1
|
Hammer S, Nunes DW, Hammer M, Zeman F, Akers M, Götz A, Balla A, Doppler MC, Fellner C, Platz Batista da Silva N, Thurn S, Verloh N, Stroszczynski C, Wohlgemuth WA, Palm C, Uller W. Deep learning-based differentiation of peripheral high-flow and low-flow vascular malformations in T2-weighted short tau inversion recovery MRI. Clin Hemorheol Microcirc 2024:CH232071. [PMID: 38306026 DOI: 10.3233/ch-232071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
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.
Collapse
Affiliation(s)
- Simone Hammer
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Danilo Weber Nunes
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany
| | - Michael Hammer
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany
| | - Florian Zeman
- Center for Clinical Trials, Medical Center University of Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Michael Akers
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Andrea Götz
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Annika Balla
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Michael Christian Doppler
- Department of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Claudia Fellner
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Natascha Platz Batista da Silva
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Sylvia Thurn
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Niklas Verloh
- Department of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christian Stroszczynski
- Department of Radiology, Medical Center Universityof Regensburg, Faculty of Medicine, University of Regensburg, Regensburg, Germany
| | - Walter Alexander Wohlgemuth
- Department of Radiology, Medical Center University of Halle (Saale), Faculty of Medicine, University of Halle (Saale), Halle, Germany
| | - Christoph Palm
- Regensburg Medical Image Computing (ReMIC), Ostbayerische Technische Hochschule Regensburg (OTH Regensburg), Regensburg, Germany
- Regensburg Center of Biomedical Engineering (RCBE), OTH Regensburg and University of Regensburg, Regensburg, Germany
| | - Wibke Uller
- Department of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| |
Collapse
|