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Automated Quantitative Assessment of Retinal Vascular Tortuosity in Patients with Sickle Cell Disease.
Ophthalmol Sci
November 2024
Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, California.
Objective: To quantitatively assess the retinal vascular tortuosity of patients with sickle cell disease (SCD) and retinopathy (SCR) using an automated deep learning (DL)-based pipeline.
Design: Cross-sectional study.
Subjects: Patients diagnosed with SCD and screened for SCR at an academic eye center between January 2015 and November 2022 were identified using electronic health records.
Deep Learning-Based Identification of Echocardiographic Abnormalities From Electrocardiograms.
JACC Asia
January 2025
Department of Frontier Cardiovascular Science, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: Heart failure should be diagnosed as early as possible. Although deep learning models can predict one or more echocardiographic findings from electrocardiograms (ECGs), such analyses are not comprehensive.
Objectives: This study aimed to develop a deep learning model for comprehensive prediction of echocardiographic findings from ECGs.
Multiomics unravels the complexity of male obesity: a prospective observational study.
J Transl Med
January 2025
Department of Endocrinology, Diabetology and Metabolism, Lausanne University Hospital, Avenue de la Sallaz 8, CH-1011, Lausanne, Switzerland.
Background: Obesity is associated with varying degrees of metabolic dysfunction. In this study, we aimed to discover markers of the severity of metabolic impairment in men with obesity via a multiomics approach.
Methods: Thirty-two morbidly men with obesity who were candidates for Roux-en-Y gastric bypass (RYGB) surgery were prospectively followed.
Background: To evaluate the associations between anatomical changes and visual outcomes after membrane peeling in eyes with different stages of idiopathic epiretinal membrane (iERM) using optical coherence tomography angiography (OCTA).
Methods: All iERM eyes were graded into four stages based on the presence of ectopic inner foveal layers (EIFL) and underwent 23-gauge vitrectomy combined with ERM and internal limiting membrane (ILM) peeling, while their fellow eyes were treated as the control group. OCTA was used to measure retinal thickness(RT), foveal avascular zone (FAZ)-related parameters and superficial and deep capillary plexus (SCP and DCP) layers using 6 × 6 mm scans before, 1 month and 3 months after surgery.
Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet.
Sci Rep
January 2025
Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
The diverse types and sizes, proximity to non-nodule structures, identical shape characteristics, and varying sizes of nodules make them challenging for segmentation methods. Although many efforts have been made in automatic lung nodule segmentation, most of them have not sufficiently addressed the challenges related to the type and size of nodules, such as juxta-pleural and juxta-vascular nodules. The current research introduces a Squeeze-Excitation Dilated Attention-based Residual U-Net (SEDARU-Net) with a robust intensity normalization technique to address the challenges related to different types and sizes of lung nodules and to achieve an improved lung nodule segmentation.
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