Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques
Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random fore...
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2021
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| Online Access: | http://hdl.handle.net/10835/12363 https://doi.org/10.3390/jcm10184281 |
| _version_ | 1789408403834011648 |
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| author | Castro De Luna, Gracia María Jiménez Rodríguez, Diana Castaño Fernández, Ana Belén Pérez Rueda, Antonio |
| author_facet | Castro De Luna, Gracia María Jiménez Rodríguez, Diana Castaño Fernández, Ana Belén Pérez Rueda, Antonio |
| author_sort | Castro De Luna, Gracia María |
| collection | DSpace |
| description | Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) < 46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0°, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-12363 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2021 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-123632023-04-12T19:20:53Z Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques Castro De Luna, Gracia María Jiménez Rodríguez, Diana Castaño Fernández, Ana Belén Pérez Rueda, Antonio subclinical keratoconus deep learning corneal topography random forest Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) < 46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0°, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time. 2021-10-04T10:49:07Z 2021-10-04T10:49:07Z 2021-09-21 info:eu-repo/semantics/article 2077-0383 http://hdl.handle.net/10835/12363 https://doi.org/10.3390/jcm10184281 en https://www.mdpi.com/2077-0383/10/18/4281 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | subclinical keratoconus deep learning corneal topography random forest Castro De Luna, Gracia María Jiménez Rodríguez, Diana Castaño Fernández, Ana Belén Pérez Rueda, Antonio Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques |
| title | Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques |
| title_full | Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques |
| title_fullStr | Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques |
| title_full_unstemmed | Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques |
| title_short | Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques |
| title_sort | diagnosis of subclinical keratoconus based on machine learning techniques |
| topic | subclinical keratoconus deep learning corneal topography random forest |
| url | http://hdl.handle.net/10835/12363 https://doi.org/10.3390/jcm10184281 |
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