Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Assessment of Diabetic Neuropathy (DN)
2.2. Assessment of Diabetic Retinopathy (DR)
2.3. Data Analysis
2.3.1. Dataset Splits
2.3.2. Dataset Processing
2.4. Training
2.4.1. Algorithm Evaluation
2.4.2. Demographic Analysis
3. Results
Model Performance
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Phase | Name | Description | Patients * | Images * |
---|---|---|---|---|
Training | trA | all images (patients both with and without DR) | 1081 | 17,028 |
trDR+ | only images of patients with DR | 93 | 1503 | |
trDR− | only images of patients without DR | 988 | 15,525 | |
Testing | tsA | all images (patients both with and without DR) | 121 | 1892 |
tsDR+ | only images of patients with DR | 11 | 165 | |
tsDR− | only images of patients without DR | 110 | 1727 | |
{trA} ∩ {tsA} = {Ø}; {trA} ∪ {tsA} = {A} {trDR+} ∩ {trDR−} = {Ø}; {trDR+} ∪ {trDR−} = {trA} {tsDR+} ∩ {tsDR−} = {Ø}; {tsDR+} ∪ {tsDR−} = {tsA} |
Hyper-Parameter | Values | Number of Values |
---|---|---|
Architecture | {Inception, Squeezenet, Densenet} | 3 |
Optimiser | {SGD, Adam} | 2 |
Learning rate | [10 × 10−6, 10 × 10−2] | 10 |
Momentum | {0.95, 0.99} | 2 |
Dropout | {0.3, 0.5, 0.7} | 3 |
Class rebalancing | {weighted loss, weighted sampling} | 2 |
Total number of combinations/models | 720 |
Models | Trained on: | Performance (AUC) on: | |
---|---|---|---|
Phase 1 | All models (see Table 2) | Training set, split 1 | Validation set, split 1 |
Phase 2 | Best model from Phase 1 | Training set, all splits | Test set (average AUC) |
Disease State | No Diabetic Retinopathy/Neuropathy | Diabetic Retinopathy | Diabetic Neuropathy | Combined | ||
---|---|---|---|---|---|---|
Variables | n = 1101 | n = 189 | n = 276 | n = 43 | p Value | |
Age | 55.71 (10.214) | 56.04 (10.057) | 57.55 (10.059) | 57.046 (10.127) | 0.074 | |
Gender(m/f) | 568/533 | 99/90 | 150/126 | 23/20 | 0.916 | |
Duration of diabetes(in years) | 3.75 (5.11) | 9.364 (6.20) | 7.756 (6.14) | 11.205 (6.166) | 0.00 | |
Hba1c | 7.90 (2.43) | 9.475 (2.22) | 8.424 (2.21) | 9.323 (2.229) | 0.00 | |
BMI range(mean) | 14–44 (25.89) | 15.41–51.95 (24.19) | 14.82–39.73 (25.03) | 16.65–33.75 (24.455) | 0.001 | |
Lipid Profile | ||||||
Serum Cholesterol mmol/L | 4.80 (1.05) | 4.862 (1.16) | 4.919 (0.965) | 5.11 (1.021) | 0.094 | |
Serum TGL cholesterol mmol/L | 1.75 (1.151) | 1.748 (1.006) | 1.719 (1.12) | 1.877 (0.918) | 0.271 | |
Serum HDL cholesterol mmol/L | 0.99 (0.254) | 1.046 (0.274) | 1.066 (0.262) | 1.054 (0.202) | 0.00 |
val | tsA | tsDR− | tsDR+ | |
---|---|---|---|---|
trA | 0.8013 ± 0.0257 | 0.7097 ± 0.0031 | 0.7105 ± 0.0032 | 0.8673 ± 0.0088 |
trDR− | 0.7842 ± 0.0334 | 0.6944 ± 0.0139 | 0.6941 ± 0.0145 | 0.8733± 0.0504 |
trDR+ | 0.6878 ± 0.0945 | 0.5993 ± 0.0410 | 0.5981 ± 0.0485 | 0.6805 ± 0.0587 |
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Cervera, D.R.; Smith, L.; Diaz-Santana, L.; Kumar, M.; Raman, R.; Sivaprasad, S. Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning. Diagnostics 2021, 11, 1943. https://doi.org/10.3390/diagnostics11111943
Cervera DR, Smith L, Diaz-Santana L, Kumar M, Raman R, Sivaprasad S. Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning. Diagnostics. 2021; 11(11):1943. https://doi.org/10.3390/diagnostics11111943
Chicago/Turabian StyleCervera, Diego R., Luke Smith, Luis Diaz-Santana, Meenakshi Kumar, Rajiv Raman, and Sobha Sivaprasad. 2021. "Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning" Diagnostics 11, no. 11: 1943. https://doi.org/10.3390/diagnostics11111943