Automated analysis of corneal nerve tortuosity in diabetes: implications for neuropathy detection

Klisser, Jacob; Tummanapalli, Shyam Sunder; Kim, Juno; Chiang, Jeremy Chung Bo; Khou, Vincent; Issar, Tushar; Naduvilath, Thomas; Poynten, Ann M.; Markoulli, Maria; Krishnan, Arun V.

doi:10.1080/08164622.2021.1940875

Cited by 6 publications

(7 citation statements)

References 35 publications

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“…Could this mean that tortuosity estimates are weighted towards longer nerves when they are visible? Consistent with this view, a simple model that weighted tortuosity estimates based on nerve fibre length was found to exhibit sound correspondence with human judgements of tortuosity and provided diagnostic leverage in the prediction of diabetes 11 .…”

Section: Introductionmentioning

confidence: 59%

In-vivo corneal confocal microscopy: Imaging analysis, biological insights and future directions

et al. 2023

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In-vivo corneal confocal microscopy is a powerful imaging technique which provides clinicians and researcher with the capabilities to observe microstructures at the ocular surfaces in significant detail. In this Mini Review, the optics and image analysis methods with the use of corneal confocal microscopy are discussed. While novel insights of neuroanatomy and biology of the eyes, particularly the ocular surface, have been provided by corneal confocal microscopy, some debatable elements observed using this technique remain and these are explored in this Mini Review. Potential improvements in imaging methodology and instrumentation are also suggested.

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Section: Introductionmentioning

confidence: 59%

In-vivo corneal confocal microscopy: Imaging analysis, biological insights and future directions

et al. 2023

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“…nerve fiber tortuosity is significantly greater in the severe neuropathy group than in the control, mild and moderate neuropathy patient groups. Klisser et al [17] also demonstrate a significant reduction in corneal nerve tortuosity in individuals with both T1DM (type 1) and T2DM (type 2) relative to controls, as well as a significant difference in tortuosity between diabetic participants with neuropathy compared to those without. In addition, Ma et al [11] conclude from a detailed statistical analysis that increased corneal nerve tortuosity is linked to frequencies of ocular discomfort, visual function disturbance, and tear film instability in dry eye disease.…”

Section: Introductionmentioning

confidence: 89%

“…In this paper, we grade corneal nerves into four tortuosity levels, as in [5], [16], since the four-level tortuosity highlights significant variation in the corneal nerve fibers of patients with diabetic neuropathy [8], [17] and dry eye disease [9], [11].…”

Section: Introductionmentioning

confidence: 99%

DeepGrading: Deep Learning Grading of Corneal Nerve Tortuosity

Mou

Liu

et al. 2022

IEEE Trans. Med. Imaging

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Accurate estimation and quantification of the corneal nerve fiber tortuosity in corneal confocal microscopy (CCM) is of great importance for disease understanding and clinical decision-making. However, the grading of corneal nerve tortuosity remains a great challenge due to the lack of agreements on the definition and quantification of tortuosity. In this paper, we propose a fully automated deep learning method that performs image-level tortuosity grading of corneal nerves, which is based on CCM images and segmented corneal nerves to further improve the grading accuracy with interpretability principles. The proposed method consists of two stages: 1) A pretrained feature extraction backbone over ImageNet is finetuned with a proposed novel bilinear attention (BA) module for the prediction of the regions of interest (ROIs) and coarse grading of the image. The BA module enhances the ability of the network to model long-range dependencies and global contexts of nerve fibers by capturing secondorder statistics of high-level features. 2) An auxiliary tortuosity grading network (AuxNet) is proposed to obtain an auxiliary grading over the identified ROIs, enabling the coarse and additional gradings to be finally fused together for more accurate final results. The experimental results show that our method surpasses existing methods in tortuosity grading, and achieves an overall accuracy of 85.64% in four-level classification. We also validate it over a clinical dataset, and the statistical analysis demonstrates a significant difference of tortuosity levels between healthy control and diabetes group. We have released a dataset with 1500 CCM images and their manual annotations of four tortuosity levels for public access. The code is available at: https://github.com/iMED-Lab/TortuosityGrading.

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“…Moreover, sub-basal nerve tortuosity is increased in the short term after corneal refractive surgery [21]. Tortuosity has also been investigated in healthy subjects [22], and in other diseases such as non-neurological autoimmune diseases [23], diabetic neuropathy [7,12,24], glaucoma [25], unilateral herpes zoster [26], acute acanthamoeba and fungal keratitis [27] and herpes simplex keratitis [28]. In order to establish a link between tortuosity and ocular or systemic anomalies, the degree of nerve fiber tortuosity has been commonly measured on an ordinal scale [8,29], which directly implies adding an element of subjectivity in the grading process.…”

Section: Introductionmentioning

confidence: 99%

“…The OSDI questionnaire was administered to all volunteers. This instrument allows DED screening [42] and also DED severity classification: "mild" (score [13][14][15][16][17][18][19][20][21][22], "moderate" (score [23][24][25][26][27][28][29][30][31][32] and "severe" (score 33-100).…”

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confidence: 99%

New Method for the Automated Assessment of Corneal Nerve Tortuosity Using Confocal Microscopy Imaging

et al. 2022

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An automated tool for corneal nerve fiber tortuosity quantification from in vivo confocal microscopy (IVCM) is described and evaluated. The method is a multi-stage process based on the splitting of the corneal nerve fibers into individual segments, whose endpoints are an extreme or intersection of white pixels on a binarized image. Individual segment tortuosity is quantified in terms of the arc-chord ratio. Forty-three IVCM images from 43 laser-assisted in situ keratomileusis (LASIK) surgery patients were used for evaluation. Images from symptomatic dry eye disease (DED) post-LASIK patients, with (n=16) and without (n=7) ocular pain, and non-DED post-LASIK controls (n=20) were assessed. The automated tortuosity measure was compared to a manual grading one, obtaining a moderate correlation (Spearman’s rank correlation coefficient = 0.49, p=0.0008). The new tortuosity index was significantly higher in post-LASIK patients with ocular pain than in control patients (p=0.001), while no significant differences were detected with manual measurement (p>0.28). The tortuosity quantification was positively correlated with the ocular surface disease index (OSDI) and a numeric rating scale (NRS) assessing pain (p=0.0012 and p=0.0051, respectively). The results show good performance of the proposed automated methodology for the evaluation of corneal nerve tortuosity.

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Automated analysis of corneal nerve tortuosity in diabetes: implications for neuropathy detection

Cited by 6 publications

References 35 publications

In-vivo corneal confocal microscopy: Imaging analysis, biological insights and future directions

In-vivo corneal confocal microscopy: Imaging analysis, biological insights and future directions

DeepGrading: Deep Learning Grading of Corneal Nerve Tortuosity

New Method for the Automated Assessment of Corneal Nerve Tortuosity Using Confocal Microscopy Imaging

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