USING ARTIFICIAL INTELLIGENCE AND NOVEL TECHNOLOGY TO DETECT AND MONITOR CORNEAL DISEASE

Grant number: 1187763 | Funding period: 2020 - 2023

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Abstract

Keratoconus is a disease of the cornea, the front window of the eye, that affects vision and can cause blindness. It is diagnosed using images of the eye, but early disease is difficult to detect, and there is also no worldwide agreement on which signs best indicate that the disease is progressing. We will use a type of artificial intelligence called machine-learning together with an innovative new imaging machine to overcome these challenges and thereby improve both diagnosis and treatment.

Related publications (7)

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Stratification of keratoconus progression using unsupervised machine learning analysis of tomographical parameters

K Cao, K Verspoor, E Chan, M Daniell, S Sahebjada, PN Baird

2023-01-01

Purpose: This study aimed to stratify eyes with keratoconus (KC) based on longitudinal changes in all Pentacam parameters into clu..

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Diagnosis and Management of Keratoconus-A Narrative Review of Clinicians' Perspectives

Minji Song, Tanya Chen, Adam Moktar, Elsie Chan, Elaine W Chong, Mark Daniell, Srujana Sahebjada

2022-12-01

This review discusses the current practices, attitudes, and trends in diagnosing and managing keratoconus (KC) in adults and child..

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Non-genetic risk factors for keratoconus

Minji Song, Qing Yi Fang, Ishith Seth, Paul N Baird, Mark D Daniell, Srujana Sahebjada

2022-05-05

Keratoconus is a complex and multifactorial disease and its exact aetiology remains unknown. This current study examined the impor..

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Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis

Ke Cao, Karin Verspoor, Srujana Sahebjada, Paul N Baird

2022-02-01

(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accur..

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Machine learning with a reduced dimensionality representation of comprehensive Pentacam tomography parameters to identify subclinical keratoconus

Ke Cao, Karin Verspoor, Elsie Chan, Mark Daniell, Srujana Sahebjada, Paul N Baird

2021-10-01

PURPOSE: To investigate the performance of a machine learning model based on a reduced dimensionality parameter space derived from..

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Risk factors and association with severity of keratoconus: the Australian study of Keratoconus

Srujana Sahebjada, Elsie Chan, Jing Xie, Grant R Snibson, Mark Daniell, Paul N Baird

2021-03-01

SIGNIFICANCE: Our results show that asthmatic patients tend to have more severe KC and thus close monitoring for disease progressi..

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Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus

Ke Cao, Karin Verspoor, Srujana Sahebjada, Paul N Baird

2020-04-24

Purpose: Keratoconus (KC) represents one of the leading causes of corneal transplantation worldwide. Detecting subclinical KC woul..