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  1. Article ; Online: Advancing Risk Stratification in Pulmonary Arterial Hypertension Through Cardiac MRI: The Need for Collaboration and Standardization.

    Frantz, Robert P / Swift, Andrew J

    Chest

    2024  Volume 165, Issue 1, Page(s) 12–13

    MeSH term(s) Humans ; Pulmonary Arterial Hypertension/diagnostic imaging ; Familial Primary Pulmonary Hypertension ; Magnetic Resonance Imaging ; Reference Standards ; Risk Assessment
    Language English
    Publishing date 2024-01-09
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 1032552-9
    ISSN 1931-3543 ; 0012-3692
    ISSN (online) 1931-3543
    ISSN 0012-3692
    DOI 10.1016/j.chest.2023.10.038
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  2. Article ; Online: Importance of cardiac magnetic resonance imaging assessment of left ventricular filling pressure at resting state.

    Garg, Pankaj / Swift, Andrew J

    European heart journal

    2022  Volume 43, Issue 36, Page(s) 3495

    MeSH term(s) Heart Atria/physiopathology ; Humans ; Magnetic Resonance Imaging ; Ventricular Dysfunction, Left/diagnostic imaging ; Ventricular Dysfunction, Left/physiopathology
    Language English
    Publishing date 2022-08-09
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 603098-1
    ISSN 1522-9645 ; 0195-668X
    ISSN (online) 1522-9645
    ISSN 0195-668X
    DOI 10.1093/eurheartj/ehac420
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  3. Article ; Online: Uncertainty Estimation for Heatmap-Based Landmark Localization.

    Schobs, Lawrence Andrew / Swift, Andrew J / Lu, Haiping

    IEEE transactions on medical imaging

    2023  Volume 42, Issue 4, Page(s) 1021–1034

    Abstract: Automatic anatomical landmark localization has made great strides by leveraging deep learning methods in recent years. The ability to quantify the uncertainty of these predictions is a vital component needed for these methods to be adopted in clinical ... ...

    Abstract Automatic anatomical landmark localization has made great strides by leveraging deep learning methods in recent years. The ability to quantify the uncertainty of these predictions is a vital component needed for these methods to be adopted in clinical settings, where it is imperative that erroneous predictions are caught and corrected. We propose Quantile Binning, a data-driven method to categorize predictions by uncertainty with estimated error bounds. Our framework can be applied to any continuous uncertainty measure, allowing straightforward identification of the best subset of predictions with accompanying estimated error bounds. We facilitate easy comparison between uncertainty measures by constructing two evaluation metrics derived from Quantile Binning. We compare and contrast three epistemic uncertainty measures (two baselines, and a proposed method combining aspects of the two), derived from two heatmap-based landmark localization model paradigms (U-Net and patch-based). We show results across three datasets, including a publicly available Cephalometric dataset. We illustrate how filtering out gross mispredictions caught in our Quantile Bins significantly improves the proportion of predictions under an acceptable error threshold. Finally, we demonstrate that Quantile Binning remains effective on landmarks with high aleatoric uncertainty caused by inherent landmark ambiguity, and offer recommendations on which uncertainty measure to use and how to use it. The code and data are available at https://github.com/schobs/qbin.
    MeSH term(s) Uncertainty ; Anatomic Landmarks
    Language English
    Publishing date 2023-04-03
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2022.3222730
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  4. Article ; Online: How should studies using AI be reported? lessons from a systematic review in cardiac MRI.

    Maiter, Ahmed / Salehi, Mahan / Swift, Andrew J / Alabed, Samer

    Frontiers in radiology

    2023  Volume 3, Page(s) 1112841

    Abstract: Recent years have seen a dramatic increase in studies presenting artificial intelligence (AI) tools for cardiac imaging. Amongst these are AI tools that undertake segmentation of structures on cardiac MRI (CMR), an essential step in obtaining clinically ... ...

    Abstract Recent years have seen a dramatic increase in studies presenting artificial intelligence (AI) tools for cardiac imaging. Amongst these are AI tools that undertake segmentation of structures on cardiac MRI (CMR), an essential step in obtaining clinically relevant functional information. The quality of reporting of these studies carries significant implications for advancement of the field and the translation of AI tools to clinical practice. We recently undertook a systematic review to evaluate the quality of reporting of studies presenting automated approaches to segmentation in cardiac MRI (Alabed et al. 2022 Quality of reporting in AI cardiac MRI segmentation studies-a systematic review and recommendations for future studies.
    Language English
    Publishing date 2023-01-30
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2673-8740
    ISSN (online) 2673-8740
    DOI 10.3389/fradi.2023.1112841
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  5. Article ; Online: Cardiovascular magnetic resonance can improve the precision for left ventricular filling pressure assessment.

