Automatic Screening and Identifying Myopic Maculopathy on Optical Coherence Tomography Images Using Deep Learning

Ye, Xin; Wang, Jun; Chen, Yiqi; Lv, Zhe; He, Shucheng; Mao, Jianbo; Xu, Jiahao; Shen, Lijun

doi:10.1167/tvst.10.13.10

Cited by 21 publications

(12 citation statements)

References 36 publications

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“…Examples are present in the literature of the use of AI for the detection of PM from optical coherence tomography (OCT) images (32,52). However, this was beyond the scope of this study.…”

Section: Discussionmentioning

confidence: 98%

Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis

Prashar

Tay²

2023

Preprint

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Background Pathological myopia (PM) is a major cause of worldwide blindness and represents a serious threat to eye health globally. Artificial intelligence (AI)-based methods are gaining traction in ophthalmology as highly sensitive and specific tools for screening and diagnosis of many eye diseases. However, there is currently a lack of high-quality evidence for their use in the diagnosis of PM. Methods A systematic review and meta-analysis of studies evaluating the diagnostic performance of AI-based tools in PM was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance. Five electronic databases were searched, results were assessed against the inclusion criteria and a quality assessment was conducted for included studies. Model sensitivity and specificity were pooled using the DerSimonian and Laird (random-effects) model. Subgroup analysis and meta-regression were performed. Results Of 1021 citations identified, 17 studies were included in the systematic review and 11 studies, evaluating 165,787 eyes, were included in the meta-analysis. The area under the summary receiver operator curve (SROC) was 0.9905. The pooled sensitivity was 95.9% [95.5%-96.2%], and the overall pooled specificity was 96.5% [96.3%-96.6%]. The pooled diagnostic odds ratio (DOR) for detection of PM was 841.26 [418.37-1691.61]. Conclusions This systematic review and meta-analysis provides robust early evidence that AI-based, particularly deep-learning based, diagnostic tools are a highly specific and sensitive modality for the detection of PM. There is potential for such tools to be incorporated into ophthalmic public health screening programmes, particularly in resource-poor areas with a substantial prevalence of high myopia.

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“…Examples are present in the literature of the use of AI for the detection of PM from optical coherence tomography (OCT) images (32,52). However, this was beyond the scope of this study.…”

Section: Discussionmentioning

confidence: 98%

Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis

Prashar

Tay²

2023

Preprint

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“…Previous studies also involved auto-detection of myopic macular diseases using AI algorithm due to the rising prevalence of myopia and multiple vision-threatening retinal damages ( Sogawa et al, 2020 ; Choi et al, 2021 ; Ye et al, 2021 ; Li et al, 2022a ). Ye et al (2021 ) engineered the deep-learning (DL) model to identify myopic maculopathy, including macular choroidal thinning, macular Bruch membrane (BM) defects, sub-retinal hyper-reflective material (SHRM), myopic traction maculopathy (MTM), and dome-shaped macula (DSM), and the result showed that the AUC was 0.927–0.974 for five myopic maculopathies. Choi et al (2021 ) trained and validated three DL models to identify myopia and generated a result of the absolute agreement with retina specialists which was 99.11%.…”

Section: Discussionmentioning

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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“…However, traditional convolutional neural networks such as AlexNet and VGG16 may suffer from gradient explosion or disappearance when the depth is increased. New methods represented by ResNet ( Tan et al, 2021 ; Ye et al, 2021 ; Park et al, 2022 ), InceptionNet ( Choi et al, 2021 ; Li et al, 2022b ), and DenseNet ( Sogawa et al, 2020 ) have effectively addressed this problem. Lu et al ( Lu et al, 2021 ) used ResNet18 as the backbone network to classify lesions in patients with high myopia based on color fundus images.…”

Section: Ai Technology For Myopia Screening and Diagnosismentioning

confidence: 99%

Artificial intelligence technology for myopia challenges: A review

Zhang

Zou

2023

Front. Cell Dev. Biol.

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Myopia is a significant global health concern and affects human visual function, resulting in blurred vision at a distance. There are still many unsolved challenges in this field that require the help of new technologies. Currently, artificial intelligence (AI) technology is dominating medical image and data analysis and has been introduced to address challenges in the clinical practice of many ocular diseases. AI research in myopia is still in its early stages. Understanding the strengths and limitations of each AI method in specific tasks of myopia could be of great value and might help us to choose appropriate approaches for different tasks. This article reviews and elaborates on the technical details of AI methods applied for myopia risk prediction, screening and diagnosis, pathogenesis, and treatment.

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Automatic Screening and Identifying Myopic Maculopathy on Optical Coherence Tomography Images Using Deep Learning

Cited by 21 publications

References 36 publications

Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis

Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis

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

Artificial intelligence technology for myopia challenges: A review

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