Flaxman SR et al (2017) Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob Health 5(12):e1221–e1234. https://doi.org/10.1016/s2214-109x(17)30393-5
Shah S (2009) Blindness and visual impairment due to retinal diseases. Community Eye Health 22(69):8–9
PubMed PubMed Central Google Scholar
Ferris FL III et al (2013) Clinical classification of age-related macular degeneration. Ophthalmology 120(4):844–851
Agarwal A et al (2018) Current role of optical coherence tomography angiography: expert panel discussion. Indian J Ophthalmol 66(12):1696–1699. https://doi.org/10.4103/ijo.IJO_1048_18
Article PubMed PubMed Central Google Scholar
González-Gonzalo C et al (2020) Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration. Acta Ophthalmol 98(4):368–377. https://doi.org/10.1111/aos.14306
Li B et al (2022) Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography. Br J Ophthalmol 106(8):1079–1086. https://doi.org/10.1136/bjophthalmol-2020-316290
Nguyen TD et al (2024) Retinal disease diagnosis using deep learning on ultra-wide-field fundus images. Diagnostics. https://doi.org/10.3390/diagnostics14010105
Article PubMed PubMed Central Google Scholar
Arora A, Arora A (2023) The promise of large language models in health care. Lancet 401(10377):641. https://doi.org/10.1016/s0140-6736(23)00216-7
Chowdhury AR, Chatterjee T, Banerjee S (2019) A random forest classifier-based approach in the detection of abnormalities in the retina. Med Biol Eng Comput 57(1):193–203. https://doi.org/10.1007/s11517-018-1878-0
Emir B, Colak E (2023) Performance analysis of pretrained convolutional neural network models for ophthalmological disease classification. Arq Bras Oftalmol. https://doi.org/10.5935/0004-2749.2022-0124
Article PubMed PubMed Central Google Scholar
He J et al (2023) An interpretable transformer network for the retinal disease classification using optical coherence tomography. Sci Rep 13(1):3637. https://doi.org/10.1038/s41598-023-30853-z
Article CAS PubMed PubMed Central Google Scholar
Kang EY et al (2021) A multimodal imaging-based deep learning model for detecting treatment-requiring retinal vascular diseases: model development and validation study. JMIR Med Inform 9(5):e28868. https://doi.org/10.2196/28868
Article PubMed PubMed Central Google Scholar
Schardt C et al (2007) Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med Inform Decis Mak 7:16. https://doi.org/10.1186/1472-6947-7-16
Article PubMed PubMed Central Google Scholar
He T, Zhou Q, Zou Y (2022) Automatic detection of age-related macular degeneration based on deep learning and local outlier factor algorithm. Diagnostics. https://doi.org/10.3390/diagnostics12020532
Article PubMed PubMed Central Google Scholar
Vaghefi E et al (2020) Multimodal retinal image analysis via deep learning for the diagnosis of intermediate dry age-related macular degeneration: a feasibility study. J Ophthalmol 2020:7493419. https://doi.org/10.1155/2020/7493419
Article CAS PubMed PubMed Central Google Scholar
Thomas A et al (2021) A novel multiscale and multipath convolutional neural network based age-related macular degeneration detection using OCT images. Comput Methods Programs Biomed 209:106294. https://doi.org/10.1016/j.cmpb.2021.106294
Thomas A et al (2021) A novel multiscale convolutional neural network based age-related macular degeneration detection using OCT images. Biomed Signal Process Control 67:102538. https://doi.org/10.1016/j.bspc.2021.102538
Zheng Y, Hijazi MH, Coenen F (2012) Disease/no disease" grading of age-related macular degeneration by an image mining approach. Invest Ophthalmol Vis Sci 53(13):8310–8318. https://doi.org/10.1167/iovs.12-9576
Ji Q et al (2018) Efficient deep learning-based automated pathology identification in retinal optical coherence tomography images. Algorithms 11(6):88
Sun Y, Zhang H, Yao X (2020) Automatic diagnosis of macular diseases from OCT volume based on its two-dimensional feature map and convolutional neural network with attention mechanism. J Biomed Opt. https://doi.org/10.1117/1.jbo.25.9.096004
Article PubMed PubMed Central Google Scholar
Das V, Dandapat S, Bora PK (2019) Multi-scale deep feature fusion for automated classification of macular pathologies from OCT images. Biomed Signal Process Control 54:101605
Mookiah MRK et al (2015) Local configuration pattern features for age-related macular degeneration characterization and classification. Comput Biol Med 63:208–218
Kadry S et al (2022) Automated detection of age-related macular degeneration using a pre-trained deep-learning scheme. J Supercomput 78(5):7321–7340
Cao S et al (2023) Application effect of an artificial intelligence-based fundus screening system: evaluation in a clinical setting and population screening. Biomed Eng Online 22(1):38. https://doi.org/10.1186/s12938-023-01097-9
Article PubMed PubMed Central Google Scholar
Acharya UR et al (2016) Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features. Comput Biol Med 73:131–140
Thomas SA, Titus G (2020) Design of a portable retinal imaging module with automatic abnormality detection. Biomed Signal Process Control 60:101962
Srivastava R, Ong EP, and Lee B-H. Role of the choroid in automated age-related macular degeneration detection from optical coherence tomography images. in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020. IEEE
Mohaimin SM et al (2018) Automated method for the detection and segmentation of drusen in colour fundus image for the diagnosis of age-related macular degeneration. IET Image Process 12(6):919–927
Takhchidi H et al (2021) Labelling of data on fundus color pictures used to train a deep learning model enhances its macular pathology recognition capabilities. Bull Russian State Med Univ 4:28–33
Gupta IK, Choubey A, Choubey S (2022) Artifical intelligence with optimal deep learning enabled automated retinal fundus image classification model. Expert Syst 39(10):e13028
Tan JH et al (2018) Age-related macular degeneration detection using deep convolutional neural network. Future Gener Comput Syst 87:127–135
Khalid S et al (2021) Automated diagnosis system for age-related macular degeneration using hybrid features set from fundus images. Int J Imaging Syst Technol 31(1):236–252
Santos AM et al (2018) Semivariogram and semimadogram functions as descriptors for AMD diagnosis on SD-OCT topographic maps using support vector machine. Biomed Eng Online 17(1):160. https://doi.org/10.1186/s12938-018-0592-3
Article PubMed PubMed Central Google Scholar
Liu YY et al (2011) Computerized macular pathology diagnosis in spectral domain optical coherence tomography scans based on multiscale texture and shape features. Invest Ophthalmol Vis Sci 52(11):8316–8322. https://doi.org/10.1167/iovs.10-7012
Article PubMed PubMed Central Google Scholar
Mookiah MRK et al (2014) Automated diagnosis of age-related macular degeneration using greyscale features from digital fundus images. Comput Biol Med 53:55–64
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