Study of Retinal Biometric Systems with Respect to Feature Classification for Recognition and Diabetic Retinopathy

Prabhu, Srikanth; Chakraborty, Chandan; Banerjee, Rohini; Ray, Ajoy Kumar

doi:10.1166/jmihi.2011.1015

Cited by 6 publications

(2 citation statements)

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“…High-dose STZ-induced type 1 diabetic rats, low-dose STZ-induced combined with high-fat diet-induced type 2 diabetic rats, and spontaneous type 2 diabetic KKAy mice are some of the most common models used to study diabetes and its complications. These models are modeled by destroying pancreatic tissue or increasing the fat intake, resulting in a complete or relative deficiency in insulin secretion, leading to abnormal blood glucose and/or insulin resistance, which causes intracellular mitochondrial damage ( 14 ), abnormal vascular endothelial cell function, excessive inflammation and oxidative stress, and other pathological processes ( 8 , 15 ), ultimately leading to a disruption of the blood-retinal barrier and retinal vascular leakage ( 16 , 17 ), by which point the lesion has shifted from the neuronal cells to the vascular endothelium and pericytes. From this perspective, the conventional diabetic animal model is not a model of retinal neurodegenerative disease and the use of a conventional diabetic animal model is more suitable for evaluating the efficacy of drugs to improve vasculopathy than for evaluating the efficacy of drugs to delay retinal nerve cell degeneration.…”

Section: Discussionmentioning

confidence: 99%

Monosodium Glutamate-Induced Mouse Model With Unique Diabetic Retinal Neuropathy Features and Artificial Intelligence Techniques for Quantitative Evaluation

Liu

Huang²,

Sun

et al. 2022

Front. Immunol.

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ObjectiveTo establish an artificial intelligence-based method to quantitatively evaluate subtle pathological changes in retinal nerve cells and synapses in monosodium glutamate (MSG) mice and provide an effective animal model and technique for quantitative evaluation of retinal neurocytopathies.MethodsICR mice were subcutaneously injected with MSG to establish a model of metabolic syndrome. We then established a mouse model of type 1 diabetes, type 2 diabetes, and KKAy mouse model as control. The HE sections of the retina were visualized using an optical microscope. AI technology was used for quantitative evaluation of the retinal lesions in each group of rats. The surface area custom parameters of the retinal nerve fiber layer (RNFL), inner plexiform layer (IPL), inner nuclear layer (INL), and outer plexiform layer (OPL) were defined as SR, SIPL, SINL, and SOPL, respectively. Their heights were defined as HR, HIPL, HINL, and HOPL, and the number of ganglion cells was defined as A. Then, the attention-augmented fully convolutional Unet network was used to segment the retinal HE images, and AI technology to identify retinal neurocytopathies quantitatively.ResultsThe attention-augmented fully convolutional Unet network increased PA and IOU parameters for INL, OPL, RNFL, and ganglion cells and was superior in recognizing fine structures. A quantitative AI identification of the height of each layer of the retina showed that the heights of the IPL and INL of the MSG model were significantly less than those of the control groups; the retinas of the other diabetic models did not exhibit this pathological feature. The RNFLs of type 2 diabetes were thinner, and the characteristics of retinopathy were not obvious in the other animal models. The pathological changes seen on HE images were consistent with the results of the quantitative AI evaluation. Immunohistochemistry results showed that NMDAR2A, GluR2, and NRG1 were significantly downregulated in the retina of MSG mice.ConclusionsThe MSG retinopathy model is closely associated with neurotransmitter abnormalities and exhibits important characteristics of retinal neurodegeneration, making it suitable for studying retinal neurocytopathies. The AI recognition technology for retinal images established in the present study can be used for the quantitative and objective evaluation of drug efficacy.

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Section: Discussionmentioning

confidence: 99%

Monosodium Glutamate-Induced Mouse Model With Unique Diabetic Retinal Neuropathy Features and Artificial Intelligence Techniques for Quantitative Evaluation

Liu

Huang²,

Sun

et al. 2022

Front. Immunol.

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show abstract

“…Type 2 diabetes mellitus is associated with cardiovascular diseases [3], neuropathy [4], peripheral vascular insufficiency [5], lung dysfunction [6], and retinopathy [7].…”

Section: Introductionmentioning

confidence: 99%

Empirical mode decomposition analysis of near-infrared spectroscopy muscular signals to assess the effect of physical activity in type 2 diabetic patients

Molinari

Martis²,

Acharya

et al. 2015

Computers in Biology and Medicine

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Entropy analysis of muscular near-infrared spectroscopy (NIRS) signals during exercise programme of type 2 diabetic patients: Quantitative assessment of muscle metabolic pattern

Molinari

Acharya

Martis

et al. 2013

Computer Methods and Programs in Biomedicine

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Study of Retinal Biometric Systems with Respect to Feature Classification for Recognition and Diabetic Retinopathy

Cited by 6 publications

References 0 publications

Monosodium Glutamate-Induced Mouse Model With Unique Diabetic Retinal Neuropathy Features and Artificial Intelligence Techniques for Quantitative Evaluation

Monosodium Glutamate-Induced Mouse Model With Unique Diabetic Retinal Neuropathy Features and Artificial Intelligence Techniques for Quantitative Evaluation

Empirical mode decomposition analysis of near-infrared spectroscopy muscular signals to assess the effect of physical activity in type 2 diabetic patients

Entropy analysis of muscular near-infrared spectroscopy (NIRS) signals during exercise programme of type 2 diabetic patients: Quantitative assessment of muscle metabolic pattern

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