Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Literature Search Strategy
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- Full text original papers that evaluated machine learning algorithms in the diagnosis of KC;
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- No limit on the year of publication was applied;
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- Publications only in the English language were included;
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- Publications in which KC was the only corneal condition evaluated.
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- Criteria for exclusion:
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- Publications evaluating other corneal diseases without focusing only on KC;
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- Publications evaluating the efficacy of machine learning in treating KC (treatments);
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- Review papers;
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- Publications in which no machine learning algorithms were included, but only statistical research was undertaken;
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- Non-English publications.
2.2. Data Extraction
2.3. Reporting Completeness of Machine Learning Studies in KC
2.4. Statistical Methods
2.5. Outcomes Measure
3. Results
3.1. Search Collection
3.2. Search Characteristics
3.3. Detecting KC from Controls and Meta-Analysis
3.4. Detecting Early KC from Controls and Meta-Analysis
3.5. Detection of Different KC Severities
3.6. Reporting Completeness of Machine Learning Studies in KC
4. Discussion
4.1. Reporting Completeness of Machine Learning Studies in KC
4.2. Limitations in the Current Literature
4.3. Approach for Future Studies
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Study Objectives | First Author | Year | No. of Centers Involved (Country) | Sample Size | No. of KC/Early KC Eyes | Machine Learning Method/s Used | Data Type (No. of Parameters) | Corneal Imaging Modality | Evaluation Methods |
---|---|---|---|---|---|---|---|---|---|
Detect KC eyes from controls | Maeda et al. [12] | 1995 | 1 (USA) | 176 | 44 | Combined discriminant analysis and classification tree | P (8) | TMS-1 | Internal |
Kalin et al. [16] | 1996 | NR | 106 | 5 | Combined discriminant analysis and classification tree | P (8) | TMS-1 | Validation study | |
Rabinowitz et al. [17] | 1998 | 1 (USA) | 241 | 99 | Linear discriminant analysis | P (5) | TMS-1 | Internal | |
Twa et al. [18] | 2005 | NR (USA) | 244 | 112 | Decision tree | P (36) | Keratron | Internal | |
Bessho et al. [19] | 2006 | 2 (Japan) | 165 | 63 | logistic regression | P (na) | Orbscan II | External | |
Saad et al. [20] | 2010 | NR | 143 | 31 | Discriminant analysis | P (51) | Orbscan IIz | Internal | |
Smadja et al. [21] | 2013 | 1 (France) | 325 | 148 | Decision tree | P (55) | GALILEI | Internal | |
Mahmoud et al. [22] | 2013 | 3 (Colombia, USA, Switzerland) | 407 | 163 | logistic regression | P (na) | GALILEI | External | |
Saad et al. [23] | 2014 | 1 (France) | 166 | 64 | Discriminant analysis | P (7) | Orbscan IIz | Internal | |
Silverman et al. [24] | 2014 | 1 (UK) | 204 | 74 | Multiple methods | P (161) | Artemis-1 | Internal | |
