Category weighted network and relation weighted label for diabetic retinopathy screening

Han, Zhen; Yang, Bin; Li, Zhuorong; Tong, Zhou

doi:10.1016/j.compbiomed.2022.106408

Cited by 9 publications

(2 citation statements)

References 33 publications

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“…The longer the course of diabetes, the higher the incidence of DR ( Huang et al, 2023 ). At present, the pathogenesis of DR is unclear, but glucose metabolism disorder is the root cause of DR ( Han et al, 2023 ). In the early stage of DR, patients with general ocular symptoms can experience various visual impairments with the development of the disease, among which flash sensation and vision loss are the most common ( Grauslund, 2022 ).…”

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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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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“…Currently, the pathogenesis of DR is not fully clarified; however, the root cause is glucose metabolism disorder [33] . Regarding its clinical manifestations, patients with DR often show no obvious symptoms in the early stages; however, with the development of the disease, patients with DR experience varying degrees of vision loss and blindness [34][35] . Fundus manifestations of DR include microhemangioma, bleeding spots, rigid exudation, retinal angiopathy, and optic neuropathy [36] .…”

Section: Introductionmentioning

confidence: 99%

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

Liu,

Fei,

Chen

et al. 2023

Int J Ophthalmol

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Diabetic retinopathy (DR) is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide. Early detection and treatment can effectively delay vision decline and even blindness in patients with DR. In recent years, artificial intelligence (AI) models constructed by machine learning and deep learning (DL) algorithms have been widely used in ophthalmology research, especially in diagnosing and treating ophthalmic diseases, particularly DR. Regarding DR, AI has mainly been used in its diagnosis, grading, and lesion recognition and segmentation, and good research and application results have been achieved. This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.

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Ensemble classification based optimized transfer learning feature method for early stage diagnosis of diabetic retinopathy

Kasım

2023

J Ambient Intell Human Comput

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Category weighted network and relation weighted label for diabetic retinopathy screening

Cited by 9 publications

References 33 publications

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

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

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

Ensemble classification based optimized transfer learning feature method for early stage diagnosis of diabetic retinopathy

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