Purpose: To investigate the associations between visual function and the optical coherence tomography (OCT) parameters in eyes with idiopathic epiretinal membrane (ERM).
Methods: Thirty-nine consecutive eyes with ERM were enrolled. In addition to OCT parameters, such as central retinal thickness (CRT), the area of gap between the ERM and the retinal surface (SUKIMA) was newly defined and calculated from the vertical and horizontal OCT images (SUKIMAv and SUKIMAh). The average of SUKIMAv and SUKIMAh (SUKIMAave) was used for the statistical analysis. The vertical and horizontal metamorphopsia scores (MV, MH) and the average of MV and MH (Mave) were also used for the analysis.
Results: The Mave was not significantly associated with logMAR visual acuity (VA) (P = 0.57, linear regression analysis). Analysis using second-order bias-corrected Akaike information criterion model selection identified the age, CRT, and SUKIMAave as being associated with logMAR VA. On the other hand, among the OCT parameters, SUKIMAave and CRT were associated with the Mave. In addition, there was a significant relationship between SUKIMAh and MV (P = 0.011) and between SUKIMAv and MH (P = 0.0014).
Conclusions: We identified SUKIMA as a novel OCT parameter that is useful to predict both VA and metamorphopsia in patients with ERM.
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http://dx.doi.org/10.1167/iovs.62.6.6 | DOI Listing |
Med Image Comput Comput Assist Interv
October 2024
Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Recent advancements in Contrastive Language-Image Pre-training (CLIP) [21] have demonstrated notable success in self-supervised representation learning across various tasks. However, the existing CLIP-like approaches often demand extensive GPU resources and prolonged training times due to the considerable size of the model and dataset, making them poor for medical applications, in which large datasets are not always common. Meanwhile, the language model prompts are mainly manually derived from labels tied to images, potentially overlooking the richness of information within training samples.
View Article and Find Full Text PDFCardiovasc Diagn Ther
December 2024
Department of Cardiology, St. Luke's International Hospital, Tokyo, Japan.
Tetralogy of Fallot (TOF) is a condition that often leads to long-term enlargement of the aortic root in after surgery. The aortic dilation is believed to be caused by histological abnormalities of the aortic media and the hemodynamic characteristics of increased aortic flow, compared to pulmonary flow. Severe cyanosis, severe right ventricular outflow tract (RVOT) obstruction, older age at repair, a larger aortic size at the time of repair, and a history of an aortopulmonary shunt parameters related to long-standing volume overload of the aortic root were the reported risk factors.
View Article and Find Full Text PDFPhotodiagnosis Photodyn Ther
January 2025
Southwest Hospital/Southwest Eye Hospital, Army Medical University, Chongqing 400038, China.; Key Lab of Visual Damage and Regeneration & Restoration of Chongqing, Chongqing, China; Jinfeng Laboratory, Chongqing, China. Electronic address:
Purpose: Utilizing Swept source optical coherence tomography angiography (SS-OCTA) investigates the changes in the vascular characteristics of the choriocapillaris and larger vascular layers, including Sattler's and Haller's layers, in the macular region of young myopia patients, in order to to Enhance our comprehension of the underlying mechanisms of the pathogenesis of myopia.
Methods: A retrospective analysis was performed on 103 young adults (154 eyes) that underwent SS-OCTA. Axial lengths (AL) were measured, 64 eyes with AL < 26.
Noise Health
January 2025
School of Public Health, Anhui University of Science and Technology, Huainan, Anhui, People's Republic of China.
Objectives: This study aims to investigate the relationship between noise kurtosis and cardiovascular disease (CVD) risk while exploring the potential of kurtosis assessment in evaluating CVD risk associated with complex noise exposure in coal mines.
Methods: This cross-sectional study started in April 2021 and ended in November 2022. It involved 705 coal miners selected from 1045 participants.
J Craniofac Surg
October 2024
Division of Oral, Facial y Maxillofacial Surgery, Faculty of Dentistry, Universidad de La Frontera.
Background: Artificial intelligence (AI) has been a contribution in recent years to the development of new tools for dental, surgical, and esthetic treatment. In the case of image diagnosis, AI allows automated analysis of some facial parameters. The aim of this study was to evaluate the precision and reproducibility of these IA analyses compared with a human operator.
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