Ophthalmology

[OCT biomarkers in diabetic maculopathy and artificial intelligence].

Diabetes mellitus is a chronic disease the microvascular complications of which include diabetic retinopathy and maculopathy. Diabetic macular edema, proliferative diabetic retinopathy, and diabetic macular ischemia pose a threat to visual acuity. Artificial intelligence can play an increasingly more important role in making the diagnosis and the treatment regimen of maculopathies in everyday clin...

Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation.

Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) have limited adaptive capabilities, hindering their ability to reliably predict neural output under dynamic input conditions. Can embedding neural adap...

Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review.

In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversib...

Deep Learning Enabled Universal Multiplexed Fluorescence Detection for Point-of-Care Applications.

There is a significant demand for multiplexed fluorescence sensing and detection across a range of applications. Yet, the development of portable and ...

Managing a patient with uveitis in the era of artificial intelligence: Current approaches, emerging trends, and future perspectives.

The integration of artificial intelligence (AI) with healthcare has opened new avenues for diagnosing, treating, and managing medical conditions with ...

Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia.

The prediction of refractory Mycoplasma pneumoniae pneumonia (RMPP) remains a clinically significant challenge. This study aimed to develop an early p...

Screening for diabetic retinopathy with artificial intelligence: a real world evaluation.

AIM: Periodic screening for diabetic retinopathy (DR) is effective for preventing blindness. Artificial intelligence (AI) systems could be useful for ...

Establishment of a corneal ulcer prognostic model based on machine learning.

Corneal infection is a major public health concern worldwide and the most common cause of unilateral corneal blindness. Toxic effects of different mic...

A hybrid model for the detection of retinal disorders using artificial intelligence techniques.

The prevalence of vision impairment is increasing at an alarming rate. The goal of the study was to create an automated method that uses optical coher...

Selection of pre-trained weights for transfer learning in automated cytomegalovirus retinitis classification.

Cytomegalovirus retinitis (CMVR) is a significant cause of vision loss. Regular screening is crucial but challenging in resource-limited settings. A c...

Applications of Artificial Intelligence in Diagnosis of Dry Eye Disease: A Systematic Review and Meta-Analysis.

PURPOSE: Clinical diagnosis of dry eye disease is based on a subjective Ocular Surface Disease Index questionnaire or various objective tests, however...

Development and evaluation of a deep neural network model for orthokeratology lens fitting.

PURPOSE: To optimise the precision and efficacy of orthokeratology, this investigation evaluated a deep neural network (DNN) model for lens fitting. T...

Computational gastronomy: capturing culinary creativity by making food computable.

Cooking, a quintessential creative pursuit, holds profound significance for individuals, communities, and civilizations. Food and cooking transcend me...

Clinical efficacy of pre-trained large language models through the lens of aphasia.

The rapid development of large language models (LLMs) motivates us to explore how such state-of-the-art natural language processing systems can inform...

A mathematical theory of relational generalization in transitive inference.

Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This all...

Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence.

OBJECTIVES: This study aimed to quantitatively evaluate optic nerve head and retinal vascular parameters in children with hyperopia in relation to age...

Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.

PURPOSE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (E...

LMBiS-Net: A lightweight bidirectional skip connection based multipath CNN for retinal blood vessel segmentation.

Blinding eye diseases are often related to changes in retinal structure, which can be detected by analysing retinal blood vessels in fundus images. Ho...

Automated eyeball volume measurement based on CT images using neural network-based segmentation and simple estimation.

With the increase in the dependency on digital devices, the incidence of myopia, a precursor of various ocular diseases, has risen significantly. Beca...

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from ins...