An Artificial-Intelligence–Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs

Tang, Jungen; Yuan, Mingzhen; Tian, Kaibin; Wang, Yuelin; Wang, Dongyue; Yang, Jingyuan; Yang, Zhikun; He, Xixi; Luo, Yan; Liu, Ying; Xu, Jie; Li, Xirong; Ding, Dayong; Ren, Yanhan; Chen, Youxin; Sadda, Srinivas R; Wu, Yu

doi:10.1167/tvst.11.6.16

Cited by 18 publications

(6 citation statements)

References 41 publications

(37 reference statements)

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“…7 studies (31.8%) were graded as having a low risk of bias in all 4 domains [16,18,20,[26][27][28]33]. 12 studies (54.5%) had at least one domain with applicability concerns [14,15,17,19,21,22,24,25,[29][30][31][32]. For patient selection, 12 studies (54.5%) were graded as having an unclear risk of bias because of the lack of a clear description of public datasets, and 12 studies (54.5%) had unclear applicability concerns due to unavailable composition information.…”

Section: Risk Of Bias Assessment and Publication Biasmentioning

confidence: 99%

Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis

Zhang

Liu

et al. 2023

Eye

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Background/Objective Pathologic myopia (PM) is a major cause of severe visual impairment and blindness, and current applications of artificial intelligence (AI) have covered the diagnosis and classification of PM. This meta-analysis and systematic review aimed to evaluate the overall performance of AI-based models in detecting PM and related complications. Methods We searched PubMed, Scopus, Embase, Web of Science and IEEE Xplore for eligible studies before Dec 20, 2022. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We calculated the pooled sensitivity (SEN), specificity (SPE) and the summary area under the curve (AUC) using a random effects model, to evaluate the performance of AI in the detection of PM based on fundus or optical coherence tomography (OCT) images. Results 22 studies were included in the systematic review, and 14 of them were included in the quantitative analysis. Of all included studies, SEN and SPE ranged from 80.0% to 98.7% and from 79.5% to 100.0% for PM detection, respectively. For the detection of PM, the summary AUC was 0.99 (95% confidence interval (CI) 0.97 to 0.99), and the pooled SEN and SPE were 0.95 (95% CI 0.92 to 0.96) and 0.97 (95% CI: 0.94 to 0.98), respectively. For the detection of PM-related choroid neovascularization (CNV), the summary AUC was 0.99 (95% CI: 0.97 to 0.99). Conclusion Our review demonstrated the excellent performance of current AI algorithms in detecting PM and related complications based on fundus and OCT images.

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Section: Risk Of Bias Assessment and Publication Biasmentioning

confidence: 99%

Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis

Zhang

Liu

et al. 2023

Eye

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“… Li et al (2022) used DCNN-DS model to detect no myopic maculopathy, tessellated fundus, and pathologic myopia, and the validation accuracies on the two external testing datasets were 96.3 and 93.0%, respectively. Tang et al (2022) used ResNet-50 model to develop the META-PM study categorizing system, and the mean accuracy was 0.9119 ± 0.0093 on the five categories. The overall accuracy of the VOLO-D2 model in this study is 96.60% on the five categories, but the number of images in the external test set is small, only 176.…”

Section: Discussionmentioning

confidence: 99%

Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition

Wan

Fang

Hua³

et al. 2023

Front. Comput. Neurosci.

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PurposeTo propose a five-category model for the automatic detection of myopic macular lesions to help grassroots medical institutions conduct preliminary screening of myopic macular lesions from limited number of color fundus images.MethodsFirst, 1,750 fundus images of non-myopic retinal lesions and four categories of pathological myopic maculopathy were collected, graded, and labeled. Subsequently, three five-classification models based on Vision Outlooker for Visual Recognition (VOLO), EfficientNetV2, and ResNet50 for detecting myopic maculopathy were trained with data-augmented images, and the diagnostic results of the different trained models were compared and analyzed. The main evaluation metrics were sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), area under the curve (AUC), kappa and accuracy, and receiver operating characteristic curve (ROC).ResultsThe diagnostic accuracy of the VOLO-D2 model was 96.60% with a kappa value of 95.60%. All indicators used for the diagnosis of myopia-free macular degeneration were 100%. The sensitivity, NPV, specificity, and PPV for diagnosis of leopard fundus were 96.43, 98.33, 100, and 100%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of diffuse chorioretinal atrophy were 96.88, 98.59, 93.94, and 99.29%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of patchy chorioretinal atrophy were 92.31, 99.26, 97.30, and 97.81%, respectively. The sensitivity, specificity, PPV, and NPV for the diagnosis of macular atrophy were 100, 98.10, 84.21, and 100%, respectively.ConclusionThe VOLO-D2 model accurately identified myopia-free macular lesions and four pathological myopia-related macular lesions with high sensitivity and specificity. It can be used in screening pathological myopic macular lesions and can help ophthalmologists and primary medical institution providers complete the initial screening diagnosis of patients.

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“…There were no disagreements between reviewers at the full-text screening stage. Eleven studies were included in the meta-analysis (25)(26)(27)(28)(29)(30)(31)(32)(33)(34), and a further six ( (35)(36)(37)(38)(39)(40) in the systematic review.…”

Section: Study Selectionmentioning

confidence: 99%

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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An Artificial-Intelligence–Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs

Cited by 18 publications

References 41 publications

Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis

Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis

Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition

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

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