A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development
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AuthorBolarín Guillén, José Miguel; Cavas Martínez, Francisco; Velázquez Blázquez, José Sebastián; Alió Sanz, Jorge Luciano
Knowledge AreaExpresión Gráfica en Ingeniería
SponsorsThis publication has been carried out as part of the Thematic Network for Co-Operative Research in Health (RETICS), reference number RD16/0008/0012, financed by the Carlos III Health Institute-General Subdirection of Networks and Cooperative Investigation Centers (R&D&I National Plan 2013-2016), European Regional Development Funds (FEDER), and the Results Valorization Program financed by the Technical University of Cartagena (PROVALOR-UPCT).
Bibliographic CitationBolarín, Jose & Cavas, F. & Velázquez, J.S. & Alió, Jorge. (2020). A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development. Applied Sciences. 10. 1874. 10.3390/app10051874.
3D cornea model
Corrected Distance Visual Acuity (CDVA)
This work pursues two objectives: defining a new concept of risk probability associated with su_ering 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. Di_erent 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 ...
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