Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm

He, Tingting; Zhou, Qiaoer; Zou, Yuanwen

doi:10.3390/diagnostics12020532

Cited by 26 publications

(11 citation statements)

References 49 publications

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“…Gour et al [ 44 ] used VGG-16 to classify 331 FP images from ODIR dataset (8 classes including AMD, cataract, diabetes, glaucoma, hyperattention, myopia, and other abnormalities) for AMD identifying with a sensitivity of 6% and a specificity of 94%. He et al [ 45 ] used ResNet-50 to classify 795 OCT images from Mendeley and Duke datasets (3 classes including AMD, DME, and normal) for AMD identifying with a sensitivity of 96% and a specificity of 99%. Kadry et al [ 46 ] used VGG-16, VGG-19, AlexNet, and ResNet-50 to classify 3200 FP images and 3200 OCT images from iChallenge AMD database, OCTID (2 classes including AMD and Non-AMD) resulting in sensitivity of 88%, 84%, 88%, 88% and specificity of 85%, 87%, 85%, 84%, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Deep learning for detection of age-related macular degeneration: A systematic review and meta-analysis of diagnostic test accuracy studies

Leng

Shi

et al. 2023

PLoS ONE

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Objective To evaluate the diagnostic accuracy of deep learning algorithms to identify age-related macular degeneration and to explore factors impacting the results for future model training. Methods Diagnostic accuracy studies published in PubMed, EMBASE, the Cochrane Library, and ClinicalTrails.gov before 11 August 2022 which employed deep learning for age-related macular degeneration detection were identified and extracted by two independent researchers. Sensitivity analysis, subgroup, and meta-regression were performed by Review Manager 5.4.1, Meta-disc 1.4, and Stata 16.0. The risk of bias was assessed using QUADAS-2. The review was registered (PROSPERO CRD42022352753). Results The pooled sensitivity and specificity in this meta-analysis were 94% (P = 0, 95% CI 0.94–0.94, I2 = 99.7%) and 97% (P = 0, 95% CI 0.97–0.97, I2 = 99.6%), respectively. The pooled positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the area under the curve value were 21.77(95% CI 15.49–30.59), 0.06 (95% CI 0.04–0.09), 342.41 (95% CI 210.31–557.49), and 0.9925, respectively. Meta-regression indicated that types of AMD (P = 0.1882, RDOR = 36.03) and layers of the network (P = 0.4878, RDOR = 0.74) contributed to the heterogeneity. Conclusions Convolutional neural networks are mostly adopted deep learning algorithms in age-related macular degeneration detection. Convolutional neural networks, especially ResNets, are effective in detecting age-related macular degeneration with high diagnostic accuracy. Types of age-related macular degeneration and layers of the network are the two essential factors that impact the model training process. Proper layers of the network will make the model more reliable. More datasets established by new diagnostic methods will be used to train deep learning models in the future, which will benefit for fundus application screening, long-range medical treatment, and reducing the workload of physicians.

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Section: Resultsmentioning

confidence: 99%

“…Generally, 8 studies [ 42 , 47 , 50 – 54 , 56 ] did not study other retinal diseases. 3 studies [ 45 , 49 , 55 ] only contained one other diseases. 2 studies [ 48 , 56 ] had small datasets with no more than 100 images.…”

Section: Discussionmentioning

confidence: 99%

Deep learning for detection of age-related macular degeneration: A systematic review and meta-analysis of diagnostic test accuracy studies

Leng

Shi

et al. 2023

PLoS ONE

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“…After testing, the F1 score, accuracy, and recall of the segmented lesion size were 0.65, 0.75, and 0.72, respectively, and the F1 scores, accuracy, and recall of the leakage area were 0.73, 0.80, and 0.78, respectively. He et al (2022) created a DL model that can detect age-related macular degeneration through the ResNet-50 model and local outlier factor (LOF) algorithm and used the UCSD dataset and Duke dataset to train and test the model. Finally, the accuracy of the model was 0.9987 for the UCSD dataset and 0.9756 for the Duke dataset.…”

Section: Application Of Artificial Intelligence In Retinal Vascular D...mentioning

confidence: 99%

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Liu

et al. 2023

Front. Cell Dev. Biol.

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As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

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“…Some papers focus on diagnosing only one particular disease. In He et al 30 , AMD was diagnosed from Normal cases using ResNet-50. The AUC of 0.99, Sensitivity of , and Specificity of 95.02 were the reported results.…”

Section: Related Workmentioning

confidence: 99%

Wavelet scattering transform application in classification of retinal abnormalities using OCT images

Baharlouei,

Rabbani,

Plonka

2023

Sci Rep

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To assist ophthalmologists in diagnosing retinal abnormalities, Computer Aided Diagnosis has played a significant role. In this paper, a particular Convolutional Neural Network based on Wavelet Scattering Transform (WST) is used to detect one to four retinal abnormalities from Optical Coherence Tomography (OCT) images. Predefined wavelet filters in this network decrease the computation complexity and processing time compared to deep learning methods. We use two layers of the WST network to obtain a direct and efficient model. WST generates a sparse representation of the images which is translation-invariant and stable concerning local deformations. Next, a Principal Component Analysis classifies the extracted features. We evaluate the model using four publicly available datasets to have a comprehensive comparison with the literature. The accuracies of classifying the OCT images of the OCTID dataset into two and five classes were $$100\%$$ 100 % and $$82.5\%$$ 82.5 % , respectively. We achieved an accuracy of $$96.6\%$$ 96.6 % in detecting Diabetic Macular Edema from Normal ones using the TOPCON device-based dataset. Heidelberg and Duke datasets contain DME, Age-related Macular Degeneration, and Normal classes, in which we achieved accuracy of $$97.1\%$$ 97.1 % and $$94.4\%$$ 94.4 % , respectively. A comparison of our results with the state-of-the-art models shows that our model outperforms these models for some assessments or achieves nearly the best results reported so far while having a much smaller computational complexity.

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Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm

Cited by 26 publications

References 49 publications

Deep learning for detection of age-related macular degeneration: A systematic review and meta-analysis of diagnostic test accuracy studies

Deep learning for detection of age-related macular degeneration: A systematic review and meta-analysis of diagnostic test accuracy studies

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

Wavelet scattering transform application in classification of retinal abnormalities using OCT images

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