Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
7 authors.
Johannes ThalhammerFrom the Department of Physics, School of Natural Sciences (J.T., M.S., T.D., F.P., D.P., F.S.), Munich Institute of Biomedical Engineering (J.T., M.S., T.D., T.L., F.P., D.P., F.S.), Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar (J.T., M.S., T.D., F.P., D.P.), Institute for Advanced Study (J.T., F.P., D.P.), and Computational Imaging and Inverse Problems, Department of Computer Science, School of Computation, Information, and Technology (T.L.), Technical University of Munich, Boltzmannstrasse 11, 85748 Garching, Germany.ORCID 0000-0001-5198-8571
Manuel SchultheißFrom the Department of Physics, School of Natural Sciences (J.T., M.S., T.D., F.P., D.P., F.S.), Munich Institute of Biomedical Engineering (J.T., M.S., T.D., T.L., F.P., D.P., F.S.), Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar (J.T., M.S., T.D., F.P., D.P.), Institute for Advanced Study (J.T., F.P., D.P.), and Computational Imaging and Inverse Problems, Department of Computer Science, School of Computation, Information, and Technology (T.L.), Technical University of Munich, Boltzmannstrasse 11, 85748 Garching, Germany.ORCID 0000-0002-4261-5420
Tina DorostiFrom the Department of Physics, School of Natural Sciences (J.T., M.S., T.D., F.P., D.P., F.S.), Munich Institute of Biomedical Engineering (J.T., M.S., T.D., T.L., F.P., D.P., F.S.), Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar (J.T., M.S., T.D., F.P., D.P.), Institute for Advanced Study (J.T., F.P., D.P.), and Computational Imaging and Inverse Problems, Department of Computer Science, School of Computation, Information, and Technology (T.L.), Technical University of Munich, Boltzmannstrasse 11, 85748 Garching, Germany.ORCID 0000-0002-5747-3250
Tobias LasserFrom the Department of Physics, School of Natural Sciences (J.T., M.S., T.D., F.P., D.P., F.S.), Munich Institute of Biomedical Engineering (J.T., M.S., T.D., T.L., F.P., D.P., F.S.), Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar (J.T., M.S., T.D., F.P., D.P.), Institute for Advanced Study (J.T., F.P., D.P.), and Computational Imaging and Inverse Problems, Department of Computer Science, School of Computation, Information, and Technology (T.L.), Technical University of Munich, Boltzmannstrasse 11, 85748 Garching, Germany.ORCID 0000-0001-5669-920X
Franz PfeifferFrom the Department of Physics, School of Natural Sciences (J.T., M.S., T.D., F.P., D.P., F.S.), Munich Institute of Biomedical Engineering (J.T., M.S., T.D., T.L., F.P., D.P., F.S.), Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar (J.T., M.S., T.D., F.P., D.P.), Institute for Advanced Study (J.T., F.P., D.P.), and Computational Imaging and Inverse Problems, Department of Computer Science, School of Computation, Information, and Technology (T.L.), Technical University of Munich, Boltzmannstrasse 11, 85748 Garching, Germany.ORCID 0000-0001-6665-4363
Daniela PfeifferFrom the Department of Physics, School of Natural Sciences (J.T., M.S., T.D., F.P., D.P., F.S.), Munich Institute of Biomedical Engineering (J.T., M.S., T.D., T.L., F.P., D.P., F.S.), Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar (J.T., M.S., T.D., F.P., D.P.), Institute for Advanced Study (J.T., F.P., D.P.), and Computational Imaging and Inverse Problems, Department of Computer Science, School of Computation, Information, and Technology (T.L.), Technical University of Munich, Boltzmannstrasse 11, 85748 Garching, Germany.ORCID 0000-0003-2002-8337
Florian SchaffFrom the Department of Physics, School of Natural Sciences (J.T., M.S., T.D., F.P., D.P., F.S.), Munich Institute of Biomedical Engineering (J.T., M.S., T.D., T.L., F.P., D.P., F.S.), Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar (J.T., M.S., T.D., F.P., D.P.), Institute for Advanced Study (J.T., F.P., D.P.), and Computational Imaging and Inverse Problems, Department of Computer Science, School of Computation, Information, and Technology (T.L.), Technical University of Munich, Boltzmannstrasse 11, 85748 Garching, Germany.ORCID 0000-0003-2144-851X
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
Purpose To explore the potential benefits of deep learning-based artifact reduction in sparse-view cranial CT scans and its impact on automated hemorrhage detection. Materials and Methods In this retrospective study, a U-Net was trained for artifact reduction on simulated sparse-view cranial CT scans in 3000 patients, obtained from a public dataset and reconstructed with varying sparse-view levels. Additionally, EfficientNet-B2 was trained on full-view CT data from 17 545 patients for automated hemorrhage detection. Detection performance was evaluated using the area under the receiver operating characteristic curve (AUC), with differences assessed using the DeLong test, along with confusion matrices. A total variation (TV) postprocessing approach, commonly applied to sparse-view CT, served as the basis for comparison. A Bonferroni-corrected significance level of .001/6 = .00017 was used to accommodate for multiple hypotheses testing. Results Images with U-Net postprocessing were better than unprocessed and TV-processed images with respect to image quality and automated hemorrhage detection. With U-Net postprocessing, the number of views could be reduced from 4096 (AUC: 0.97 [95% CI: 0.97, 0.98]) to 512 (0.97 [95% CI: 0.97, 0.98],
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Improving Automated Hemorrhage Detection at Sparse-View CT via U-Net-based Artifact Reduction. · full record | OpenQuestion