Spotlight on iPad Visual Field Tests Efficacy

Ichhpujani, Parul; Dhillon, Hennaav

doi:10.2147/opth.s340508

Cited by 1 publication

(1 citation statement)

References 23 publications

(46 reference statements)

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“…The ophthalmologist interprets the automated perimetry by combining risk factors, appearance of the optic nerve, age, ocular pressure (IOP), and family history of glaucoma [ 12 ].…”

Section: Some Approaches To Medical Practice In Glaucomamentioning

confidence: 99%

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

Camara,

Cunha

2024

Medicina

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Glaucoma is one of the leading causes of irreversible blindness in the world. Early diagnosis and treatment increase the chances of preserving vision. However, despite advances in techniques for the functional and structural assessment of the retina, specialists still encounter many challenges, in part due to the different presentations of the standard optic nerve head (ONH) in the population, the lack of explicit references that define the limits of glaucomatous optic neuropathy (GON), specialist experience, and the quality of patients’ responses to some ancillary exams. Computer vision uses deep learning (DL) methodologies, successfully applied to assist in the diagnosis and progression of GON, with the potential to provide objective references for classification, avoiding possible biases in experts’ decisions. To this end, studies have used color fundus photographs (CFPs), functional exams such as visual field (VF), and structural exams such as optical coherence tomography (OCT). However, it is still necessary to know the minimum limits of detection of GON characteristics performed through these methodologies. This study analyzes the use of deep learning (DL) methodologies in the various stages of glaucoma screening compared to the clinic to reduce the costs of GON assessment and the work carried out by specialists, to improve the speed of diagnosis, and to homogenize opinions. It concludes that the DL methodologies used in automated glaucoma screening can bring more robust results closer to reality.

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“…The ophthalmologist interprets the automated perimetry by combining risk factors, appearance of the optic nerve, age, ocular pressure (IOP), and family history of glaucoma [ 12 ].…”

Section: Some Approaches To Medical Practice In Glaucomamentioning

confidence: 99%

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

Camara,

Cunha

2024

Medicina

View full text Add to dashboard Cite

show abstract

Spotlight on iPad Visual Field Tests Efficacy

Cited by 1 publication

References 23 publications

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

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