Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis

Cheung, Ronald; Chun, Jacob; Sheidow, Tom G.; Motolko, Michael; Malvankar-Mehta, Monali S.

doi:10.1038/s41433-021-01540-y

Cited by 18 publications

(12 citation statements)

References 40 publications

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“…While there is a plethora of other studies that have trained neural networks to detect macular degeneration, many used small datasets, or lack a transparent ground truth and thus are very unlikely to generalise. 31 …”

Section: Discussionmentioning

confidence: 99%

Automation of Macular Degeneration Classification in the AREDS Dataset, Using a Novel Neural Network Design

Xie¹,

Vaghefi²,

Yang³

et al. 2023

OPTH

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Purpose To create an ensemble of Convolutional Neural Networks (CNNs), capable of detecting and stratifying the risk of progressive age-related macular degeneration (AMD) from retinal photographs. Design Retrospective cohort study. Methods Three individual CNNs are trained to accurately detect 1) advanced AMD, 2) drusen size and 3) the presence or otherwise of pigmentary abnormalities, from macular centered retinal images were developed. The CNNs were then arranged in a “cascading” architecture to calculate the Age-related Eye Disease Study (AREDS) Simplified 5-level risk Severity score (Risk Score 0 – Risk Score 4), for test images. The process was repeated creating a simplified binary “low risk” (Scores 0–2) and “high risk” (Risk Score 3–4) classification. Participants There were a total of 188,006 images, of which 118,254 images were deemed gradable, representing 4591 patients, from the AREDS1 dataset. The gradable images were split into 50%/25%/25% ratios for training, validation and test purposes. Main Outcome Measures The ability of the ensemble of CNNs using retinal images to predict an individual’s risk of experiencing progression of their AMD based on the AREDS 5-step Simplified Severity Scale. Results When assessed against the 5-step Simplified Severity Scale, the results generated by the ensemble of CNN’s achieved an accuracy of 80.43% (quadratic kappa 0.870). When assessed against a simplified binary (Low Risk/High Risk) classification, an accuracy of 98.08%, sensitivity of ≥85% and specificity of ≥99% was achieved. Conclusion We have created an ensemble of neural networks, trained on the AREDS 1 dataset, that is able to accurately calculate an individual’s score on the AREDS 5-step Simplified Severity Scale for AMD. If the results presented were replicated, then this ensemble of CNNs could be used as a screening tool that has the potential to significantly improve health outcomes by identifying asymptomatic individuals who would benefit from AREDS2 macular supplements.

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Section: Discussionmentioning

confidence: 99%

Automation of Macular Degeneration Classification in the AREDS Dataset, Using a Novel Neural Network Design

Xie¹,

Vaghefi²,

Yang³

et al. 2023

OPTH

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“…In contrast, there are few reports regarding comprehensive AMD management, including health check-ups [19,20]. Furthermore, there have been noticeable screening-related changes, such as the increasing benefits of optical coherence tomography (OCT) [21,22] ,improved accuracy of artificial intelligence (AI) for fundus images and OCT [21][22][23][24] , the guidance effect on smoking cessation [25], and the refinement of the strategy for nutritional supplement intake confirmed using AREDS/AREDS2 [10].…”

Section: Introductionmentioning

confidence: 99%

Real-world effectiveness of screening programs for age-related macular degeneration: amended Japanese specific health checkups and augmented screening programs with OCT or AI

et al. 2022

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“…1,2 Novel technologies have been applied in AMD clinical tools and research efforts to address this growing need and have demonstrated strong initial results. [3][4][5][6][7] While research has identified molecular etiologies in AMD development, including lipofuscin accumulation in retinal pigmented epithelial cells, choroidal ischemia with vascular endothelial growth factor (VEGF) involvement, oxidative stress, and genetic factors, no clear pathogenic mechanism to direct treatment or prevention has emerged. [8][9][10] Pathogenic biomarkers are contained in biofluids such as serum, tears, aqueous humour and vitreous humour, which can be obtained in both clinical and surgical settings.…”

Section: Introductionmentioning

confidence: 99%

“…AI has already been applied in AMD research and clinical tool development, with compelling research efforts focused on screening, treatment, prognosis, and structure-function mapping of the retina. [3][4][5][6][7]11,12 Supervised AI techniques, including discriminant analysis or artificial neural networks, are trained using defined cases and learn to classify groups or predict outcomes. 10,[13][14][15] In contrast, unsupervised AI techniques such as hierarchical cluster analysis and principal component analysis (PCA) are adept at determining trends in highly dimensional data as they can group unlabeled data based on similarities or differences and find associations between variables in large data sets.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

Pucchio¹,

Krance²,

Pur³

et al. 2022

OPTH

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This systematic review explores the use of artificial intelligence (AI) in the analysis of biofluid markers in age-related macular degeneration (AMD). We detail the accuracy and validity of AI in diagnostic and prognostic models and biofluid markers that provide insight into AMD pathogenesis and progression. This review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines. A comprehensive search was conducted across 5 electronic databases including Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, EMBASE, Medline, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics in AMD were included. Identified studies were assessed for risk of bias and critically appraised using the Joanna Briggs Institute Critical Appraisal tools. A total of 10,264 articles were retrieved from all databases and 37 studies met the inclusion criteria, including 15 cross-sectional studies, 15 prospective cohort studies, five retrospective cohort studies, one randomized controlled trial, and one case–control study. The majority of studies had a general focus on AMD (58%), while neovascular AMD (nAMD) was the focus in 11 studies (30%), and geographic atrophy (GA) was highlighted by three studies. Fifteen studies examined disease characteristics, 15 studied risk factors, and seven guided treatment decisions. Altered lipid metabolism (HDL-cholesterol, total serum triglycerides), inflammation (c-reactive protein), oxidative stress, and protein digestion were implicated in AMD development and progression. AI tools were able to both accurately differentiate controls and AMD patients with accuracies as high as 87% and predict responsiveness to anti-VEGF therapy in nAMD patients. Use of AI models such as discriminant analysis could inform prognostic and diagnostic decision-making in a clinical setting. The identified pathways provide opportunity for future studies of AMD development and could be valuable in the advancement of novel treatments.

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Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis

Cited by 18 publications

References 40 publications

Automation of Macular Degeneration Classification in the AREDS Dataset, Using a Novel Neural Network Design

Automation of Macular Degeneration Classification in the AREDS Dataset, Using a Novel Neural Network Design

Real-world effectiveness of screening programs for age-related macular degeneration: amended Japanese specific health checkups and augmented screening programs with OCT or AI

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

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