Hammer, Simone and Nunes, Danilo Weber and Hammer, Michael and Zeman, Florian and Akers, Michael and Goetza, Andrea and Balla, Annika and Doppler, Michael Christian and Fellner, Claudia and da Silvaa, Natascha Platz Batista and Thurn, Sylvia and Verloh, Niklas and Stroszczynski, Christian and Wohlgemuth, Walter Alexander and Palm, Christoph and Uller, Wibke (2024) Deep learning-based differentiation of peripheral high-flow and low-flow vascular malformations in T2-weighted short tau inversion recovery MRI. CLINICAL HEMORHEOLOGY AND MICROCIRCULATION, 87 (2). pp. 221-235. ISSN 1386-0291, 1875-8622
Full text not available from this repository. (Request a copy)Abstract
BACKGROUND: Differentiation of high-flow from low-flow vascular malformations (VMs) is crucial for therapeutic management of this orphan disease. OBJECTIVE: Aconvolutional 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | MAGNETIC-RESONANCE ANGIOGRAPHY; CLASSIFICATION; DIAGNOSIS; TUMORS; Vascular malformation; deep learning; magnetic resonance imaging |
| Subjects: | 000 Computer science, information & general works > 000 Generalities, Science 000 Computer science, information & general works > 004 Computer science 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Röntgendiagnostik |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 18 Jul 2025 06:29 |
| Last Modified: | 18 Jul 2025 06:29 |
| URI: | https://pred.uni-regensburg.de/id/eprint/63469 |
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