Ophthalmic Diagnostic Imaging: Glaucoma

Weinreb, Robert N.; Bowd, Christopher; Moghimi, Sasan; Tafreshi, Ali; Rausch, Sebastian; Zangwill, Linda M.

doi:10.1007/978-3-030-16638-0_5

Cited by 7 publications

(6 citation statements)

References 131 publications

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“…Also, significant overlap between normal variability and early disease aggravates the diagnostic dilemma. Imaging offers the ability to document and objectively quantify the diagnostic parameters and structural changes over time 15 …”

Section: Diagnostic Approachmentioning

confidence: 99%

Ophthalmic imaging for the diagnosis and monitoring of glaucoma: A review

Elgin

Chen

Al-Aswad

2022

Clinical Exper Ophthalmology

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The last three decades have been a turning point for early glaucoma diagnosis and monitoring by newly developed imaging techniques. Improvements in imaging of the anterior segment (AS), retinal nerve fibre layer (RNFL), optic nerve head (ONH), and macular ganglion cell complex (GCC) came into existence and broke new ground for early diagnosis and progression survey. This review will summarise the different ophthalmic imaging techniques, their principles, pros and cons, ongoing development processes, and the future of imaging methods. The aim is to provide the clinician with an understanding of the role of imaging methods and provide insight for appropriate use and evaluation of their outputs in glaucoma diagnosis and treatment.

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Section: Diagnostic Approachmentioning

confidence: 99%

Ophthalmic imaging for the diagnosis and monitoring of glaucoma: A review

Elgin

Chen

Al-Aswad

2022

Clinical Exper Ophthalmology

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“…Standard automated perimetry-based VF performance remains the benchmark for assessing the intensity of glaucomatous injury [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 29 , 30 ]. Epidemiological and experimental studies have shown glaucomatous RGC loss precedes the apparition of VF defects [ 29 , 30 , 31 , 32 , 33 , 34 ] because structural alterations occur before the onset of functional changes [ 35 , 36 ]. Therefore, the absence of symptoms and inadvertent signs, mainly during the earlier glaucoma stages (such as in pre-perimetric glaucoma cases and early perimetric glaucoma patients), complicates glaucoma diagnosis and overshadows the visual prognosis of affected individuals [ 5 , 6 , 7 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Discussionmentioning

confidence: 99%

Impact of Systemic Comorbidities on Ocular Hypertension and Open-Angle Glaucoma, in a Population from Spain and Portugal

García–Villanueva

Millá

Bolarín

et al. 2022

JCM

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Open-angle glaucoma (OAG), the most prevalent clinical type of glaucoma, is still the main cause of irreversible blindness worldwide. OAG is a neurodegenerative illness for which the most important risk factor is elevated intraocular pressure (IOP). Many questions remain unanswered about OAG, such as whether nutritional or toxic habits, other personal characteristics, and/or systemic diseases influence the course of glaucoma. As such, in this study, we performed a multicenter analytical, observational, case–control study of 412 participants of both sexes, aged 40–80 years, that were classified as having ocular hypertension (OHT) or OAG. Our primary endpoint was to investigate the relationship between specific lifestyle habits; anthropometric and endocrine–metabolic, cardiovascular, and respiratory events; and commonly used psychochemicals, with the presence of OHT or OAG in an ophthalmologic population from Spain and Portugal. Demographic, epidemiological, and ocular/systemic clinical data were recorded from all participants. Data were analyzed using the R Statistics v4.1.2 and RStudio v2021.09.1 programs. The mean age was 62 ± 15 years, with 67–80 years old comprising the largest subgroup sample of participants in both study groups. The central corneal thickness (ultrasound pachymetry)-adjusted IOP (Goldman tonometry) in each eye was 20.46 ± 2.35 and 20.1 ± 2.73 mmHg for the OHT individuals, and 15.8 ± 3.83 and 16.94 ± 3.86 mmHg for the OAG patients, with significant differences between groups (both p = 0.001). The highest prevalence of the surveyed characteristics in both groups was for overweight/obesity and daily coffee consumption, followed by psychochemical drug intake, migraine, and peripheral vasospasm. Our data show that overweight/obesity, migraine, asthma, and smoking are major risk factors for conversion from OHT to OAG in this Spanish and Portuguese population.

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“…41 On the other hand, data such as OCT and SAP MD values, as well as ophthalmic exams leading to clinical diagnoses may provide a more objective standard for comparison. 83 Even if an algorithm is trained on images with human labels, it is possible that it may perform better when tested on a dataset grounded in more objective rather than subjective criteria. For example, Shibata et al 39 trained a DL algorithm using a glaucoma specialist's manual grading of fundus images.…”

Section: Table 1 (Continued)mentioning

confidence: 99%

“…Thus, Medeiros et al 60 proposed a novel approach in which objective quantifiable data from OCT was used as the reference standard. OCT provides highly reproducible imaging data 83 and has thus become the most commonly utilized imaging modality for diagnosis of structural damage from glaucoma. By pairing CFPs to their corresponding OCT RNFL global thickness values, Medeiros et al 60 trained a "machine-to-machine" algorithm that could assess optic disc photographs and predict the average RNFL thickness.…”

Section: Table 1 (Continued)mentioning

confidence: 99%

Applications of deep learning in detection of glaucoma: A systematic review

Mirzania

Muir

2020

European Journal of Ophthalmology

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Glaucoma is the leading cause of irreversible blindness and disability worldwide. Nevertheless, the majority of patients do not know they have the disease and detection of glaucoma progression using standard technology remains a challenge in clinical practice. Artificial intelligence (AI) is an expanding field that offers the potential to improve diagnosis and screening for glaucoma with minimal reliance on human input. Deep learning (DL) algorithms have risen to the forefront of AI by providing nearly human-level performance, at times exceeding the performance of humans for detection of glaucoma on structural and functional tests. A succinct summary of present studies and challenges to be addressed in this field is needed. Following PRISMA guidelines, we conducted a systematic review of studies that applied DL methods for detection of glaucoma using color fundus photographs, optical coherence tomography (OCT), or standard automated perimetry (SAP). In this review article we describe recent advances in DL as applied to the diagnosis of glaucoma and glaucoma progression for application in screening and clinical settings, as well as the challenges that remain when applying this novel technique in glaucoma.

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Ophthalmic Diagnostic Imaging: Glaucoma

Cited by 7 publications

References 131 publications

Ophthalmic imaging for the diagnosis and monitoring of glaucoma: A review

Ophthalmic imaging for the diagnosis and monitoring of glaucoma: A review

Impact of Systemic Comorbidities on Ocular Hypertension and Open-Angle Glaucoma, in a Population from Spain and Portugal

Applications of deep learning in detection of glaucoma: A systematic review

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