TY - JOUR A1 - Scharfenberg, Georg A1 - Mottok, Jürgen A1 - Artmann, Christina A1 - Hobelsberger, Martin A1 - Paric, Ivan A1 - Großmann, Benjamin A1 - Pohlt, Clemens A1 - Wackerbarth, Alena A1 - Pausch, Uli A1 - Heidrich, Christiane A1 - Fadanelli, Martin A1 - Elsner, Michael A1 - Pöcher, Daniel A1 - Pittroff, Lenz A1 - Beer, Stefan A1 - Brückl, Oliver A1 - Haslbeck, Matthias A1 - Sterner, Michael A1 - Thema, Martin A1 - Muggenthaler, Nicole A1 - Lenck, Thorsten A1 - Götz, Philipp A1 - Eckert, Fabian A1 - Deubzer, Michael A1 - Stingl, Armin A1 - Simsek, Erol A1 - Krämer, Stefan A1 - Großmann, Benjamin A1 - Schlegl, Thomas A1 - Niedersteiner, Sascha A1 - Berlehner, Thomas A1 - Joblin, Mitchell A1 - Mauerer, Wolfgang A1 - Apel, Sven A1 - Siegmund, Janet A1 - Riehle, Dirk A1 - Weber, Joachim A1 - Palm, Christoph A1 - Zobel, Martin A1 - Al-Falouji, Ghassan A1 - Prestel, Dietmar A1 - Scharfenberg, Georg A1 - Mandl, Roland A1 - Deinzer, Arnulf A1 - Halang, W. A1 - Margraf-Stiksrud, Jutta A1 - Sick, Bernhard A1 - Deinzer, Renate A1 - Scherzinger, Stefanie A1 - Klettke, Meike A1 - Störl, Uta A1 - Wiech, Katharina A1 - Kubata, Christoph A1 - Sindersberger, Dirk A1 - Monkman, Gareth J. A1 - Dollinger, Markus A1 - Dembianny, Sven A1 - Kölbl, Andreas A1 - Welker, Franz A1 - Meier, Matthias A1 - Thumann, Philipp A1 - Swidergal, Krzysztof A1 - Wagner, Marcus A1 - Haug, Sonja A1 - Vernim, Matthias A1 - Seidenstücker, Barbara A1 - Weber, Karsten A1 - Arsan, Christian A1 - Schone, Reinhold A1 - Münder, Johannes A1 - Schroll-Decker, Irmgard A1 - Dillinger, Andrea Elisabeth A1 - Fuchshofer, Rudolf A1 - Monkman, Gareth J. A1 - Shamonin (Chamonine), Mikhail A1 - Geith, Markus A. A1 - Koch, Fabian A1 - Ühlin, Christian A1 - Schratzenstaller, Thomas A1 - Saßmannshausen, Sean Patrick A1 - Auchter, Eberhard A1 - Kriz, Willy A1 - Springer, Othmar A1 - Thumann, Maria A1 - Kusterle, Wolfgang A1 - Obermeier, Andreas A1 - Udalzow, Anton A1 - Schmailzl, Anton A1 - Hierl, Stefan A1 - Langer, Christoph A1 - Schreiner, Rupert ED - Baier, Wolfgang T1 - Forschungsbericht / Ostbayerische Technische Hochschule Regensburg T3 - Forschungsberichte der OTH Regensburg - 2015 Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-13867 SN - 978-3-00-048589-3 CY - Regensburg ER - TY - JOUR A1 - Hammer, Simone A1 - Nunes, Danilo Weber A1 - Hammer, Michael A1 - Zeman, Florian A1 - Akers, Michael A1 - Götz, Andrea A1 - Balla, Annika A1 - Doppler, Michael Christian A1 - Fellner, Claudia A1 - Da Platz Batista Silva, Natascha A1 - Thurn, Sylvia A1 - Verloh, Niklas A1 - Stroszczynski, Christian A1 - Wohlgemuth, Walter Alexander A1 - Palm, Christoph A1 - Uller, Wibke T1 - Deep learning-based differentiation of peripheral high-flow and low-flow vascular malformations in T2-weighted short tau inversion recovery MRI JF - Clinical hemorheology and microcirculation N2 - BACKGROUND Differentiation of high-flow from low-flow vascular malformations (VMs) is crucial for therapeutic management of this orphan disease. OBJECTIVE A convolutional neural network (CNN) was evaluated for differentiation of peripheral vascular malformations (VMs) on T2-weighted short tau inversion recovery (STIR) MRI. METHODS 527 MRIs (386 low-flow and 141 high-flow VMs) were randomly divided into training, validation and test set for this single-center study. 1) Results of the CNN's diagnostic performance were compared with that of two expert and four junior radiologists. 2) The influence of CNN's prediction on the radiologists' performance and diagnostic certainty was evaluated. 3) Junior radiologists' performance after self-training was compared with that of the CNN. RESULTS Compared with the expert radiologists the CNN achieved similar accuracy (92% vs. 97%, p = 0.11), sensitivity (80% vs. 93%, p = 0.16) and specificity (97% vs. 100%, p = 0.50). In comparison to the junior radiologists, the CNN had a higher specificity and accuracy (97% vs. 80%, p <  0.001; 92% vs. 77%, p <  0.001). CNN assistance had no significant influence on their diagnostic performance and certainty. After self-training, the junior radiologists' specificity and accuracy improved and were comparable to that of the CNN. CONCLUSIONS Diagnostic performance of the CNN for differentiating high-flow from low-flow VM was comparable to that of expert radiologists. CNN did not significantly improve the simulated daily practice of junior radiologists, self-training was more effective. KW - magnetic resonance imaging KW - deep learning KW - Vascular malformation Y1 - 2024 U6 - https://doi.org/10.3233/CH-232071 SP - 1 EP - 15 PB - IOP Press ET - Pre-press ER -