Forecasting Progressive Trends in Keratoconus by Means of a Time Delay Neural Network
Abstract
:1. Introduction
2. Materials and Methods
2.1. Patients
2.2. Corneal Parameters Included
2.3. Suspect Progressive KC Definition
2.4. Data Preprocessing
2.4.1. Data Maximization Design: Triplets
2.4.2. Data Quality and Robustness to Errors
2.4.3. Noise Reduction and Normalization
2.5. Network Architecture
2.6. Data Split: Training and Test Datasets
2.7. Data Analysis
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
- EVICR.net European Vision Institute Clinical Research NetworkAIBILI, Azinhaga de Santa Comba, Celas, 3000-548 Coimbra, Portugal.
- Centre A—University Medical Center of Johannes Gutenberg–University (Germany)Katrin Lorenz, Katharina Bell, Anna Beck
- Centre B—Ghent University Hospital (Belgium)Bart Leroy, Elke Kreps
- Centre C—Antwerp University Hospital (Belgium)Jos Rozema, Marta Jiménez-García, Sorcha Ní Dhubhghaill, Ikram Issarti, Alejandra Consejo, Carina Koppen
- Centre D—Fondation Asile des Aveugles, Jules Gonin Eye Hospital (Switzerland)Kattayoon Hashemi
- Centre E—University Eye Clinic Maastricht (the Netherlands)Rudy MMA Nuijts, Magali Vandevenne, Frank JHM van den Biggelaar
- Centre F—Braga Hospital (Portugal)Tiago Monteiro, Rui Freitas
- Centre H—Justus Liebig University Giessen (Germany)Birgit Lorenz, Ekaterina Sokolenko, Francesco Luciani, Christine Mais
- Centre I—Tel Aviv Sourasky Medical Center (Israel)David Varssano, Eyal Cohen
Conflicts of Interest
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OPTION 1: Triplets without Error (n = 811; 121 Used for External Validation) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | AVG | |
SENS | 66.0% | 77.5% | 70.4% | 74.1% | 69.6% | 75.6% | 66.0% | 60.0% | 82.6% | 66.0% | 70.8% |
SPEC | 84.5% | 76.5% | 80.6% | 73.0% | 85.3% | 73.7% | 83.1% | 84.5% | 80.0% | 84.5% | 80.6% |
PPV | 75.0% | 62.0% | 74.5% | 71.7% | 74.4% | 63.0% | 73.3% | 73.2% | 71.7% | 75.0% | 71.4% |
NPV | 77.9% | 87.3% | 77.1% | 75.4% | 82.1% | 83.6% | 77.6% | 75.0% | 88.2% | 77.9% | 80.2% |
OPTION 2: Triplets with 1 Error Allowed (n = 1236, 169 Used for External Validation) | |||||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | AVG | |
SENS | 70.1% | 69.6% | 70.6% | 66.7% | 68.1% | 75.0% | 77.3% | 71.6% | 77.8% | 73.4% | 72.0% |
SPEC | 82.9% | 83.3% | 82.1% | 84.9% | 83.5% | 75.2% | 72.8% | 74.7% | 67.9% | 72.2% | 78.0% |
PPV | 81.3% | 78.6% | 80.0% | 72.4% | 75.4% | 67.1% | 64.6% | 68.8% | 59.0% | 69.9% | 71.7% |
NPV | 72.3% | 75.8% | 73.4% | 81.1% | 77.9% | 81.7% | 83.3% | 77.2% | 83.7% | 75.6% | 78.2% |
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Jiménez-García, M.; Issarti, I.; Kreps, E.O.; Ní Dhubhghaill, S.; Koppen, C.; Varssano, D.; Rozema, J.J.; on behalf of The REDCAKE Study Group. Forecasting Progressive Trends in Keratoconus by Means of a Time Delay Neural Network. J. Clin. Med. 2021, 10, 3238. https://doi.org/10.3390/jcm10153238
Jiménez-García M, Issarti I, Kreps EO, Ní Dhubhghaill S, Koppen C, Varssano D, Rozema JJ, on behalf of The REDCAKE Study Group. Forecasting Progressive Trends in Keratoconus by Means of a Time Delay Neural Network. Journal of Clinical Medicine. 2021; 10(15):3238. https://doi.org/10.3390/jcm10153238
Chicago/Turabian StyleJiménez-García, Marta, Ikram Issarti, Elke O. Kreps, Sorcha Ní Dhubhghaill, Carina Koppen, David Varssano, Jos J. Rozema, and on behalf of The REDCAKE Study Group. 2021. "Forecasting Progressive Trends in Keratoconus by Means of a Time Delay Neural Network" Journal of Clinical Medicine 10, no. 15: 3238. https://doi.org/10.3390/jcm10153238
APA StyleJiménez-García, M., Issarti, I., Kreps, E. O., Ní Dhubhghaill, S., Koppen, C., Varssano, D., Rozema, J. J., & on behalf of The REDCAKE Study Group. (2021). Forecasting Progressive Trends in Keratoconus by Means of a Time Delay Neural Network. Journal of Clinical Medicine, 10(15), 3238. https://doi.org/10.3390/jcm10153238