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Interobserver and intraobserver variability among different vendors for mitral valve assessment: implications for transcatheter mitral valve repair.
Radiol Med
January 2025
Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
Purpose: Pre-procedural imaging is critical for transcatheter mitral valve repair planning in patients with mitral valve disease. As differences among various measurement techniques for valve evaluation are still poorly understood, we sought to assess the intra- and interobserver agreement of complex measurements derived from a prototype mitral evaluation tool (Siemens) and a commercially available tool (CVI42) using both saddle- and D-shaped mitral annulus techniques.
Materials And Methods: Multiphasic cardiac computed tomography angiography data were loaded into each software.
Scoring of swine lung images: a comparison between a computer vision system and human evaluators.
Vet Res
January 2025
Veterinary Diagnostic Laboratory, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA.
Cranioventral pulmonary consolidation (CVPC) is a common lesion observed in the lungs of slaughtered pigs, often associated with Mycoplasma (M.) hyopneumoniae infection. There is a need to implement simple, fast, and valid CVPC scoring methods.
View Article and Find Full Text PDFOptimizing hip MRI: enhancing image quality and elevating inter-observer consistency using deep learning-powered reconstruction.
BMC Med Imaging
January 2025
Department of Magnetic Resonance Imaging, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China.
Background: Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. Leveraging deep learning-based reconstruction (DLR) holds the potential to mitigate scan time without compromising image quality.
View Article and Find Full Text PDFEvaluation of artificial intelligence-based autosegmentation for a high-performance cone-beam computed tomography imaging system in the pelvic region.
Phys Imaging Radiat Oncol
January 2025
Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Background And Purpose: A novel ring-gantry cone-beam computed tomography (CBCT) imaging system shows improved image quality compared to its conventional version, but its effect on autosegmentation is unknown. This study evaluates the impact of this high-performance CBCT on autosegmentation performance, inter-observer variability, contour correction times and delineation confidence, compared to the conventional CBCT.
Materials And Methods: Twenty prostate cancer patients were enrolled in this prospective clinical study.
Diagnostic accuracy in coronary CT angiography analysis: artificial intelligence versus human assessment.
Open Heart
January 2025
Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Background: Visual assessment of coronary CT angiography (CCTA) is time-consuming, influenced by reader experience and prone to interobserver variability. This study evaluated a novel algorithm for coronary stenosis quantification (atherosclerosis imaging quantitative CT, AI-QCT).
Methods: The study included 208 patients with suspected coronary artery disease (CAD) undergoing CCTA in Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography-1.
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