Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification

Camara, José; Neto, Alexandre; Pires, Ivan Miguel; Villasana, María Vanessa; Zdravevski, Eftim; Cunha, António

doi:10.3390/jimaging8020019

Cited by 32 publications

(24 citation statements)

References 140 publications

(149 reference statements)

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“…There are 17 publicly available data sets with glaucoma data globally, the majority of which contain fundus photos 140. Several of these publicly available data sets have been used in the literature for training and external validation for segmentation and classification tasks for optic discs from fundus photos 26,36,141,142. For example, DRISHTI-GS, a publicly available retinal image data set, is comprised of 50 training images and 51 test images of normal and glaucomatous eyes that includes manual segmentation of ONH parameters performed by 4 specialists 36.…”

Section: Challenges For Ai-based Model Development In Glaucomamentioning

confidence: 99%

“…Especially in such instances, image resolution becomes a key limiting factor to analysis and can affect preprocessing steps such as image channel selection, illumination normalization, contrast enhancement, and the extraction of blood vessels. 36 High-quality images from eyes with media opacities, with movement artifacts, or anterior segment pathology can be difficult to obtain. Another fundamental challenge is that clinical glaucoma evaluation generally requires integrated analysis of multiple modalities (eg, clinical examination, ONH imaging, and VF testing) to determine the glaucoma subtypes and any progression, not only fundus photo findings.…”

Section: Fundus Photo Algorithms: Challengesmentioning

confidence: 99%

“…140 Several of these publicly available data sets have been used in the literature for training and external validation for segmentation and classification tasks for optic discs from fundus photos. 26,36,141,142 For example, DRISHTI-GS, a publicly available retinal image data set, is comprised of 50 training images and 51 test images of normal and glaucomatous eyes that includes manual segmentation of ONH parameters performed by 4 specialists. 36 Other commonly used data sets of fundus photos include ACRIMA, RIM-ONE, High-Resolution Fundus, the Optic Nerve Head Segmentation Data Set, ORIGA database, and the Singapore Chinese and Indian Eye Studies.…”

Section: Data Scarcity and Publicly Available Datamentioning

confidence: 99%

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Applications of Artificial Intelligence and Deep Learning in Glaucoma

Chen

Anran

Tan

et al. 2023

Asia-Pacific Journal of Ophthalmology

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Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.

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Section: Challenges For Ai-based Model Development In Glaucomamentioning

confidence: 99%

Section: Fundus Photo Algorithms: Challengesmentioning

confidence: 99%

Section: Data Scarcity and Publicly Available Datamentioning

confidence: 99%

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Applications of Artificial Intelligence and Deep Learning in Glaucoma

Chen

Anran

Tan

et al. 2023

Asia-Pacific Journal of Ophthalmology

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“…Glaucoma is classified into open-angle glaucomas (OAGs), angle-closure glaucoma (ACGs) based on the anatomic status of the anterior chamber angle ( Kang and Tanna, 2021 ). The most frequent type may differ from one region of the world to another ( Schuster et al, 2020 ; Camara et al, 2022 ). For instance, ACGs is more prevalent in certain regions of Asia, whereas OAGs is more equally distributed throughout the world.…”

Section: Introductionmentioning

confidence: 99%

Clinical pharmacology and pharmacogenetics of prostaglandin analogues in glaucoma

Zhou

Zhan²,

Wei³

2022

Front. Pharmacol.

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Glaucoma is the main cause of irreversible visual loss worldwide, and comprises a group of progressive, age-related, and chronic optic neuropathies. Prostaglandin analogs are considered a first-line treatment in the management of glaucoma and have the best efficacy in reducing intraocular pressure. When comparing these therapeutic agents between them, long-term therapy with 0.03% bimatoprost is the most effective followed by treatment with 0.005% latanoprost and 0.004% travoprost. The prevalence of adverse events is lower for latanoprost than for other prostaglandin analogs. However, some patients do not respond to the treatment with prostaglandin analogs (non-responders). Intraocular pressure-lowering efficacy differs significantly between individuals partly owing to genetic factors. Rs1045642 in ABCB1, rs4241366 in SLCO2A1, rs9503012 in GMDS, rs10306114 in PTGS1, rs11568658 in MRP4, rs10786455 and rs6686438 in PTGFR were reported to be positive with the response to prostaglandin analogs in patients with glaucoma. A negative association was found between single nucleotide polymorphisms of PTGFR (rs11578155 and rs6672484) and the response to prostaglandin analogs in patients with glaucoma. The current review is an analysis of the information relevant to prostaglandin analog treatments based on previous literatures. It describes in detail the clinical pharmacology and pharmacogenetics of drugs belonging to this therapeutical class to provide a sound pharmacological basis for their proper use in ophthalmological clinical practice.

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“…The application of DL in medicine has fulfilled the function of automated lesion recognition and prognosis prediction in various diseases ( Gulshan et al, 2016 ; Zhang Q. et al, 2021 ). In the field of ophthalmology, different AI algorithms have been developed and applied for auto-detection of diverse diseases like glaucoma ( Keel et al, 2019 ; Camara et al, 2022 ), ocular surface diseases ( Zhang Y. Y. et al, 2021 ), and multiple retinal disorders, like DR, AMD, and myopic retinopathy, and have exhibited relatively high accuracy and reliable performance in clinical diagnosis as well as the pre-hospital community screening, providing a promising solution for the challenge mentioned previously ( Rajalakshmi et al, 2018 ; He et al, 2020 ; Xie et al, 2020 ; Cen et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening

Bai

Wan

et al. 2022

Front. Cell Dev. Biol.

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Objective: To evaluate the accuracy and feasibility of the auto-detection of 15 retinal disorders with artificial intelligence (AI)-assisted optical coherence tomography (OCT) in community screening.Methods: A total of 954 eyes of 477 subjects from four local communities were enrolled in this study from September to December 2021. They received OCT scans covering an area of 12 mm × 9 mm at the posterior pole retina involving the macular and optic disc, as well as other ophthalmic examinations performed using their demographic information recorded. The OCT images were analyzed using integrated software with the previously established algorithm based on the deep-learning method and trained to detect 15 kinds of retinal disorders, namely, pigment epithelial detachment (PED), posterior vitreous detachment (PVD), epiretinal membranes (ERMs), sub-retinal fluid (SRF), choroidal neovascularization (CNV), drusen, retinoschisis, cystoid macular edema (CME), exudation, macular hole (MH), retinal detachment (RD), ellipsoid zone disruption, focal choroidal excavation (FCE), choroid atrophy, and retinal hemorrhage. Meanwhile, the diagnosis was also generated from three groups of individual ophthalmologists (group of retina specialists, senior ophthalmologists, and junior ophthalmologists) and compared with those by the AI. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated, and kappa statistics were performed.Results: A total of 878 eyes were finally enrolled, with 76 excluded due to poor image quality. In the detection of 15 retinal disorders, the ROC curve comparison between AI and professors’ presented relatively large AUC (0.891–0.997), high sensitivity (87.65–100%), and high specificity (80.12–99.41%). Among the ROC curve comparisons with those by the retina specialists, AI was the closest one to the professors’ compared to senior and junior ophthalmologists (p < 0.05).Conclusion: AI-assisted OCT is highly accurate, sensitive, and specific in auto-detection of 15 kinds of retinal disorders, certifying its feasibility and effectiveness in community ophthalmic screening.

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Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification

Cited by 32 publications

References 140 publications

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Clinical pharmacology and pharmacogenetics of prostaglandin analogues in glaucoma

Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening

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