Koprowski et al. [25] | 2015 | 1 (Brazil) | 746 | 477 | Decision tree | P (11) | Corvis | Internal | |
Shetty et al. [26] | 2015 | 1 (India) | 128 | 85 | Logistic regression | P (na) | Pentacam | Internal | |
Kovacs et al. [27] | 2016 | 1 (Hungary) | 120 | 60 | Neural network | P (na) | Pentacam HR | Internal | |
Ruiz et al. [28] | 2016 | 1 (Belgium) | 648 | 454 | Support vector machine | P (22) | Pentacam HR | Internal | |
Ambrosio et al. [29] | 2017 | 2 (Brazil, Italy) | 756 | 276 | Multiple methods | P (na) | Pentacam HR & Corvis ST | Internal | |
Silverman et al. [30] | 2017 | 1 (USA) | 141 | 30 | Discriminant analysis | P (240) | Artemis-1 & Pentacam | Internal | |
Lopes et al. [31] | 2018 | 5 (UK, Brazil, Italy, USA) | 3648 | 370 | Multiple methods | P (na) | Pentacam | Internal & External | |
Chandapura et al. [32] | 2019 | NR | 439 | 218 including 102 early KC | Random forest | P (27) | Pentacam & OCT | Internal | |
* Dos Santos et al. [41] | 2019 | 1 (Austria) | 142 | 70 | Convolutional neural network | I | OCT | Internal | |
Issarti et al. [33] | 2019 | 1 (Belgium) | 624 | 312 | Neural network | P (28) | Pentacam | Internal | |
Kamiya et al. [42] | 2019 | 1 (Japan) | 543 | 304 | Convolutional neural network | I | AS-OCT | Internal | |
* Lavric et al. [43] | 2019 | NR | 3000 | 1500 | Convolutional neural network | I | SyntEyes model | Internal | |
Leão et al. [34] | 2019 | 2 (Brazil, Italy) | 574 | 223 | Discriminant analysis | P (na) | Corvis ST | NR | |
Bolarin et al. [35] | 2020 | 1 (Spain) | 169 | 107 | logistic regression | P | Sirius | Internal | |
Castro-Luna et al. [36] | 2020 | 1 (Spain) | 60 | 30 | Naive Bayes | P | CSO | Internal | |
* Issarti et al. [37] | 2020 | 2 (Belgium) | 812 | 508 | Neural Network | P (90) | Pentacam HR | Internal & External | |
Kuo et al. [44] | 2020 | 1 (Taiwan) | 326 | 170 | Convolutional neural network | I | TMS-4 | Internal | |
Lavric et al. [38] | 2020 | NR | 3151 | 1181 including 791 early KC | Multiple methods | P (443) | SS-1000 CASIA OCT | Internal | |
* Shi et al. [39] | 2020 | 1 (China) | 121 | 38 | Neural network | P (49) | UHR-OCT & Pentacam HR | Internal | |
Velazquez-Blazquez et al. [40] | 2020 | 1 (Spain) | 178 | 104 including 61 early KC | Logistic regression | P (27) | Sirius | Internal | |
Detect early KC eyes from controls | Saad et al. [20] | 2010 | NR | 143 | 40 | Discriminant analysis | P (51) | Orbscan IIz | Internal |
Smadja et al. [21] | 2013 | 1 (France) | 224 | 47 | Decision tree | P (55) | GALILEI | Internal | |
* Ventura et al. [45] | 2013 | NR (Brazil) | 204 | 68 | Neural network | P (41) | Ocular Response Analyzer | Internal | |
Chan et al. [46] | 2015 | 1 (Singapore) | 128 | 24 | Discriminant analysis | P (na) | Orbscan IIz | Validation study | |
Kovacs et al. [27] | 2016 | 1 (Hungary) | 75 | 15 | Neural network | P (na) | Pentacam HR | Internal | |
Ruiz et al. [28] | 2016 | 1 (Belgium) | 261 | 67 | Support vector machine | P (22) | Pentacam HR | Internal | |
Ambrosio et al. [29] | 2017 | 2 (Brazil, Italy) | 574 | 94 | Multiple methods | P (na) | Pentacam HR & Corvis ST | Internal | |
Xu et al. [47] | 2017 | 1 (China) | 363 | 77 | Discriminant analysis | P (na) | Pentacam HR | Internal | |
