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  1. Article ; Online: Human AI Teaming for Coronary CT Angiography Assessment

    Florian Andre / Philipp Fortner / Matthias Aurich / Sebastian Seitz / Ann-Kathrin Jatsch / Max Schöbinger / Michael Wels / Martin Kraus / Mehmet Akif Gülsün / Norbert Frey / Andre Sommer / Johannes Görich / Sebastian J. Buss

    Diagnostics, Vol 13, Iss 23, p

    Impact on Imaging Workflow and Diagnostic Accuracy

    2023  Volume 3574

    Abstract: As the number of coronary computed tomography angiography (CTA) examinations is expected to increase, technologies to optimize the imaging workflow are of great interest. The aim of this study was to investigate the potential of artificial intelligence ( ... ...

    Abstract As the number of coronary computed tomography angiography (CTA) examinations is expected to increase, technologies to optimize the imaging workflow are of great interest. The aim of this study was to investigate the potential of artificial intelligence (AI) to improve clinical workflow and diagnostic accuracy in high-volume cardiac imaging centers. A total of 120 patients (79 men; 62.4 (55.0–72.7) years; 26.7 (24.9–30.3) kg/m 2 ) undergoing coronary CTA were randomly assigned to a standard or an AI-based (human AI) coronary analysis group. Severity of coronary artery disease was graded according to CAD-RADS. Initial reports were reviewed and changes were classified. Both groups were similar with regard to age, sex, body mass index, heart rate, Agatston score, and CAD-RADS. The time for coronary CTA assessment (142.5 (106.5–215.0) s vs. 195.0 (146.0–265.5) s; p < 0.002) and the total reporting time (274.0 (208.0–377.0) s vs. 350 (264.0–445.5) s; p < 0.02) were lower in the human AI than in the standard group. The number of cases with no, minor, or CAD-RADS relevant changes did not differ significantly between groups (52, 7, 1 vs. 50, 8, 2; p = 0.80). AI-based analysis significantly improves clinical workflow, even in a specialized high-volume setting, by reducing CTA analysis and overall reporting time without compromising diagnostic accuracy.
    Keywords coronary artery disease ; coronary CT angiography ; artificial intelligence ; workflow ; human AI teaming ; computed tomography ; Medicine (General) ; R5-920
    Subject code 610
    Language English
    Publishing date 2023-11-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Normalizing Flows for Out-of-Distribution Detection

    Costin Florian Ciușdel / Lucian Mihai Itu / Serkan Cimen / Michael Wels / Chris Schwemmer / Philipp Fortner / Sebastian Seitz / Florian Andre / Sebastian Johannes Buß / Puneet Sharma / Saikiran Rapaka

    Applied Sciences, Vol 12, Iss 3839, p

    Application to Coronary Artery Segmentation

    2022  Volume 3839

    Abstract: Coronary computed tomography angiography (CCTA) is an effective imaging modality, increasingly accepted as a first-line test to diagnose coronary artery disease (CAD). The accurate segmentation of the coronary artery lumen on CCTA is important for the ... ...

    Abstract Coronary computed tomography angiography (CCTA) is an effective imaging modality, increasingly accepted as a first-line test to diagnose coronary artery disease (CAD). The accurate segmentation of the coronary artery lumen on CCTA is important for the anatomical, morphological, and non-invasive functional assessment of stenoses. Hence, semi-automated approaches are currently still being employed. The processing time for a semi-automated lumen segmentation can be reduced by pre-selecting vessel locations likely to require manual inspection and by submitting only those for review to the radiologist. Detection of faulty lumen segmentation masks can be formulated as an Out-of-Distribution (OoD) detection problem. Two Normalizing Flows architectures are investigated and benchmarked herein: a Glow-like baseline, and a proposed one employing a novel coupling layer. Synthetic mask perturbations are used for evaluating and fine-tuning the learnt probability densities. Expert annotations on a separate test-set are employed to measure detection performance relative to inter-user variability. Regular coupling-layers tend to focus more on local pixel correlations and to disregard semantic content. Experiments and analyses show that, in contrast, the proposed architecture is capable of capturing semantic content and is therefore better suited for OoD detection of faulty lumen segmentations. When compared against expert consensus, the proposed model achieves an accuracy of 78.6% and a sensitivity of 76%, close to the inter-user mean of 80.9% and 79%, respectively, while the baseline model achieves an accuracy of 64.3% and a sensitivity of 48%.
    Keywords out-of-distribution ; normalizing flows ; coronary computed tomography angiography ; lumen segmentation ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 004
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Publisher Correction

    Alexander Mühlberg / Alexander Katzmann / Volker Heinemann / Rainer Kärgel / Michael Wels / Oliver Taubmann / Félix Lades / Thomas Huber / Stefan Maurus / Julian Holch / Jean-Baptiste Faivre / Michael Sühling / Dominik Nörenberg / Martine Rémy-Jardin

    Scientific Reports, Vol 10, Iss 1, Pp 1-

    The Technome - A Predictive Internal Calibration Approach for Quantitative Imaging Biomarker Research

    2020  Volume 1

    Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper. ...

    Abstract An amendment to this paper has been published and can be accessed via a link at the top of the paper.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2020-07-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: The Technome - A Predictive Internal Calibration Approach for Quantitative Imaging Biomarker Research

    Alexander Mühlberg / Alexander Katzmann / Volker Heinemann / Rainer Kärgel / Michael Wels / Oliver Taubmann / Félix Lades / Thomas Huber / Stefan Maurus / Julian Holch / Jean-Baptiste Faivre / Michael Sühling / Dominik Nörenberg / Martine Rémy-Jardin

    Scientific Reports, Vol 10, Iss 1, Pp 1-

    2020  Volume 15

    Abstract: Abstract The goal of radiomics is to convert medical images into a minable data space by extraction of quantitative imaging features for clinically relevant analyses, e.g. survival time prediction of a patient. One problem of radiomics from computed ... ...

    Abstract Abstract The goal of radiomics is to convert medical images into a minable data space by extraction of quantitative imaging features for clinically relevant analyses, e.g. survival time prediction of a patient. One problem of radiomics from computed tomography is the impact of technical variation such as reconstruction kernel variation within a study. Additionally, what is often neglected is the impact of inter-patient technical variation, resulting from patient characteristics, even when scan and reconstruction parameters are constant. In our approach, measurements within 3D regions-of-interests (ROI) are calibrated by further ROIs such as air, adipose tissue, liver, etc. that are used as control regions (CR). Our goal is to derive general rules for an automated internal calibration that enhance prediction, based on the analysed features and a set of CRs. We define qualification criteria motivated by status-quo radiomics stability analysis techniques to only collect information from the CRs which is relevant given a respective task. These criteria are used in an optimisation to automatically derive a suitable internal calibration for prediction tasks based on the CRs. Our calibration enhanced the performance for centrilobular emphysema prediction in a COPD study and prediction of patients’ one-year-survival in an oncological study.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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