[This corrects the article DOI: 10.1371/journal.pone.0168211.].
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175620 | PLOS |
Ultrasound Med Biol
January 2025
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology - NTNU, Trondheim, Norway; Health Research, SINTEF, Trondheim, Norway.
Objective: To develop and compare methods to automatically estimate regional ultrasound image quality for echocardiography separate from view correctness.
Methods: Three methods for estimating image quality were developed: (i) classic pixel-based metric: the generalized contrast-to-noise ratio (gCNR), computed on myocardial segments (region of interest) and left ventricle lumen (background), extracted by a U-Net segmentation model; (ii) local image coherence: the average local coherence as predicted by a U-Net model that predicts image coherence from B-mode ultrasound images at the pixel level; (iii) deep convolutional network: an end-to-end deep-learning model that predicts the quality of each region in the image directly. These methods were evaluated against manual regional quality annotations provided by three experienced cardiologists.
Eye (Lond)
January 2025
Maidstone Hospital Eye Department, Hermitage Lane, Maidstone, UK.
Background And Objectives: Faricimab, a bispecific antibody targeting VEGF-A and angiopoietin-2, has shown promise in treating neovascular age-related macular degeneration (nAMD). This study evaluates 1-year outcomes of faricimab in treatment-experienced nAMD patients.
Methods: This single-centre retrospective cohort study included patients previously treated for nAMD who switched to faricimab between November 2022 and March 2024.
Biol Trace Elem Res
January 2025
Department of Spine Surgery, Qilu Hospital of Shandong University, Jinan, Shandong, China.
J Imaging
December 2024
Technology Department, CERN, 1211 Geneva, Switzerland.
Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing four different pathologies: Tumors, Strokes, Multiple Sclerosis (MS), and White Matter Hyperintensities (WMH).
View Article and Find Full Text PDFCurr Oncol
January 2025
Department of Radiation Oncology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China.
(1) Background: Volumetric modulated arc therapy (VMAT) can deliver more accurate dose distribution and reduce radiotherapy-induced toxicities for postoperative cervical and endometrial cancer. This study aims to retrospectively analyze the relationship between dosimetric parameters of organs at risk (OARs) and acute toxicities and provide suggestions for the dose constraints. (2) Methods: A total of 164 postoperative cervical and endometrial cancer patients were retrospectively analyzed, and the endpoints were grade ≥ 2 acute urinary toxicity (AUT) and acute lower gastrointestinal toxicity (ALGIT).
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