AIMC Topic: Cornea

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Deep learning-based segmentation and density estimation of corneal nerves and dendritic cells from In Vivo confocal microscopy images.

Scientific reports
The purpose of this study was to compare manual assessment of corneal nerve fiber length (CNFL) and dendritic cell (DC) density with an automated assessment method utilizing deep learning segmentation to perform rule-based density estimation. Corneal...

AI-MK: artificial intelligence for assessing and monitoring microbial keratitis.

BMJ open ophthalmology
BACKGROUND/AIMS: To evaluate the performance of an artificial intelligence (AI) model for detecting and monitoring microbial keratitis (MK) using anterior segment optical coherence tomography (AS-OCT).

Dual vision transformer with bio-inspired optimization for explainable keratoconus classification.

International ophthalmology
BACKGROUND: Keratoconus (KCN) is a progressive degenerative corneal disorder characterized by corneal thinning and cone-shaped protrusion, leading to significant visual impairment if not detected early. Accurate staging of KCN using corneal topograph...

Exploring biomarkers for keratoconus: current insights and future directions.

Molecular biology reports
Keratoconus (KC) is a progressive corneal disorder characterized by thinning of the cornea and conical protrusion leading to distorted vision and blindness. The disease often marks in adolescence and progresses until the mid-40s, with varying degrees...

The application of artificial intelligence-based algorithms in predicting the progression of keratoconus: a systematic review.

International ophthalmology
PURPOSE: To conduct a systematic review of studies examining the use of artificial intelligence (AI) algorithms in predicting the progression of keratoconus (KCN).

Metaheuristic-optimized swin transformer with SHAP explainability for keratoconus classification from corneal topography maps.

International ophthalmology
Keratoconus (KCN) is an uncommon corneal disorder where the central cornea undergoes advanced thinning and causes non-uniform astigmatism. This results in metamorphopsia and potential vision loss if it is left untreated. Early detection of KCN is maj...

Prediction of the ectasia screening index from raw Casia2 volume data for keratoconus identification by using convolutional neural networks.

PloS one
Purpose Prediction of the ectasia screening index, an estimator provided by the Casia2 instrument for identifying keratoconus, from raw optical coherence tomography data using convolutional neural networks. Methods Three convolutional neural networks...

Comparative performance of deep learning architectures for diabetic peripheral neuropathy detection using corneal confocal microscopy: a retrospective single-centre study.

BMJ open
OBJECTIVES: This study aims to develop a deep learning algorithm (DLA) using the InceptionV3 architecture for effective diabetic peripheral neuropathy (DPN) screening via corneal confocal microscopy (CCM) images.

An optimized multi-scale dilated attention layer for keratoconus disease classification.

International ophthalmology
INTRODUCTION: Keratoconus (KCN) is a progressive and non-inflammatory corneal disorder characterized by thinning and conical deformation of the cornea, resulting in visual impairment. Early and accurate detection is crucial to prevent disease progres...

Characterising corneal changes in aniridia-related keratopathy using in vivo confocal microscopy and a self-supervised AI model.

BMJ open ophthalmology
PURPOSE: To investigate whether corneal changes observed via in vivo confocal microscopy (IVCM) in patients with aniridia-related keratopathy (ARK) reflect clinical severity.