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Keywords {🔍}

corneal, neuropathy, nerve, pubmed, google, images, scholar, image, diabetes, diabetic, length, confocal, cnf, microscopy, ldla, accm, analysis, learning, ccm, dataset, segmentation, deep, study, total, results, participants, model, clinical, group, manual, points, data, central, number, university, algorithm, diagnosis, fibre, nerves, fig, dla, automated, type, lcnn, cas, peripheral, sensitivity, early, petropoulos, quantification,

Topics {✒️}

5em \mathrm{sd}=\sqrt{\frac{1}{n-1}\sum \limits_i{\left $$ \mathrm{rmse}=\sqrt{\frac{1}{ /corneal%20nerve%20tortuosity%20data%20set artificial intelligence-based dla intra-epidermal nerve fibres deep-learning-based approaches make artificial intelligence-based dlas /corneal%20nerve%20data%20set thermal thresholds/sudomotor/autonomic article download pdf artificial intelligence-based achieve full-image segmentation conventional small-fiber tests hand-crafted features increases mountain view convolutional neural network traditional machine-learning burden proposed u-net architecture diagnose small-fibre neuropathy single u-net model proposed u-net model small nerve fibres designing hand-crafted features age-related reference range multi-scale quantitative morphology small fiber damage pixel-wise segmentation map image-level segmentation result convolutional neural networks corneal confocal microscopy vivo confocal microscopy time-consuming manual annotation conventional machine-learning methods efficient pixel-wise classification small fiber neuropathy corneal nerve fibre central neurodegenerative disease deep neural networks dual-model automatic detection small-fibre dysfunction manual ground-truth analysis peripheral nerve society corneal nerve morphology unmyelinated nerve fibres nerve fiber loss davide borroni u-net networks vito romano corneal nerve segmentation nerve fibre length

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         headline:An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study
         description:Corneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either time-consuming manual annotation or a less-sensitive automated image analysis approach. We aimed to develop and validate an artificial intelligence-based, deep learning algorithm for the quantification of nerve fibre properties relevant to the diagnosis of diabetic neuropathy and to compare it with a validated automated analysis program, ACCMetrics. Our deep learning algorithm, which employs a convolutional neural network with data augmentation, was developed for the automated quantification of the corneal sub-basal nerve plexus for the diagnosis of diabetic neuropathy. The algorithm was trained using a high-end graphics processor unit on 1698 corneal confocal microscopy images; for external validation, it was further tested on 2137 images. The algorithm was developed to identify total nerve fibre length, branch points, tail points, number and length of nerve segments, and fractal numbers. Sensitivity analyses were undertaken to determine the AUC for ACCMetrics and our algorithm for the diagnosis of diabetic neuropathy. The intraclass correlation coefficients for our algorithm were superior to those for ACCMetrics for total corneal nerve fibre length (0.933 vs 0.825), mean length per segment (0.656 vs 0.325), number of branch points (0.891 vs 0.570), number of tail points (0.623 vs 0.257), number of nerve segments (0.878 vs 0.504) and fractals (0.927 vs 0.758). In addition, our proposed algorithm achieved an AUC of 0.83, specificity of 0.87 and sensitivity of 0.68 for the classification of participants without (n = 90) and with (n = 132) neuropathy (defined by the Toronto criteria). These results demonstrated that our deep learning algorithm provides rapid and excellent localisation performance for the quantification of corneal nerve biomarkers. This model has potential for adoption into clinical screening programmes for diabetic neuropathy. The publicly shared cornea nerve dataset (dataset 1) is available at http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Tortuosity%20Data%20Set.htm and http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Data%20Set.htm .
         datePublished:2019-11-12T00:00:00Z
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            Corneal confocal microscopy
            Corneal nerve
            Deep learning
            Diabetic neuropathy
            Image processing and analysis
            Image segmentation
            Ophthalmic imaging
            Small nerve fibres
            Internal Medicine
            Metabolic Diseases
            Human Physiology
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      headline:An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study
      description:Corneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either time-consuming manual annotation or a less-sensitive automated image analysis approach. We aimed to develop and validate an artificial intelligence-based, deep learning algorithm for the quantification of nerve fibre properties relevant to the diagnosis of diabetic neuropathy and to compare it with a validated automated analysis program, ACCMetrics. Our deep learning algorithm, which employs a convolutional neural network with data augmentation, was developed for the automated quantification of the corneal sub-basal nerve plexus for the diagnosis of diabetic neuropathy. The algorithm was trained using a high-end graphics processor unit on 1698 corneal confocal microscopy images; for external validation, it was further tested on 2137 images. The algorithm was developed to identify total nerve fibre length, branch points, tail points, number and length of nerve segments, and fractal numbers. Sensitivity analyses were undertaken to determine the AUC for ACCMetrics and our algorithm for the diagnosis of diabetic neuropathy. The intraclass correlation coefficients for our algorithm were superior to those for ACCMetrics for total corneal nerve fibre length (0.933 vs 0.825), mean length per segment (0.656 vs 0.325), number of branch points (0.891 vs 0.570), number of tail points (0.623 vs 0.257), number of nerve segments (0.878 vs 0.504) and fractals (0.927 vs 0.758). In addition, our proposed algorithm achieved an AUC of 0.83, specificity of 0.87 and sensitivity of 0.68 for the classification of participants without (n = 90) and with (n = 132) neuropathy (defined by the Toronto criteria). These results demonstrated that our deep learning algorithm provides rapid and excellent localisation performance for the quantification of corneal nerve biomarkers. This model has potential for adoption into clinical screening programmes for diabetic neuropathy. The publicly shared cornea nerve dataset (dataset 1) is available at http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Tortuosity%20Data%20Set.htm and http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Data%20Set.htm .
      datePublished:2019-11-12T00:00:00Z
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      pageStart:419
      pageEnd:430
      sameAs:https://doi.org/10.1007/s00125-019-05023-4
      keywords:
         Corneal confocal microscopy
         Corneal nerve
         Deep learning
         Diabetic neuropathy
         Image processing and analysis
         Image segmentation
         Ophthalmic imaging
         Small nerve fibres
         Internal Medicine
         Metabolic Diseases
         Human Physiology
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      name:Data Science Institute, Lancaster University, Lancaster, UK
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      name:Department of Ophthalmology, Riga Stradins University, Riga, Latvia
      name:Department of Ophthalmology, Peking University Third Hospital, Beijing, China
      name:St Paul’s Eye Unit, Royal Liverpool University Hospital, Liverpool, UK
      name:Cixi Institute of Biomedical Engineering, Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo, China
      name:Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, USA
      name:Department of Endocrinology and Diabetes, University Hospital Aintree, Liverpool, UK
      name:Department of Ophthalmology, Peking University Third Hospital, Beijing, China
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      name:Department of Eye and Vision Science, University of Liverpool, Liverpool, UK
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