniques will require evaluation in clinical trials and may in themselves serve as successful surrogate end points in trials in the years to come.

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Machine Learning and Deep Neural Networks

Cited by 25 publications

References 28 publications

Patient-tailored Contrast Delivery Protocols for Computed Tomography Coronary Angiography

Patient-tailored Contrast Delivery Protocols for Computed Tomography Coronary Angiography

Artificial intelligence in computed tomography plaque characterization: A review

From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation?

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