TY - JOUR 
A1 - Bolarín Guillén, José Miguel
AU - Cavas Martínez, Francisco
AU - Velázquez Blázquez, José Sebastián
AU - Alió Sanz, Jorge Luciano
T1 - A machine-learning model based on morphogeometric parameters for RETICS disease classification and GUI development
Y1 - 2020
SN - 2076-3417
UR - http://hdl.handle.net/10317/9329
AB - 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 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.
KW - Expresión Gráfica en Ingeniería
KW - Scheimpflug
KW - 3D cornea model
KW - Early keratoconus
KW - Corrected Distance Visual Acuity (CDVA)
KW - 3201.09 Oftalmología
KW - 1203.09 Diseño Con Ayuda del Ordenador
LA - eng
PB - MDPI
ER -