AIMS/HYPOTHESIS 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. METHODS 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. RESULTS 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). CONCLUSIONS/INTERPRETATION 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. DATA AVAILABILITY 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.

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基于人工智能的深度学习算法，使用角膜共聚焦显微镜诊断糖尿病性神经病：一项开发和验证研究。

目的/假说角膜共聚焦显微镜是一种快速的非侵入性眼科成像技术，可识别周围和中枢神经退行性疾病。但是，对角膜下基底神经丛形态的量化需要费时的手动注释或不太敏感的自动图像分析方法。我们旨在开发和验证一种基于人工智能的深度学习算法，用于量化与糖尿病性神经病的诊断有关的神经纤维特性，并将其与经过验证的自动化分析程序ACCMetrics进行比较。方法我们开发了一种深度学习算法，该算法采用具有数据增强功能的卷积神经网络，用于自动定量角膜下基底神经丛，以诊断糖尿病性神经病。使用高端图形处理器单元在1698个角膜共聚焦显微镜图像上对算法进行了训练；为了进行外部验证，已在2137张图像上进行了进一步测试。开发该算法以识别总神经纤维长度，分支点，尾巴点，神经节的数量和长度以及分形数。进行了敏感性分析，以确定ACCMetrics的AUC和我们用于诊断糖尿病性神经病的算法。结果我们的算法的类内相关系数在角膜神经纤维总长度（0.933 vs 0.825），每节平均长度（0​​.656 vs 0.325），分支点数量（0.891 vs 0.570），尾点数量方面优于ACCMetrics （0.623 vs 0.257），神经节数（0.878 vs 0.504）和分形数（0.927 vs 0.758）。此外，对于没有（n = 90）和（n = 132）神经病（由多伦多标准定义）的参与者分类，我们提出的算法实现了0.83的AUC，0.87的特异性和0.68的敏感性。结论/解释这些结果表明，我们的深度学习算法为定量角膜神经生物标记物提供了快速而出色的定位性能。该模型具有被纳入糖尿病神经病临床筛查程序的潜力。数据可用性可在http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Tortuosity%20Data%20Set.htm和http://bioimlab.dei.unipd.it上获得公开共享的角膜神经数据集（数据集1）。 /Corneal%20Nerve%20Data%20Set.htm。对于没有（n = 90）和有（n = 132）神经病（由多伦多标准定义）的参与者分类，评分标准为68。结论/解释这些结果表明，我们的深度学习算法为定量角膜神经生物标记物提供了快速而出色的定位性能。该模型具有被纳入糖尿病神经病临床筛查程序的潜力。数据可用性可在http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Tortuosity%20Data%20Set.htm和http://bioimlab.dei.unipd.it上获得公开共享的角膜神经数据集（数据集1）。 /Corneal%20Nerve%20Data%20Set.htm。对于没有（n = 90）和有（n = 132）神经病（由多伦多标准定义）的参与者分类，评分标准为68。结论/解释这些结果表明，我们的深度学习算法为定量角膜神经生物标记物提供了快速而出色的定位性能。该模型具有被纳入糖尿病神经病临床筛查程序的潜力。数据可用性可在http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Tortuosity%20Data%20Set.htm和http://bioimlab.dei.unipd.it上获得公开共享的角膜神经数据集（数据集1）。 /Corneal%20Nerve%20Data%20Set.htm。结论/解释这些结果表明，我们的深度学习算法为定量角膜神经生物标记物提供了快速而出色的定位性能。该模型具有被纳入糖尿病神经病临床筛查程序的潜力。数据可用性可在http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Tortuosity%20Data%20Set.htm和http://bioimlab.dei.unipd.it上获得公开共享的角膜神经数据集（数据集1）。 /Corneal%20Nerve%20Data%20Set.htm。结论/解释这些结果表明，我们的深度学习算法为定量角膜神经生物标记物提供了快速而出色的定位性能。该模型具有被纳入糖尿病神经病临床筛查程序的潜力。数据可用性可在http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Tortuosity%20Data%20Set.htm和http://bioimlab.dei.unipd.it上获得公开共享的角膜神经数据集（数据集1）。 /Corneal%20Nerve%20Data%20Set.htm。