Latest AI and machine learning research in ophthalmology for healthcare professionals.
Surgical mortality is the third leading cause of death globally, with mortality rates in Africa double those of high-income countries despite patients being younger and undergoing lower-risk procedures. One of the contributors to poor outcomes in low- and middle-income countries (LMICs) is the lack of digital data, which is essential for quality improvement, audit and feedback systems, and early w...
Automated detection of papilloedema using artificial intelligence (AI) and retinal images acquired through an ophthalmoscope for triage of patients with potential intracranial pathology could prove to be beneficial, particularly in resource-limited settings where access to neuroimaging may be limited. However, a comprehensive overview of the current literature on this field is lacking. We conducte...
Echocardiograms provide essential insights into cardiac health, yet their complex, multidimensional data poses significant challenges for analysis and...
The rapid advancement of AI in ophthalmology is transforming diagnostics, especially in resource-limited settings. The shortage of ophthalmologists an...
In-context learning, a prompt-based learning mechanism that enables multimodal foundation models to adapt to new tasks, can eliminate the need for ret...
Chagas disease affects 6–7 million people worldwide and causes approximately 12,000 deaths annually. Diagnostic methods vary by disease stage, with se...
This study evaluates the diagnostic performance of several AI models, including Deepseek, in diagnosing corneal diseases, glaucoma, and neuroâ–¡ophthalm...
Timely and accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and clinically managing patients. Current workflows r...
To evaluate diffusion weighted imaging (DWI) with advanced diffusion models, optical coherence tomography (OCT), and automatic retinal oximetry as pot...
Amid efforts to understand spaceflight associated neuro-ocular syndrome (SANS), uncovering the role of the choroid in its etiology is challenged by th...
Surgical success hinges on two core factors: technical execution and cognitive planning. While the former can be trained and potentially augmented thr...
Glaucoma is a leading cause of irreversible blindness worldwide, with early diagnosis often hindered by subtle symptomatology and the lack of comprehe...
To develop and validate deep learning (DL)-based models for classifying geographic atrophy (GA) subtypes using Optical Coherence Tomography (OCT) scan...
The choroid layer plays a critical role in maintaining outer retinal health and is implicated in numerous vision-threatening diseases such as diabetic...
To train and evaluate segmentation-free 3D convolutional neural network (3DCNN) models for estimating visual field (VF) from optical coherence tomogra...
To advance precision medicine in pathology, robust AI-driven foundation models are increasingly needed to uncover complex patterns in large-scale path...
Real-world ocular imaging datasets are essential for advancing research in artificial intelligence (AI), autonomous disease screening, and clinical de...
Intralesional corticosteroid injections (ILCS) are a common treatment for keloid lesions; however, many patients exhibit resistance, and some experien...
This study proposes a deep learning vision-language model for the automated diagnosis of pediatric dental diseases, with a focus on differentiating be...
Advancements in artificial intelligence (AI) are revolutionising the healthcare sector, but challenges exist in AI adoption and its long-term use. Thi...