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Auszug relevanter Studien und Veröffentlichungen

Schmude: “Approximating the Number of White Matter Lesions for Patients with Atrial Fibrillation using Acoustocerebrography and Deep Learning”; Poster presented at European Stroke Conference, April 12, 2018, Athens

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Dobkowska-Chudon et al.: “Detecting cerebrovascular changes in the brain caused by hypertension in atrial fibrillation group using acoustocerebrography”, PLoS ONE 2018, Volume 13, Issue 7

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Sauer et al.: “Diagnose einer septischen Enzephalopathie durch Akustocerebrografie – Ergebnisse einer Pilotstudie”; Poster presented at DIVI Leipzig, Dec 8, 2017; E-Poster Ref. No. EP/11/01

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Olszewski et al: “Is Acoustocerebrography a new noninvasive method for early detection of the brain changes in patients with hypertension?”; Poster accepted for European Society of Cardiology Conference, August 2017, Barcelona

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Schmude: “Detecting Changes in the Human Brain caused by Atrial Fibrillation using Ultrasound and Deep Learning”; Poster presented at European Stroke Conference, May 24, 2017, Berlin; E-Poster Ref. No. L 022

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Sievert et al.: “Pilotstudie zur Diagnose einer septischen Enzephalopathie durch Akustocerebrografie – Zwischenauswertung”; Poster presented at DAC Nuremberg, May 4, 2017; E-Poster Ref. No. 2.2.12

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Bogdan et al: Computer Aided Multispectral Ultrasound Diagnostics Brain Health Monitoring System based on Acoustocerebrography, Conference Paper submitted for MEDICO 2016, Cyprus

Dobkowska-Chudon et al: “Utilizing Comparison Magnetic Resonance Imaging and Acoustocerebrography Signals in the Assessment of Focal Cerebral Microangiopathic Lesions in Patients with Asymptomatic Atrial Fibrillation (Preliminary Clinical Study Results)”, EAA 2016 Congress, Jastrzębia Góra, Poland, in: Archives of Acoustics 2016, Volume 41, Issue 2

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Olszewski et al.: “The novel non-invasive ultrasound device for detecting early changes of the brain in patients with heart failure”, European Journal of Heart Failure 2016, Volume 18, Issue Supplement S1

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Wrobel et al.: “On ultrasound classification of stroke risk factors from randomly chosen respondents using non-invasive multispectral ultrasonic brain measurements and adaptive profiles”, Biocybernetics and Biomedical Engineering 2015, Volume 35, Issue 4

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