    Gosling, Rebecca / Swift, Andrew J / Garg, Pankaj

    European heart journal

    2022  Volume 44, Issue 5, Page(s) 427–428

    MeSH term(s) Humans ; Pulmonary Wedge Pressure ; Magnetic Resonance Imaging ; Magnetic Resonance Spectroscopy ; Heart Atria
    Language English
    Publishing date 2022-10-27
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 603098-1
    ISSN 1522-9645 ; 0195-668X
    ISSN (online) 1522-9645
    ISSN 0195-668X
    DOI 10.1093/eurheartj/ehac740
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  6. Article ; Online: CT Pulmonary Angiography in Chronic Thromboembolic Disease: Where Do We Stand?

    Swift, Andrew J / Rajaram, Smitha

    Radiology

    2020  Volume 296, Issue 2, Page(s) 430–431

    MeSH term(s) Angiography ; Chronic Disease ; Computed Tomography Angiography ; Humans ; Thromboembolism
    Language English
    Publishing date 2020-05-19
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.2020201344
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  7. Article: Characterisation of the octogenarians presenting to the diagnostic heart failure clinic: SHEAF registry.

    Thompson, Luke / Carr, Fiona / Rogers, Dominic / Lewis, Nigel / Charalampopoulos, Athanasios / Fent, Graham / Garg, Pankaj / Swift, Andrew J / Al-Mohammad, Abdallah

    Open heart

    2024  Volume 11, Issue 1

    Abstract: Introduction: Heart failure (HF) incidence is increasing in older adults with high hospitalisation and mortality rates. Treatment is complicated by side effects and comorbidities. We investigated the clinical characteristics of octogenarians presenting ... ...

    Abstract Introduction: Heart failure (HF) incidence is increasing in older adults with high hospitalisation and mortality rates. Treatment is complicated by side effects and comorbidities. We investigated the clinical characteristics of octogenarians presenting to the HF clinic.
    Methods: Data were collected on octogenarians (80-89 years) referred to the HF clinic in two periods. The data included demographics, HF phenotype, comorbidities, symptoms and treatment. We investigate the temporal changes in clinical characteristics using χ
    Results: Data were collected in April 2012 to January 2014 and in June 2021 to December 2022. In this cross-sectional study of temporal data, 571 octogenarians were referred to the clinic in the latter period, in whom the prevalence of HF was 68.48% (391 patients). HF with preserved ejection fraction (HFpEF) was the most common phenotype and increased significantly compared with the first period (46.3% and 29.2%, p<0.001). Frailty, chronic kidney disease and ischaemic heart disease increased significantly versus the first period (p<0.001). During the second period, and following the consultation, of the patients with HF with reduced ejection fraction (HFrEF), 86.4% and 82.7% were on a beta blocker and on an ACE inhibitor/angiotensin receptor blocker/angiotensin receptor-neprilysin inhibitor, respectively. Clinical characteristics associated with further optimisations of HF pharmacological therapy in the HF clinic were: New York Heart Association (NYHA) functional class III and the presence of HFrEF phenotype CONCLUSIONS: With a prevalence of HF at 68% among the octogenarians referred to the HF clinic, HFpEF incidence is rising. The decision to optimise HF pharmacological treatment in octogenarians is driven by NYHA functional class III and the presence of HFrEF phenotype.
    MeSH term(s) Humans ; Heart Failure/diagnosis ; Heart Failure/epidemiology ; Heart Failure/physiopathology ; Heart Failure/drug therapy ; Aged, 80 and over ; Female ; Male ; Cross-Sectional Studies ; Registries ; Prevalence ; Stroke Volume/physiology ; Age Factors ; Incidence ; Comorbidity ; Risk Factors ; Ventricular Function, Left/physiology
    Language English
    Publishing date 2024-04-24
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Multicenter Study
    ZDB-ID 2747269-3
    ISSN 2053-3624
    ISSN 2053-3624
    DOI 10.1136/openhrt-2023-002584
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  8. Article ; Online: A transparent artificial intelligence framework to assess lung disease in pulmonary hypertension.

    Mamalakis, Michail / Dwivedi, Krit / Sharkey, Michael / Alabed, Samer / Kiely, David / Swift, Andrew J

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 3812

    Abstract: Recent studies have recognized the importance of characterizing the extent of lung disease in pulmonary hypertension patients by using Computed Tomography. The trustworthiness of an artificial intelligence system is linked with the depth of the ... ...

