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Found 6 papers, 3 papers with code
Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus
The cone-beam computed tomography (CBCT) provides 3D volumetric imaging of a target with low radiation dose and cost compared with conventional computed tomography, and it is widely used in the detection of paranasal sinus disease.
Comparative Validation of AI and non-AI Methods in MRI Volumetry to Diagnose Parkinsonian Syndromes
Dice scores of both DL models were sufficiently high (>0. 85), and their AUCs for disease classification were superior to that of FS.
Improved Chest Anomaly Localization without Pixel-level Annotation via Image Translation Network Application in Pseudo-paired Registration Domain
For the first stage, we introduce an advanced deep-learning-based registration technique that virtually and reasonably converts unpaired data into paired data for learning registration maps, by sequentially utilizing linear-based global and uniform coordinate transformation and AI-based non-linear coordinate fine-tuning.
AI-based computer-aided diagnostic system of chest digital tomography synthesis: Demonstrating comparative advantage with X-ray-based AI systems
The proposed CDTS-based AI CAD system yielded sensitivities of 0. 782 and 0. 785 and accuracies of 0. 895 and 0. 837 for the performance of detecting tuberculosis and pneumonia, respectively, against normal subjects.
3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography
The proposed method based on unsupervised learning improves the patient-level anomaly detection by 10% (area under the curve, 0. 959) compared with a gold standard based on supervised learning (area under the curve, 0. 848), and it localizes the anomaly region with 93% accuracy, demonstrating its high performance.
Automated Precision Localization of Peripherally Inserted Central Catheter Tip through Model-Agnostic Multi-Stage Networks
In internal validation, when MFCN was applied to the existing single model, MFP was improved by an average of 45%.
