@misc{ScharfenbergMottokArtmannetal., author = {Scharfenberg, Georg and Mottok, J{\"u}rgen and Artmann, Christina and Hobelsberger, Martin and Paric, Ivan and Großmann, Benjamin and Pohlt, Clemens and Wackerbarth, Alena and Pausch, Uli and Heidrich, Christiane and Fadanelli, Martin and Elsner, Michael and P{\"o}cher, Daniel and Pittroff, Lenz and Beer, Stefan and Br{\"u}ckl, Oliver and Haslbeck, Matthias and Sterner, Michael and Thema, Martin and Muggenthaler, Nicole and Lenck, Thorsten and G{\"o}tz, Philipp and Eckert, Fabian and Deubzer, Michael and Stingl, Armin and Simsek, Erol and Kr{\"a}mer, Stefan and Großmann, Benjamin and Schlegl, Thomas and Niedersteiner, Sascha and Berlehner, Thomas and Joblin, Mitchell and Mauerer, Wolfgang and Apel, Sven and Siegmund, Janet and Riehle, Dirk and Weber, Joachim and Palm, Christoph and Zobel, Martin and Al-Falouji, Ghassan and Prestel, Dietmar and Scharfenberg, Georg and Mandl, Roland and Deinzer, Arnulf and Halang, W. and Margraf-Stiksrud, Jutta and Sick, Bernhard and Deinzer, Renate and Scherzinger, Stefanie and Klettke, Meike and St{\"o}rl, Uta and Wiech, Katharina and Kubata, Christoph and Sindersberger, Dirk and Monkman, Gareth J. and Dollinger, Markus and Dembianny, Sven and K{\"o}lbl, Andreas and Welker, Franz and Meier, Matthias and Thumann, Philipp and Swidergal, Krzysztof and Wagner, Marcus and Haug, Sonja and Vernim, Matthias and Seidenst{\"u}cker, Barbara and Weber, Karsten and Arsan, Christian and Schone, Reinhold and M{\"u}nder, Johannes and Schroll-Decker, Irmgard and Dillinger, Andrea Elisabeth and Fuchshofer, Rudolf and Monkman, Gareth J. and Shamonin (Chamonine), Mikhail and Geith, Markus A. and Koch, Fabian and {\"U}hlin, Christian and Schratzenstaller, Thomas and Saßmannshausen, Sean Patrick and Auchter, Eberhard and Kriz, Willy and Springer, Othmar and Thumann, Maria and Kusterle, Wolfgang and Obermeier, Andreas and Udalzow, Anton and Schmailzl, Anton and Hierl, Stefan and Langer, Christoph and Schreiner, Rupert}, title = {Forschungsbericht / Ostbayerische Technische Hochschule Regensburg}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-00-048589-3}, doi = {10.35096/othr/pub-1386}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13867}, language = {de} } @article{HammerNunesHammeretal., author = {Hammer, Simone and Nunes, Danilo Weber and Hammer, Michael and Zeman, Florian and Akers, Michael and G{\"o}tz, Andrea and Balla, Annika and Doppler, Michael Christian and Fellner, Claudia and Da Platz Batista Silva, Natascha and Thurn, Sylvia and Verloh, Niklas and Stroszczynski, Christian and Wohlgemuth, Walter Alexander and Palm, Christoph and Uller, Wibke}, title = {Deep learning-based differentiation of peripheral high-flow and low-flow vascular malformations in T2-weighted short tau inversion recovery MRI}, series = {Clinical hemorheology and microcirculation}, journal = {Clinical hemorheology and microcirculation}, edition = {Pre-press}, publisher = {IOP Press}, doi = {10.3233/CH-232071}, pages = {1 -- 15}, abstract = {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.}, language = {en} }