Latest AI and machine learning research in ophthalmology for healthcare professionals.
Recent advances in deep learning and machine learning have greatly increased the capabilities of extracting features for evaluating the response to anti VEGF treatment in patients with Diabetic Macular Edema (DME). In this review, we explore how these algorithms can be used for discriminating between responders and non-responders to anti vascular endothelial growth factor (VEGF) injections. Electr...
The integration of artificial intelligence (AI) with radiology signifies a transformative era in medicine. Vision foundation models have been adopted to enhance radiologic imaging analysis. However, the inherent complexities of 2D and 3D radiologic data present unique challenges that existing models, which are typically pretrained on general nonmedical images, do not adequately address. To bridge ...
Our study aims to improve the prediction performance of machine learning (ML) models by addressing false records (i.e., false positive, false negative...
OBJECTIVE: Deep learning (DL) has been used to differentiate papilledema from healthy eyes and optic disc elevation on fundus photos. As we described ...
Traumatic brain injury (TBI) induces complex molecular and cellular responses, often leading to vision deterioration and potential mortality. Current ...
Electron microscopy (EM) has revolutionized our understanding of cellular structures at the nanoscale. Accurate image segmentation is required for ana...
BACKGROUND: Candida endophthalmitis (CE) and chorioretinitis are uncommon but potentially devastating complications of candidemia, associated with sig...
PURPOSE: To evaluate the success of Chat Generative Pre-trained Transformer (ChatGPT; OpenAl), Google Gemini (Alphabet, Inc), and Microsoft Copilot (M...
Glioblastoma (GBM) is a highly aggressive brain tumor with poor outcomes and limited treatment options. The telomerase reverse transcriptase (TERT) pr...
PURPOSE: To report an explainable deep learning (XDL) system to automatically detect thyroid eye disease (TED) using facial images.
BACKGROUND: The rising incidence of refractures and associated adverse outcomes among individuals with osteoporotic vertebral compression fractures ha...
PURPOSE: Adenoviral keratoconjunctivitis is the most common infectious disease in ophthalmology. Its clinical forms include epidemic keratoconjunctivi...
Autism spectrum disorder (ASD) is clinically heterogeneous, with ongoing debates about phenotypic differences between boys and girls. Understanding th...
CLINICAL RELEVANCE: Meibomian gland dysfunction (MGD) is a major contributor to dry eye disease, affecting a large proportion of the population and le...
BACKGROUND AND AIMS: Myopia is a prevalent refractive error, particularly among young adults, and is becoming a growing global concern. This study aim...
Malignant and premalignant ocular surface tumors (OSTs) can be sight-threatening or even life-threatening if not diagnosed and treated promptly. Artif...
PURPOSE: To evaluate and quantify diabetes-related retinal and choroid perfusion changes in individuals with and without high myopia and explore their...
The burgeoning problem of electronic waste (e-waste) management necessitates sophisticated, efficient, and precise classification techniques for recyc...
The pain-free monitoring of blood-based biomarkers is essential for early detection of diseases like cancers, infections, and metabolic disorders such...
OBJECTIVE: Cataract, a common age-related blinding eye disease, has a complex pathogenesis. This study aims to identify key genes and potential mechan...