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  • Kunz, Wolfgang G.  (3)
  • 2020-2024  (3)
  • 1
    In: Investigative Radiology, Ovid Technologies (Wolters Kluwer Health), Vol. 55, No. 3 ( 2020-3), p. 181-189
    Kurzfassung: The aim of this study was to investigate diagnostic accuracy and impact on patient management of an ultrafast (4:33 minutes/5 sequences) brain magnetic resonance imaging (MRI) protocol for the detection of intracranial pathologies in acute neurological emergencies. Materials and Methods Four hundred forty-nine consecutive emergency patients with acute nontraumatic neurological symptoms were evaluated for this institutional review board–approved prospective single-center trial. Sixty patients (30 female, 30 male; mean age, 61 years) with negative head CT were included and underwent emergency brain MRI at 3 T subsequent to CT. MRI included the ultrafast protocol (ultrafast-MRI; sag T1 GRE, ax T2 TSE, ax T2 TSE Flair, ax T2* EPI-GRE, ax DWI SS-EPI; TA, 5 minutes) and an equivalent standard-length protocol (TA, 15 minutes) as reference standard. Two blinded board-certified neuroradiologists independently analyzed the MRI with regard to image quality (1, nondiagnostic; 2, substantial artifacts; 3, satisfactory; 4, minor artifacts; 5, no artifacts) and intracranial pathologies. Sensitivity and specificity for the detection of intracranial pathologies were calculated accordingly. Results Ninety-three additional intracranial lesions (acute ischemia, n = 21; intracranial hemorrhage/microbleeds, n = 27; edema, n = 2; white matter lesion, n = 38; chronic infarction, n = 3; others, n = 2) were detected by ultrafast-MRI, whereas 101 additional intracranial lesions were detected by the standard-length protocol (acute ischemia, n = 24; intracranial hemorrhage/microbleeds, n = 32; edema, n = 2; white matter lesion, n = 38; chronic infarction, n = 3; others, n = 2). Image quality was equivalent to the standard-length protocol. Ultrafast-MRI demonstrated high diagnostic accuracy (sensitivity, 0.939 [0.881–0.972]; specificity, 1.000 [0.895–1.000] ) for the detection of intracranial pathologies. MRI led to a change in patient management in 10% compared with the initial CT. Conclusions Ultrafast-MRI enables time-optimized diagnostic workup in acute neurological emergencies at high sensitivity and specificity compared with a standard-length protocol, with direct impact on patient management. Ultrafast MRI protocols are a powerful tool in the emergency setting and may be implemented on various scanner types based on the optimization of individual acquisition parameters.
    Materialart: Online-Ressource
    ISSN: 1536-0210 , 0020-9996
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2020
    ZDB Id: 2041543-6
    ZDB Id: 80345-5
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
    In: Clinical Neuroradiology, Springer Science and Business Media LLC, Vol. 31, No. 3 ( 2021-09), p. 799-810
    Kurzfassung: To provide real-world data on outcome and procedural factors of late thrombectomy patients. Methods We retrospectively analyzed patients from the multicenter German Stroke Registry. The primary endpoint was clinical outcome on the modified Rankin scale (mRS) at 3 months. Trial-eligible patients and the subgroups were compared to the ineligible group. Secondary analyses included multivariate logistic regression to identify predictors of good outcome (mRS  ≤  2). Results Of 1917 patients who underwent thrombectomy, 208 (11%) were treated within a time window ≥ 6–24 h and met the baseline trial criteria. Of these, 27 patients (13%) were eligible for DAWN and 39 (19%) for DEFUSE3 and 156 patients were not eligible for DAWN or DEFUSE3 (75%), mainly because there was no perfusion imaging (62%; n  = 129). Good outcome was not significantly higher in trial-ineligible (27%) than in trial-eligible (20%) patients ( p  = 0.343). Patients with large trial-ineligible CT perfusion imaging (CTP) lesions had significantly more hemorrhagic complications (33%) as well as unfavorable outcomes. Conclusion In clinical practice, the high number of patients with a good clinical outcome after endovascular therapy ≥ 6–24 h as in DAWN/DEFUSE3 could not be achieved. Similar outcomes are seen in patients selected for EVT ≥ 6 h based on factors other than CTP. Patients triaged without CTP showed trends for shorter arrival to reperfusion times and higher rates of independence.
    Materialart: Online-Ressource
    ISSN: 1869-1439 , 1869-1447
    Sprache: Englisch
    Verlag: Springer Science and Business Media LLC
    Publikationsdatum: 2021
    ZDB Id: 2234662-4
    ZDB Id: 2232347-8
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
    In: Critical Care Medicine, Ovid Technologies (Wolters Kluwer Health), Vol. 48, No. 7 ( 2020-07), p. e574-e583
    Kurzfassung: Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence algorithm to classify basal lung opacities according to underlying pathologies. Design: Retrospective study. The deep neural network was trained on two publicly available datasets including 297,541 images of 86,876 patients. Patients: One hundred sixty-six patients received both supine chest radiograph and CT scans (reference standard) within 90 minutes without any intervention in between. Measurements and Main Results: Algorithm accuracy was referenced to board-certified radiologists who evaluated supine chest radiographs according to side-separate reading scores for pneumonia and effusion (0 = absent, 1 = possible, and 2 = highly suspected). Radiologists were blinded to the supine chest radiograph findings during CT interpretation. Performances of radiologists and the artificial intelligence algorithm were quantified by receiver-operating characteristic curve analysis. Diagnostic metrics (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) were calculated based on different receiver-operating characteristic operating points. Regarding pneumonia detection, radiologists achieved a maximum diagnostic accuracy of up to 0.87 (95% CI, 0.78–0.93) when considering only the supine chest radiograph reading score 2 as positive for pneumonia. Radiologist’s maximum sensitivity up to 0.87 (95% CI, 0.76–0.94) was achieved by additionally rating the supine chest radiograph reading score 1 as positive for pneumonia and taking previous examinations into account. Radiologic assessment essentially achieved nonsignificantly higher results compared with the artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.737 (0.659–0.815) versus radiologist’s area under the receiver-operating characteristic curve of 0.779 (0.723–0.836), diagnostic metrics of receiver-operating characteristic operating points did not significantly differ. Regarding the detection of pleural effusions, there was no significant performance difference between radiologist’s and artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.740 (0.662–0.817) versus radiologist’s area under the receiver-operating characteristic curve of 0.698 (0.646–0.749) with similar diagnostic metrics for receiver-operating characteristic operating points. Conclusions: Considering the minor level of performance differences between the algorithm and radiologists, we regard artificial intelligence as a promising clinical decision support tool for supine chest radiograph examinations in the clinical routine with high potential to reduce the number of missed findings in an artificial intelligence–assisted reading setting.
    Materialart: Online-Ressource
    ISSN: 0090-3493
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2020
    ZDB Id: 197890-1
    Standort Signatur Einschränkungen Verfügbarkeit
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