Ophthalmology

Harmful Algae Forecasting through an Ocean Data Justice Lens.

Forecasting systems for harmful algal blooms (HABs) are becoming more common, as HAB monitoring is increasingly networked and aggregated at national and global scales. Ocean forecasting programs in other fields have had unintended consequences and out-of-scope uses. The field of Data Justice provides a perspective for understanding unintended harm caused by the application of data technologies gen...

Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models.

INTRODUCTION: Accurate prediction of postoperative vault height following implantable collamer lens (ICL) V4c implantation is critical for minimizing complications and achieving optimal surgical outcomes. This study aims to evaluate the performance of machine learning models in predicting postoperative vault height, focusing on both regression and classification approaches.

A cross population study of retinal aging biomarkers with longitudinal pre-training and label distribution learning.

Retinal age has emerged as a promising biomarker of aging, offering a non-invasive and accessible assessment tool. We developed a deep learning model ...

Artificial Intelligence Deep Learning Models to Predict Spaceflight Associated Neuro-ocular Syndrome (SANS).

PURPOSE: To create deep learning artificial intelligence (AI) models for predicting the development of Spaceflight Associated Neuro-ocular Syndrome (S...

Explainable Artificial Intelligence (XAI) in the Era of Large Language Models: Applying an XAI Framework in Pediatric Ophthalmology Diagnosis using the Gemini Model.

Amblyopia is a neurodevelopmental disorder affecting children's visual acuity, requiring early diagnosis for effective treatment. Traditional diagnost...

Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity.

Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. T...

Interpretable machine learning models for predicting childhood myopia from school-based screening data.

This study assessed the efficacy of various diagnostic indicators and machine learning (ML) models in predicting childhood myopia. A total of 2,365 ch...

Investigation of Short-term Crystalline Lens Positional Shifts Following ICL Implantation Using SS-OCT Integrated With Multi-task Deep Learning.

PURPOSE: To investigate the tilt and decentration of the crystalline lens shortly after Implantable Collamer Lens (ICL) implantation.

Performance of artificial intelligence-based models for epiretinal membrane diagnosis: A systematic review and meta-analysis.

TOPIC: Epiretinal membrane (ERM) can impair central vision by forming a pre-retinal fibrous layer on the inner retina. Artificial intelligence (AI)-ba...

Effectiveness of smartphone technology for detection of paediatric ocular diseases-a systematic review.

BACKGROUND: Artificial intelligence has become part of healthcare with a multitude of applications being customized to roles required in clinical prac...

Integration of proteomics and artificial intelligence-driven OCT biomarker analysis in central retinal vein occlusion.

Retinal OCT biomarker analysis by artificial intelligence (AI) has not previously been integrated with proteomics. Here, we combined the two technique...

A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer.

The brain serves as the central command center for the nervous system in the human body and is made up of nerve cells known as neurons. When these ne...

Predictive modeling of hemoglobin refractive index using Gaussian process regression with interpretability through partial dependence plots.

Accurately predicting the refractive index of hemoglobin across various wavelengths and concentrations is critical for advancing optical diagnostic te...

A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema.

PURPOSE: To develop a machine learning model to predict anatomical response to anti-VEGF therapy in patients with diabetic macular edema (DME).

Deep learning-driven approach for cataract management: towards precise identification and predictive analytics.

Deep learning (DL) technology has shown significant potential in the whole process of cataract diagnosis and treatment through algorithms such as conv...

Beyond the Hype: The potential and challenges of semi-solid extrusion 3D Printing in pharmaceutical applications through the lens of Portuguese 3D experts.

In recent years, the technological landscape has experienced a notable surge in the exploration of 3D Printing technologies (3DPT), particularly as an...

Hybrid attention-based deep learning for multi-label ophthalmic disease detection on fundus images.

BACKGROUND: Ophthalmic diseases significantly impact vision and quality of life. Early diagnosis using fundus images is critical for timely treatment....

Glaucoformer: Dual-domain Global Transformer Network for Generalized Glaucoma Stage Classification.

Classification of glaucoma stages remains challenging due to substantial inter-stage similarities, the presence of irrelevant features, and subtle les...

A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America.

This scoping review aims to identify regulator-approved ophthalmic image analysis artificial intelligence as a medical device (AIaMD) in three jurisdi...

Synthetic data in medicine: Legal and ethical considerations for patient profiling.

Synthetic data is increasingly used in healthcare to facilitate privacy-preserving research, algorithm training, and patient profiling. By mimicking t...