Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship
Abstract
:1. Introduction
2. Corneal Confocal Microscopy
2.1. Corneal Anatomy
2.2. Corneal Confocal Microscopy
3. The Diagnostic Efficacy of Corneal Confocal Microscopy in Peripheral Neuropathies
4. Beyond Diabetic Peripheral Neuropathy
4.1. CCM in Other Peripheral Neuropathies
4.2. CCM in Central Neurodegenerative Disease
5. CCM Image Acquisition and Analysis
5.1. Manual Analysis
5.2. Automated Analysis
6. Fractal Dimension
6.1. Fractal Dimension in Diabetic Retinopathy
6.2. Fractal Deimention in Corneal Confocal Microscopy Images
7. Artificial Intelligence (AI)
7.1. Artificial Intelligence and Deep Learning
7.2. Artificial Intelligence and Opthalmology
7.3. Artificial Intelligence and Diabetic Retinopathy Screening
8. AI in CCM
8.1. Technical Aspects of AI in CCM
8.2. AI Models in CCM
8.3. AI in Diabetic Neuropathy
9. Future Clinical Applications
10. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | artificial intelligence |
ANN | artificial neural networks |
CCM | corneal confocal microscopy |
CIPD | chronic inflammatory demyelinating polyneuropathy |
CIPN | chemotherapy-induced peripheral neuropathy |
CNBD | corneal nerve branch density |
CNFD | corneal nerve fibre density |
CNFL | corneal nerve fibre length |
CNFrD | corneal nerve fractal dimension |
DLA | deep learning algorithm |
DPN | diabetic peripheral neuropathy |
DR | diabetic retinopathy |
HIV | human immunodeficiency virus |
IENFD | intra-epidermal nerve fibre density |
IWL | inferior whorl length |
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Citation | Participants | Reference Standard | Index Test Threshold | Test and Target Condition | AUC | Sensitivity | Specificity |
---|---|---|---|---|---|---|---|
Diabetic Peripheral Neuropathy | |||||||
Perkins et al., 2018 [42] | Total = 998 T1D = 516 T2D = 482 | Toronto Criteria Confirmed DPN | 12.5 mm/mm2 | Automated CNFL T1D—DPN | 0.77 | 73 | 69 |
12.3 mm/mm2 | Automated CNFL T2D—DPN | 0.68 | 69 | 63 | |||
12.3 mm/mm2 | Automated CNFL T1D and T2D—DPN | 0.77 | 67 | 66 | |||
Total < 8.6 mm/mm2 | Automated CNFL T1D and T2D—DPN | - | 88 | 88 | |||
Alam et al., 2017 [40] | T1D with neuropathy = 31 Control Participants = 27 | Toronto Criteria Confirmed DPN | 25 no/mm2 36.5 no/mm2 16.8 mm/mm2 | CNFD—DPN | 0.81 | 77 | 79 |
CNBD—DPN | 0.67 | 58 | 79 | ||||
CNFL—DPN | 0.74 | 61 | 86 | ||||
Chen et al., 2015 [41] | T1D = 63 Control = 26 | Toronto Criteria Confirmed DPN | 2 SD below the mean of the control group | Manual | |||
CNFD—DPN | 0.82 | 82 | 71 | ||||
CNFL—DPN | 0.70 | 59 | 74 | ||||
CNBD—DPN | 0.59 | 17 | 96 | ||||
Automated | |||||||
CNFD—DPN | 0.80 | 60 | 83 | ||||
CNFL—DPN | 0.77 | 59 | 80 | ||||
CNBD—DPN | 0.80 | 29 | 98 | ||||
Edwards et al., 2014 [47] | DM = 231 Control = 61 | Toronto Criteria Confirmed DPN | - | CNFL | 0.64 | 32 | 87 |
- | Tortuosity-standardised CNFL | 0.67 | 38 | 88 | |||
Wang et al., 2021 [43] | Total = 220 | Toronto Criteria Confirmed DPN | <15.3 mm/mm2 | CNFL | 0.70 | 80 | 59 |
Control = 48 | <39 no/mm2 | CNBD | 0.66 | 78 | 52 | ||
T2D = 172 | <25.68 n/mm2 | CNFD | 0.67 | 85 | 47 | ||
Other Peripheral Neuropathies | |||||||
Zhang et al., 2021 [48] | TTR-FAP = 15 Control = 15 | Genetically Confirmed TTR-FAP | <17.99 mm/mm2 | CNFL | 0.88 | 80 | 93 |
<21.95 mm/mm2 | IWL | 0.89 | 86 | 80 | |||
Central Peripheral Neuropathies | |||||||
Che et al., 2021 [49] | Total = 82 | Clinically confirmed PD | <10.08 mm/mm2 | CNFL | 0.67 | 85 | 45 |
