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Using a deep learning model to predict postoperative visual outcomes of idiopathic epiretinal membrane surgery.
Am J Ophthalmol
January 2025
Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan. Electronic address:
Purpose: This study assessed the performance of various deep learning models in predicting the postoperative outcomes of idiopathic epiretinal membrane (ERM) surgery based on preoperative optical coherence tomography (OCT) images.
Design: Validation of algorithms to predict the outcomes of ERM surgery based on OCT data.
Methods: Internal training and validation were performed using 1,392 OCT images from 696 eyes.
RetinaRegNet: A zero-shot approach for retinal image registration.
Comput Biol Med
January 2025
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32610, United States; Department of Medicine, University of Florida, Gainesville, FL, 32610, United States; Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, 32610, United States; Intelligent Clinical Care Center, University of Florida, Gainesville, FL, 32610, United States. Electronic address:
Retinal image registration is essential for monitoring eye diseases and planning treatments, yet it remains challenging due to large deformations, minimal overlap, and varying image quality. To address these challenges, we propose RetinaRegNet, a multi-stage image registration model with zero-shot generalizability across multiple retinal imaging modalities. RetinaRegNet begins by extracting image features using a pretrained latent diffusion model.
View Article and Find Full Text PDFSD-LayerNet: Robust and label-efficient retinal layer segmentation via anatomical priors.
Comput Methods Programs Biomed
January 2025
Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria; Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
Background And Objectives: Automated, anatomically coherent retinal layer segmentation in optical coherence tomography (OCT) is one of the most important components of retinal disease management. However, current methods rely on large amounts of labeled data, which can be difficult and expensive to obtain. In addition, these systems tend often propose anatomically impossible results, which undermines their clinical reliability.
View Article and Find Full Text PDFDiscovery of non-retinoid compounds that suppress the pathogenic effects of misfolded rhodopsin in a mouse model of retinitis pigmentosa.
PLoS Biol
January 2025
Department of Pharmacology and Cleveland Center for Membrane and Structural Biology, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America.
Pathogenic mutations that cause rhodopsin misfolding lead to a spectrum of currently untreatable blinding diseases collectively termed retinitis pigmentosa. Small molecules to correct rhodopsin misfolding are therefore urgently needed. In this study, we utilized virtual screening to search for drug-like molecules that bind to the orthosteric site of rod opsin and improve its folding and trafficking.
View Article and Find Full Text PDFRetinal Changes After Acute and Late Optic Neuritis in Aquaporin-4 Antibody Seropositive NMOSD.
J Neuroophthalmol
December 2024
Experimental and Clinical Research Center (FCO, HGZ, SM, CB, ESA, CC, FP, AUB), Max Delbrück Center for Molecular Medicine and Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; NeuroCure Clinical Research Center (FCO, HGZ, SM, CB, ESA, CC, FP, AUB), Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Department of Neurology (AJG), University of California San Francisco, San Francisco, California; Neurology (RM, ACC), Multiple Sclerosis, Myelin Disorders and Neuroinflammation Pierre Wertheimer Neurological Hospital, Hospices Civils de Lyon, France; Centre d'Esclerosi Múltiple de Catalunya (Cemcat) (ACC), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain; Experimental Neurophysiology Unit (LL, MP, M. Radaelli), Institute of Experimental Neurology (INSPE) Scientific Institute, Hospital San Raffaele and University Vita-Salute San Raffaele, Milan, Italy; Hospital Clinic of Barcelona-Institut d'Investigacions (PV, BS-D, EHM-L), Biomèdiques August Pi Sunyer, (IDIBAPS), Barcelona, Spain; CIEM MS Research Center (MAL-P, MAF), University of Minas Gerais, Medical School, Belo Horizonte, Brazil; Department of Neurology (OA, M. Ringelstein, PA), Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Neurology (M. Ringelstein), Centre for Neurology and Neuropsychiatry, LVR Klinikum, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Medicine (MRY), Harbor-University of California at Los Angeles (UCLA) Medical Center, and Lundquist Institute for Biomedical Innovation, Torrance, California; Department of Medicine (MRY), David Geffen School of Medicine at UCLA, Los Angeles, California; Departments of Ophthalmology and Visual Sciences (TJS), Kellogg Eye Center, University of Michigan Medical School, Ann Arbor, Michigan; Division of Metabolism, Endocrine and Diabetes (TJS, LC), Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan; Department of Neurology (FP), Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; and Department of Neurology (AUB), University of California, Irvine, California.
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