    Abstract Recent studies have recognized the importance of characterizing the extent of lung disease in pulmonary hypertension patients by using Computed Tomography. The trustworthiness of an artificial intelligence system is linked with the depth of the evaluation in functional, operational, usability, safety and validation dimensions. The safety and validation of an artificial tool is linked to the uncertainty estimation of the model's prediction. On the other hand, the functionality, operation and usability can be achieved by explainable deep learning approaches which can verify the learning patterns and use of the network from a generalized point of view. We developed an artificial intelligence framework to map the 3D anatomical models of patients with lung disease in pulmonary hypertension. To verify the trustworthiness of the framework we studied the uncertainty estimation of the network's prediction, and we explained the learning patterns of the network. Therefore, a new generalized technique combining local explainable and interpretable dimensionality reduction approaches (PCA-GradCam, PCA-Shape) was developed. Our open-source software framework was evaluated in unbiased validation datasets achieving accurate, robust and generalized results.
    MeSH term(s) Humans ; Artificial Intelligence ; Hypertension, Pulmonary/diagnostic imaging ; Hypertension, Pulmonary/etiology ; Models, Anatomic ; Software ; Tomography, X-Ray Computed/methods ; Radiographic Image Interpretation, Computer-Assisted ; Lung/diagnostic imaging ; Lung Diseases/complications ; Lung Diseases/diagnosis
    Language English
    Publishing date 2023-03-07
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-30503-4
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  9. Article ; Online: Assessment of Right Ventricular Function-a State of the Art.

    Hameed, Abdul / Condliffe, Robin / Swift, Andrew J / Alabed, Samer / Kiely, David G / Charalampopoulos, Athanasios

    Current heart failure reports

    2023  Volume 20, Issue 3, Page(s) 194–207

    Abstract: Purpose of review: The right ventricle (RV) has a complex geometry and physiology which is distinct from the left. RV dysfunction and failure can be the aftermath of volume- and/or pressure-loading conditions, as well as myocardial and pericardial ... ...

    Abstract Purpose of review: The right ventricle (RV) has a complex geometry and physiology which is distinct from the left. RV dysfunction and failure can be the aftermath of volume- and/or pressure-loading conditions, as well as myocardial and pericardial diseases.
    Recent findings: Echocardiography, magnetic resonance imaging and right heart catheterisation can assess RV function by using several qualitative and quantitative parameters. In pulmonary hypertension (PH) in particular, RV function can be impaired and is related to survival. An accurate assessment of RV function is crucial for the early diagnosis and management of these patients. This review focuses on the different modalities and indices used for the evaluation of RV function with an emphasis on PH.
    MeSH term(s) Humans ; Ventricular Function, Right/physiology ; Heart Failure ; Hypertension, Pulmonary/diagnosis ; Echocardiography/methods ; Heart Ventricles/diagnostic imaging ; Ventricular Dysfunction, Right/diagnostic imaging
    Language English
    Publishing date 2023-06-05
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2151202-4
    ISSN 1546-9549 ; 1546-9530
    ISSN (online) 1546-9549
    ISSN 1546-9530
    DOI 10.1007/s11897-023-00600-6
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  10. Book ; Online: Uncertainty Estimation for Heatmap-based Landmark Localization

    Schobs, Lawrence / Swift, Andrew J. / Lu, Haiping

    2022  

    Abstract: Automatic anatomical landmark localization has made great strides by leveraging deep learning methods in recent years. The ability to quantify the uncertainty of these predictions is a vital component needed for these methods to be adopted in clinical ... ...

    Abstract Automatic anatomical landmark localization has made great strides by leveraging deep learning methods in recent years. The ability to quantify the uncertainty of these predictions is a vital component needed for these methods to be adopted in clinical settings, where it is imperative that erroneous predictions are caught and corrected. We propose Quantile Binning, a data-driven method to categorize predictions by uncertainty with estimated error bounds. Our framework can be applied to any continuous uncertainty measure, allowing straightforward identification of the best subset of predictions with accompanying estimated error bounds. We facilitate easy comparison between uncertainty measures by constructing two evaluation metrics derived from Quantile Binning. We compare and contrast three epistemic uncertainty measures (two baselines, and a proposed method combining aspects of the two), derived from two heatmap-based landmark localization model paradigms (U-Net and patch-based). We show results across three datasets, including a publicly available Cephalometric dataset. We illustrate how filtering out gross mispredictions caught in our Quantile Bins significantly improves the proportion of predictions under an acceptable error threshold. Finally, we demonstrate that Quantile Binning remains effective on landmarks with high aleatoric uncertainty caused by inherent landmark ambiguity, and offer recommendations on which uncertainty measure to use and how to use it. The code and data are available at https://github.com/schobs/qbin.

    Comment: 14 pages, in IEEE Transactions on Medical Imaging, 2022
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
    Subject code 310 ; 006
    Publishing date 2022-03-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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