PD = 42 | <22.85 n/mm2 | CNFD | 0.96 | 95 | 88 | ||
Control = 40 | <26.72 n/mm2 | CNBD | 0.69 | 92 | 52 | ||
Fernandes et al., 2021 [50] | Total = 82 MS = 60 Control = 22 | Clinically confirmed MS | - | CNFD | 0.84 | - | - |
- | CNBD | 0.84 | - | - | |||
- | CNFL | 0.74 | - | - | |||
- | CNFT | 0.72 | - | - |
Parameter | Description | Unit of Measurement |
---|---|---|
Corneal nerve fibre length (CNFL) | Length of all main nerve fibres and branches | mm/mm2 |
Corneal nerve fibre density (CNFD) | Number of main nerve fibres | no/mm2 |
Corneal nerve branch density (CNBD) | Number of main nerve fibre branches | no/mm2 |
Citation | Participants | No. of Images | Study Methodology | Population | AUC | Sensitivity | Specificity | Classification Accuracy | Results Summary |
---|---|---|---|---|---|---|---|---|---|
Scarpa et al., 2019 and Scarpa et al., 2020 [89,91] | Total = 100 DPN = 50 Control = 50 | Total = 600; Training = 480; Cross-validation = 600; Evaluation = 120 | CNN | Neuropathy vs. Control (single block) | - | 98 | 96 | 97 | CNN identifies ROI allowing multiple images to be binarised into two separate categories demonstrating diagnostic efficacy |
Neuropathy vs. Control (whole subject) | - | 98 | 94 | 96 | |||||
Williams et al., 2020 [90] | Total = 222 DPN+ve = 132 DPN-ve = 90 | Images used for training the Liverpool CNN Total = 1698; | CNN and DLA | DPN+ve vs. DPN-ve | 0.83 | 68 | 87 | - | The Liverpool CNN and Liverpool DLA can quantify corneal nerve morphometrics in participants with confirmed DPN demonstrating diagnostic efficacy |
External validation of the CNN/DLA Total =1578; Images evaluated using the Liverpool CNN/DLA; | |||||||||
Participants with and without DPN as per the Toronto expert criteria included. Total images = 2137 | |||||||||
Salahouddin et al., 2021 [130] | Total = 108 Control = 21 DPN+ve = 25 DPN−ve = 62 | Training = 174; Validation = 534 | DL ANFIS | DPN−ve cs Control | 0.86 (0.77–0.94) | 84 | 71 | - | Based on CCM images alone ANFIS classified 43% of participants as DPN+ve demonstrating diagnostic utility |
DPN−ve vs. DPN+ve | 0.95 (0.91–0.99) | 92 | 80 | - | |||||
Control vs. DPN+ve | 1.0 (0.99–1.0) | 100 | 95 | - | |||||
Preston et al., 2022 [92] | Total = 369 Control = 90 DPN+ve = 130 DPN−ve = 149 | Training = 245; Validation = 84; Test = 40 | DLA | Control | - | 100 | - | 100 | Based on a single CCM image without pre-processing DLA can faithfully classify participants into controls, DPN+ve and DPN−ve categories demonstrating diagnostic utility and accuracy |
DPN-ve | - | 85 | - | 85 | |||||
DPN+ve | - | 83 | - | 83 |
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Alam, U.; Anson, M.; Meng, Y.; Preston, F.; Kirthi, V.; Jackson, T.L.; Nderitu, P.; Cuthbertson, D.J.; Malik, R.A.; Zheng, Y.; et al. Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship. J. Clin. Med. 2022, 11, 6199. https://doi.org/10.3390/jcm11206199
Alam U, Anson M, Meng Y, Preston F, Kirthi V, Jackson TL, Nderitu P, Cuthbertson DJ, Malik RA, Zheng Y, et al. Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship. Journal of Clinical Medicine. 2022; 11(20):6199. https://doi.org/10.3390/jcm11206199
Chicago/Turabian StyleAlam, Uazman, Matthew Anson, Yanda Meng, Frank Preston, Varo Kirthi, Timothy L. Jackson, Paul Nderitu, Daniel J. Cuthbertson, Rayaz A. Malik, Yalin Zheng, and et al. 2022. "Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship" Journal of Clinical Medicine 11, no. 20: 6199. https://doi.org/10.3390/jcm11206199