AI Medical Compendium Topic

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Cornea

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A novel artificial intelligence model for diagnosing Acanthamoeba keratitis through confocal microscopy.

The ocular surface
PURPOSE: To develop an artificial intelligence (AI) model to diagnose Acanthamoeba keratitis (AK) based on in vivo confocal microscopy (IVCM) images extracted from the Heidelberg Retinal Tomograph 3 (HRT 3).

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

Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)
PURPOSE: To optimise the precision and efficacy of orthokeratology, this investigation evaluated a deep neural network (DNN) model for lens fitting. The objective was to refine the standardisation of fitting procedures and curtail subjective evaluati...

Deep-learning based analysis of in-vivo confocal microscopy images of the subbasal corneal nerve plexus' inferior whorl in patients with neuropathic corneal pain and dry eye disease.

The ocular surface
PURPOSE: To evaluate and compare subbasal corneal nerve parameters of the inferior whorl in patients with dry eye disease (DED), neuropathic corneal pain (NCP), and controls using a novel deep-learning-based algorithm to analyze in-vivo confocal micr...

Thickness Speed Progression Index: Machine Learning Approach for Keratoconus Detection.

American journal of ophthalmology
PURPOSE: To develop and validate a pachymetry-based machine learning (ML) index for differentiating keratoconus, keratoconus suspect, and normal corneas.

Artificial intelligence versus conventional methods for RGP lens fitting in keratoconus.

Contact lens & anterior eye : the journal of the British Contact Lens Association
BACKGROUND: To compare the efficiency of three artificial intelligence (AI) frameworks (Standard Machine Learning (ML), Multi-Layer Perceptron (MLP) and Convolution Neural Networks (CNN)) with a reference method (Mean radius of curvature (K)) to pred...

Automated feature selection for early keratoconus screening optimization.

Biomedical physics & engineering express
In this paper, an automated feature selection (FS) method is presented to optimize machine learning (ML) models' performances, enhancing early keratoconus screening. A total of 448 parameters were analyzed from a dataset comprising 3162 observations ...

Forme fruste keratoconus detection with OCT corneal topography using artificial intelligence algorithms.

Journal of cataract and refractive surgery
PURPOSE: To differentiate a normal cornea from a forme fruste keratoconus (FFKC) with the swept-source optical coherence tomography (SS-OCT) topography CASIA 2 using machine learning artificial intelligence algorithms.

AI for Corneal Imaging: How Will This Help Us Take Care of Our Patients?

Cornea
As artificial intelligence continues to evolve at a rapid pace, there is growing enthusiasm surrounding the potential for novel applications in corneal imaging. This article provides an overview of the potential for such applications, as well as the ...