Clinical characteristics and outcomes in patients with retinal vein occlusion

Romero, Miguel Martín; Rosa, Vladimir; Rodríguez, Pablo Demelo; Visonà, Adriana; Farfán-Sedano, Ana Isabel; Sifre, Maria Luisa Peris; Skride, Andris; Usandizaga, Esther; Meireles, J.; Monréal, Manuel

doi:10.1177/1358863x221122505

Cited by 2 publications

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“…If RVO is not treated promptly and effectively, it is likely to cause serious complications, resulting in severe and irreversible visual impairment and even blindness [3][4] . The main fundus changes on color fundus photographs (CFPs) of patients with RVO include retinal hemorrhage, abnormal tortuous dilatation of retinal vessels, cotton velvet spots, and hard exudation [5][6] . The disease can also cause a variety of eye complications, such as retinal macular edema (RME), optic neuropathy, neovascular glaucoma, and traction retinal detachment [3][4][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

Ji,

Liu,

Xie

et al. 2024

Int J Ophthalmol

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AIM: To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs). METHODS: Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets, and used to train, verify and test the diagnostic model of RVO. All the images were divided into four categories [normal, central retinal vein occlusion (CRVO), branch retinal vein occlusion (BRVO), and macular retinal vein occlusion (MRVO)] by three fundus disease experts. Swin Transformer was used to build the RVO diagnosis model, and different types of RVO diagnosis experiments were conducted. The model's performance was compared to that of the experts. RESULTS: The accuracy of the model in the diagnosis of normal, CRVO, BRVO, and MRVO reached 1.000, 0.978, 0.957, and 0.978; the specificity reached 1.000, 0.986, 0.982, and 0.976; the sensitivity reached 1.000, 0.955, 0.917, and 1.000; the F1-Sore reached 1.000, 0.955 0.943, and 0.887 respectively. In addition, the area under curve of normal, CRVO, BRVO, and MRVO diagnosed by the diagnostic model were 1.000, 0.900, 0.959 and 0.970, respectively. The diagnostic results were highly consistent with those of fundus disease experts, and the diagnostic performance was superior. CONCLUSION: The diagnostic model developed in this study can well diagnose different types of RVO, effectively relieve the work pressure of clinicians, and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

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Section: Introductionmentioning

confidence: 99%

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

Ji,

Liu,

Xie

et al. 2024

Int J Ophthalmol

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

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2024

Int J Ophthalmol

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AIM: To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs). • METHODS: Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets, and used to train, verify and test the diagnostic model of RVO. All the images were divided into four categories [normal, central retinal vein occlusion (CRVO), branch retinal vein occlusion (BRVO), and macular retinal vein occlusion (MRVO)] by three fundus disease experts. Swin Transformer was used to build the RVO diagnosis model, and different types of RVO diagnosis experiments were conducted. The model's performance was compared to that of the experts.• RESULTS: The accuracy of the model in the diagnosis of normal, CRVO, BRVO, and MRVO reached 1.000, 0.978, 0.957, and 0.978; the specificity reached 1.000, 0.986, 0.982, and 0.976; the sensitivity reached 1.000, 0.955, 0.917, and 1.000; the F1-Sore reached 1.000, 0.955 0.943, and 0.887 respectively. In addition, the area under curve of normal, CRVO, BRVO, and MRVO diagnosed by the diagnostic model were 1.000, 0.900, 0.959 and 0.970, respectively. The diagnostic results were highly consistent with those of fundus disease experts, and the diagnostic performance was superior.• CONCLUSION: The diagnostic model developed in this study can well diagnose different types of RVO, effectively relieve the work pressure of clinicians, and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

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Clinical characteristics and outcomes in patients with retinal vein occlusion

Cited by 2 publications

References 10 publications

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

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