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Article

A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development

1
Technology Centre for IT and Communications (CENTIC), Scientific Park of Murcia, 30100 Murcia, Spain
2
Department of Structures, Construction and Graphical Expression, Technical University of Cartagena, 30202 Cartagena, Spain
3
Keratoconus Unit of Vissum Corporation Alicante, 03016 Alicante, Spain
4
Department of Ophthalmology, Miguel Hernández University of Elche, 03202 Alicante, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(5), 1874; https://doi.org/10.3390/app10051874
Received: 19 December 2019 / Revised: 19 February 2020 / Accepted: 2 March 2020 / Published: 9 March 2020
This work pursues two objectives: defining a new concept of risk probability associated with suffering early-stage keratoconus, classifying disease severity according to the RETICS (Thematic Network for Co-Operative Research in Health) scale. It recruited 169 individuals, 62 healthy and 107 keratoconus diseased, grouped according to the RETICS classification: 44 grade I; 18 grade II; 15 grade III; 15 grade IV; 15 grade V. Different demographic, optical, pachymetric and eometrical parameters were measured. The collected data were used for training two machine-learning models: a multivariate logistic regression model for early keratoconus detection and an ordinal logistic regression model for RETICS grade assessments. The early keratoconus detection model showed very good sensitivity, specificity and area under ROC curve, with around 95% for training and 85% for validation. The variables that made the most significant contributions were gender, coma-like, central thickness, high-order aberrations and temporal thickness. The RETICS grade assessment also showed high-performance figures, albeit lower, with a global accuracy of 0.698 and a 95% confidence interval of 0.623–0.766. The most significant variables were CDVA, central thickness and temporal thickness. The developed web application allows the fast, objective and quantitative assessment of keratoconus in early diagnosis and RETICS grading terms. View Full-Text
Keywords: Scheimpflug; 3D cornea model; early keratoconus; Corrected Distance Visual Acuity (CDVA) Scheimpflug; 3D cornea model; early keratoconus; Corrected Distance Visual Acuity (CDVA)
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MDPI and ACS Style

Bolarín, J.M.; Cavas, F.; Velázquez, J.S.; Alió, J.L. A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development. Appl. Sci. 2020, 10, 1874. https://doi.org/10.3390/app10051874

AMA Style

Bolarín JM, Cavas F, Velázquez JS, Alió JL. A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development. Applied Sciences. 2020; 10(5):1874. https://doi.org/10.3390/app10051874

Chicago/Turabian Style

Bolarín, José M., F. Cavas, J.S. Velázquez, and J.L. Alió 2020. "A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development" Applied Sciences 10, no. 5: 1874. https://doi.org/10.3390/app10051874

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