Lopes et al. [31] | 2018 | 5 (UK, Brazil, Italy, USA) | 3537 | 259 | Multiple methods | P (na) | Pentacam | Internal & External | |
Issarti et al. [33] | 2019 | 1 (Belgium) | 389 | 77 | Neural network | P (28) | Pentacam | Internal | |
Cao et al. [48] | 2020 | 1 (Australia) | 88 | 49 | Multiple methods | P (11) | Pentacam | Internal | |
* Issarti et al. [37] | 2020 | 2 (Belgium) | 812 | 117 | Neural Network | P (90) | Pentacam HR | Internal & External | |
* Kuo et al. [44] | 2020 | 1 (Taiwan) | 354 | 28 | Convolutional neural network | I | TMS-4 | Internal | |
* Shi et al. [39] | 2020 | 1 (China) | 121 | 33 | Neural network | P (49) | UHR-OCT & Pentacam HR | Internal | |
KC Severity | Yousefi et al. [49] | 2018 | multi-center (Japan) | 3156 | Density-based clustering | P (420) | CASIA OCT | NA |
Imaging Modalities | Pooled Sensitivity | Pooled Specificity |
---|---|---|
Pentacam (n = 5) | 0.987 (95% CI 0.971–0.994) | 0.989 (95% CI 0.963–0.997) |
TMS (n = 4) | 0.943 (95% CI 0.897–0.969) | 0.978 (95% CI 0.954–0.989) |
Orbscan (n = 3) | 0.947 (95% CI 0.886–0.976) | 0.983 (95% CI 0.917–0.997) |
Pooled total (n = 26) | 0.970 (95% CI 0.949–0.982) | 0.985 (95% CI 0.971–0.993) |
Imaging Modalities | Pooled Sensitivity | Pooled Specificity |
---|---|---|
Pentacam (n = 6) | 0.882 (95% CI 0.795–0.935) | 0.935 (95% CI 0.874–0.967) |
Orbscan (n = 2) | 0.842 (95% CI 0.504–0.965) | 0.958 (95% CI 0.821–0.991) |
Pooled total (n = 10) | 0.882 (95% CI 0.822–0.923) | 0.947 (95% CI 0.914–0.967) |
First Author | Year | Severity Grading (No. of Eyes) | Definition/Classification Methods | Corneal Imaging Modality | Reported Sensitivity in Detection of Each Severity Level |
---|---|---|---|---|---|
Maeda et al. [12] | 1995 | Mild (15) Moderate (18) Advanced (11) | NA | TMS-1 | Mild: 100% Moderate: 100% Advanced: 91% |
Kamiya et al. [42] | 2019 | Grade 1 (108) Grad e2 (75) Grade 3 (42) Grade 4 (79) | Amsler–Krumeich classification | AS-OCT | Grade 1: 88.9% Grade 2: 68% Grade 3: 71.4% Grad e4: 74.7% |
Issarti et al. [33] | 2019 | Mild KC (220) | a Self-defined | Pentacam | 98.81% |
Issarti et al. [33] | 2019 | Moderate KC (229) | b Self-defined | Pentacam | 99.91% |
Bolarin et al. [35] | 2020 | Grade I (44) Grade II (18) Grade III (15) Grade IV (15) Grade IV plus (15) | RETICS grading | Sirius | Grade I: 59.1% Grade II: 33.3% Grade III: 40% Grade IV: 80% Grade IV plus: 86.7% |
Velazquez-Blazquez et al. [40] | 2020 | Mild KC (42) | RETICS grading | Sirius | Mild KC: 63% |
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Cao, K.; Verspoor, K.; Sahebjada, S.; Baird, P.N. Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis. J. Clin. Med. 2022, 11, 478. https://doi.org/10.3390/jcm11030478
Cao K, Verspoor K, Sahebjada S, Baird PN. Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine. 2022; 11(3):478. https://doi.org/10.3390/jcm11030478
Chicago/Turabian StyleCao, Ke, Karin Verspoor, Srujana Sahebjada, and Paul N. Baird. 2022. "Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis" Journal of Clinical Medicine 11, no. 3: 478. https://doi.org/10.3390/jcm11030478
APA StyleCao, K., Verspoor, K., Sahebjada, S., & Baird, P. N. (2022). Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine, 11(3), 478. https://doi.org/10.3390/jcm11030478