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  1. Article ; Online: Aberrant claustrum structure in preterm-born neonates

    Antonia Neubauer / Aurore Menegaux / Jil Wendt / Hongwei Bran Li / Benita Schmitz-Koep / Tobias Ruzok / Melissa Thalhammer / David Schinz / Peter Bartmann / Dieter Wolke / Josef Priller / Claus Zimmer / Daniel Rueckert / Dennis M. Hedderich / Christian Sorg

    NeuroImage: Clinical, Vol 37, Iss , Pp 103286- (2023)

    an MRI study

    2023  

    Abstract: The human claustrum is a gray matter structure in the white matter between insula and striatum. Previous analysis found altered claustrum microstructure in very preterm-born adults associated with lower cognitive performance. As the claustrum development ...

    Abstract The human claustrum is a gray matter structure in the white matter between insula and striatum. Previous analysis found altered claustrum microstructure in very preterm-born adults associated with lower cognitive performance. As the claustrum development is related to hypoxia–ischemia sensitive transient cell populations being at-risk in premature birth, we hypothesized that claustrum structure is already altered in preterm-born neonates.We studied anatomical and diffusion-weighted MRIs of 83 preterm- and 83 term-born neonates at term-equivalent age. Additionally, claustrum development was analyzed both in a spectrum of 377 term-born neonates and longitudinally in 53 preterm-born subjects. Data was provided by the developing Human Connectome Project.Claustrum development showed increasing volume, increasing fractional anisotropy (FA), and decreasing mean diffusivity (MD) around term both across term- and preterm-born neonates. Relative to term-born ones, preterm-born neonates had (i) increased absolute and relative claustrum volumes, both indicating increased cellular and/or extracellular matter and being in contrast to other subcortical gray matter regions of decreased volumes such as thalamus; (ii) lower claustrum FA and higher claustrum MD, pointing at increased extracellular matrix and impaired axonal integrity; and (iii) aberrant covariance between claustrum FA and MD, respectively, and that of distributed gray matter regions, hinting at relatively altered claustrum microstructure.Results together demonstrate specifically aberrant claustrum structure in preterm-born neonates, suggesting altered claustrum development in prematurity, potentially relevant for later cognitive performance.
    Keywords Claustrum ; Neonate ; Brain development ; Preterm birth ; Magnetic resonance imaging ; Subplate neurons ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurology. Diseases of the nervous system ; RC346-429
    Subject code 610
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Faster and Better

    Tom Finck / Julia Moosbauer / Monika Probst / Sarah Schlaeger / Madeleine Schuberth / David Schinz / Mehmet Yiğitsoy / Sebastian Byas / Claus Zimmer / Franz Pfister / Benedikt Wiestler

    Diagnostics, Vol 12, Iss 452, p

    How Anomaly Detection Can Accelerate and Improve Reporting of Head Computed Tomography

    2022  Volume 452

    Abstract: Background: Most artificial intelligence (AI) systems are restricted to solving a pre-defined task, thus limiting their generalizability to unselected datasets. Anomaly detection relieves this shortfall by flagging all pathologies as deviations from a ... ...

    Abstract Background: Most artificial intelligence (AI) systems are restricted to solving a pre-defined task, thus limiting their generalizability to unselected datasets. Anomaly detection relieves this shortfall by flagging all pathologies as deviations from a learned norm. Here, we investigate whether diagnostic accuracy and reporting times can be improved by an anomaly detection tool for head computed tomography (CT), tailored to provide patient-level triage and voxel-based highlighting of pathologies. Methods: Four neuroradiologists with 1–10 years of experience each investigated a set of 80 routinely acquired head CTs containing 40 normal scans and 40 scans with common pathologies. In a random order, scans were investigated with and without AI-predictions. A 4-week wash-out period between runs was included to prevent a reminiscence effect. Performance metrics for identifying pathologies, reporting times, and subjectively assessed diagnostic confidence were determined for both runs. Results: AI-support significantly increased the share of correctly classified scans (normal/pathological) from 309/320 scans to 317/320 scans ( p = 0.0045), with a corresponding sensitivity, specificity, negative- and positive- predictive value of 100%, 98.1%, 98.2% and 100%, respectively. Further, reporting was significantly accelerated with AI-support, as evidenced by the 15.7% reduction in reporting times (65.1 ± 8.9 s vs. 54.9 ± 7.1 s; p < 0.0001). Diagnostic confidence was similar in both runs. Conclusion: Our study shows that AI-based triage of CTs can improve the diagnostic accuracy and accelerate reporting for experienced and inexperienced radiologists alike. Through ad hoc identification of normal CTs, anomaly detection promises to guide clinicians towards scans requiring urgent attention.
    Keywords machine learning ; neuroradiology ; computed tomography ; decision support ; anomaly detection ; classification ; Medicine (General) ; R5-920
    Subject code 333
    Language English
    Publishing date 2022-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data

    Hans Liebl / David Schinz / Anjany Sekuboyina / Luca Malagutti / Maximilian T. Löffler / Amirhossein Bayat / Malek El Husseini / Giles Tetteh / Katharina Grau / Eva Niederreiter / Thomas Baum / Benedikt Wiestler / Bjoern Menze / Rickmer Braren / Claus Zimmer / Jan S. Kirschke

    Scientific Data, Vol 8, Iss 1, Pp 1-

    2021  Volume 7

    Abstract: Measurement(s) vertebra Technology Type(s) computed tomography Factor Type(s) imaging centre • scanner manufacturer Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9 ... ...

    Abstract Measurement(s) vertebra Technology Type(s) computed tomography Factor Type(s) imaging centre • scanner manufacturer Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14716968
    Keywords Science ; Q
    Language English
    Publishing date 2021-10-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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