Latest AI and machine learning research in ophthalmology for healthcare professionals.
Deciphering the brain’s structure-function relationship is key to understanding the neuronal mechanisms underlying perception and cognition. The cortical column, a vertical organization of neurons with similar functions, is a classic example of primate neocortex structure-function organization. While columns have been identified in primary sensory areas using parametric stimuli, their prevalence a...
During rest and sleep, sequential neural activation patterns corresponding to awake experience re-emerge, and this replay has been shown to benefit subsequent behavior and memory. Whereas some studies show that replay directly recapitulates recent experience, others demonstrate that replay systematically deviates from the temporal structure, the statistics, and even the content of recent experienc...
Spiking neural networks (SNNs) have attracted growing interest for their ability to operate efficiently on low-power neuromorphic hardware, offering a...
The information we hold in mind with working memory (WM) may propagate beyond the cortex and out to the peripheral motor system. For instance, directi...
Biological neural networks can be meaningfully partitioned into sub-networks referred to as communities. Community structure is thought to support fun...
The foveal retina is a primate specialization which presents a feasible site for obtaining a complete connectome of a human CNS structure. In the fove...
Artificial neural networks (ANNs) have become powerful tools for modeling human perception and hehavior, yet it remains unclear if they resemble the s...
We present the All-GCL dataset, a large-scale resource of functional two-photon Ca2+-imaging recordings with rich meta-data information from more than...
State-of-the-art computational models of vision largely focus on fitting trial-averaged spike counts to visual stimuli using overparameterized neural ...
The rapid expansion of protein sequence databases has far outpaced experimental structure determination, leaving many unannotated sequences, particula...
Formulating hypotheses about gene-disease associations requires logical inference from prior data, followed by a laborious literature review. AI model...
Optical coherence tomography (OCT) is a widely used imaging modality in ophthalmology. Accurate semantic segmentation of these images is critical for ...
Diffusion models have emerged as the state-of-the-art method in generative artificial intelligence (AI) and have shown great success in image synthesi...
Chronic diseases often require repeated oral or local administration, which can compromise patient compliance. In wet age-related macular degeneration...
To characterize cell type specific transcriptional changes during human retinal aging and develop machine learning model for cellular age discriminati...
To develop an equitable deep learning model with knowledge distillation to enhance the demographic equity in glaucoma progression prediction. We devel...
To assess the rate of retinal vascularisation derived from ultra-widefield (UWF) imaging-based retinopathy of prematurity (ROP) screening as predictor...
For patients with facial paralysis, the wait for return of facial function and resulting vision risk from poor eye closure, difficulty speaking and ea...
To compare the performance and cost-effectiveness of DeepSeek-R1 with OpenAI o1 in diagnosing and managing ophthalmology clinical cases. Cross-section...
Artificial intelligence (AI) foundation models for colour fundus photography (CFP) have been extensively studied and demonstrated great potential for ...