Author Correction: A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography

Ryu, Gahyung; Lee, Kyung-Min; Park, Dong-Geun; Park, Sang Hyun; Sagong, Min

doi:10.1038/s41598-022-25510-w

Cited by 2 publications

(2 citation statements)

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“…These images capture essential structures within the eye, including blood vessels, the optic disc, and the macula. The information derived from these structures in retinal images is instrumental in diagnosing and treating a range of retinal conditions [3] , [4] , [5] .…”

Section: Data Descriptionmentioning

confidence: 99%

Multimodal dataset using OCTA and fundus images for the study of diabetic retinopathy

Bidwai,

Gite,

Gupta

et al. 2024

Data in Brief

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Section: Data Descriptionmentioning

confidence: 99%

Multimodal dataset using OCTA and fundus images for the study of diabetic retinopathy

Bidwai,

Gite,

Gupta

et al. 2024

Data in Brief

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“…Ahn et al [18] obtained a total of 1,542 photos (786 normal controls, 467 advanced glaucoma, and 289 early glaucoma patients) by fundus photography. Datasets were used to construct simple logistic classification and convolutional neural networks using TensorFlow.…”

Section: And DL In Ophthalmologymentioning

confidence: 99%

Current roles of artificial intelligence in ophthalmology

Keskinbora

2023

Exploration of Medicine

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Artificial intelligence (AI) studies are increasingly reporting successful results in the diagnosis and prognosis prediction of ophthalmological diseases as well as systemic disorders. The goal of this review is to detail how AI can be utilized in making diagnostic predictions to enhance the clinical setting. It is crucial to keep improving methods that emphasize clarity in AI models. This makes it possible to evaluate the information obtained from ocular imaging and easily incorporate it into therapeutic decision-making procedures. This will contribute to the wider acceptance and adoption of AI-based ocular imaging in healthcare settings combining advanced machine learning and deep learning techniques with new developments. Multiple studies were reviewed and evaluated, including AI-based algorithms, retinal images, fundus and optic nerve head (ONH) photographs, and extensive expert reviews. In these studies, carried out in various countries and laboratories of the world, it is seen those complex diagnoses, which can be detected systemic diseases from ophthalmological images, can be made much faster and with higher predictability, accuracy, sensitivity, and specificity, in addition to ophthalmological diseases, by comparing large numbers of images and teaching them to the computer. It is now clear that it can be taken advantage of AI to achieve diagnostic certainty. Collaboration between the fields of medicine and engineering foresees promising advances in improving the predictive accuracy and precision of future medical diagnoses achieved by training machines with this information. However, it is important to keep in mind that each new development requires new additions or updates to various social, psychological, ethical, and legal regulations.

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Author Correction: A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography

Cited by 2 publications

References 0 publications

Multimodal dataset using OCTA and fundus images for the study of diabetic retinopathy

Multimodal dataset using OCTA and fundus images for the study of diabetic retinopathy

Current roles of artificial intelligence in ophthalmology

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