Towards the Clinical Management of Cardiac Involvement in Systemic Inflammatory Conditions—a Central Role for CMR

Winau, L.; Nagel, Eike; Herrmann, Eva; Püntmann, Valentina O.

doi:10.1007/s12410-018-9451-7

Cited by 6 publications

(2 citation statements)

References 89 publications

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“…Early detection of MS with high sensitivity would be of significant benefit, resulting in early therapy initiation and thus reducing individual suffering and costs on a health perspective level. High resolution cardiac imaging provides the information necessary to make appropriate therapeutic decisions ( 23 ) and, therefore, magnetic resonance imaging (MRI) is considered as a gold standard for non-invasive diagnosis of myocardial tissue conditions ( 24 ). Therefore, guidelines such as the ESC guideline for the diagnosis and treatment of acute and chronic heart failure, ( 25 ) recommend cardiac MRI using Late Gadolinium Enhancement for reliable diagnoses of MS.…”

Section: Use Casesmentioning

confidence: 99%

The Disruption of Trust in the Digital Transformation Leading to Health 4.0

Guckert¹,

Milanović²,

Hannig³

et al. 2022

Front. Digit. Health

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The specification and application of policies and guidelines for public health, medical education and training, and screening programmes for preventative medicine are all predicated on trust relationships between medical authorities, health practitioners and patients. These relationships are in turn predicated on a verbal contract that is over two thousand years old. The impact of information and communication technology (ICT), underpinning Health 4.0, has the potential to disrupt this analog relationship in several dimensions; but it also presents an opportunity to strengthen it, and so to increase the take-up and effectiveness of new policies. This paper develops an analytic framework for the trust relationships in Health 4.0, and through three use cases, assesses a medical policy, the introduction of a new technology, and the implications of that technology for the trust relationships. We integrate this assessment in a set of actionable recommendations, in particular that the trust framework should be part of the design methodology for developing and deploying medical applications. In a concluding discussion, we advocate that, in a post-pandemic world, IT to support policies and programmes to address widespread socio-medical problems with mental health, long Covid, physical inactivity and vaccine misinformation will be essential, and for that, strong trust relationships between all the stakeholders are absolutely critical.

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Section: Use Casesmentioning

confidence: 99%

The Disruption of Trust in the Digital Transformation Leading to Health 4.0

Guckert¹,

Milanović²,

Hannig³

et al. 2022

Front. Digit. Health

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“…Non-invasive state of the art methods for diagnosis of MS all suffer from drawbacks impeding their use in comprehensible screening. Magnetic resonance imaging (MRI) is considered as the gold standard for diagnosing MS by achieving high sensitivity and specificity (Winau et al 2018). Intramyocardial late Gadolinium enhancement (LE) of the contrast agent can be visualised by MRI and has been shown to serve as an indicator for scar tissue (Kim et al 1999).…”

Section: Introductionmentioning

confidence: 99%

Detecting myocardial scar using electrocardiogram data and deep neural networks

Gumpfer

Grün

Hannig

et al. 2020

Biological Chemistry

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Ischaemic heart disease is among the most frequent causes of death. Early detection of myocardial pathologies can increase the benefit of therapy and reduce the number of lethal cases. Presence of myocardial scar is an indicator for developing ischaemic heart disease and can be detected with high diagnostic precision by magnetic resonance imaging. However, magnetic resonance imaging scanners are expensive and of limited availability. It is known that presence of myocardial scar has an impact on the well-established, reasonably low cost, and almost ubiquitously available electrocardiogram. However, this impact is non-specific and often hard to detect by a physician. We present an artificial intelligence based approach — namely a deep learning model — for the prediction of myocardial scar based on an electrocardiogram and additional clinical parameters. The model was trained and evaluated by applying 6-fold cross-validation to a dataset of 12-lead electrocardiogram time series together with clinical parameters. The proposed model for predicting the presence of scar tissue achieved an area under the curve score, sensitivity, specificity, and accuracy of 0.89, 70.0, 84.3, and 78.0%, respectively. This promisingly high diagnostic precision of our electrocardiogram-based deep learning models for myocardial scar detection may support a novel, comprehensible screening method.

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