Prediction of the Progression of Thyroid-Associated Ophthalmopathy at First Ophthalmologic Examination: Use of a Neural Network

Abstract

In the present work we analyzed patients with thyroid-associated ophthalmopathy (TAO) at various clinical stages of disease progression and implemented a model of neural analysis for disease classification and prediction of progression. We studied 246 patients (group 1), seen only once because they had absent, minimal, or inactive TAO and 152 patients (group 2), seen two or more times because of active and/or progressive TAO. The ophthalmologic assessment included: (1) lid fissure measurement; (2) Hertel; (3) color vision; (4) cover test and Hess screen; (5) visual acuity; (6) tonometry; (7) fundus examination;(8) visual field; (9) orbital computed tomography (CT) scan or ultrasound. A back propagation model of neural network was based on the relative variations of 13 clinical eye signs (input variables) for classification and prediction of disease progression (output variable). Approximately 300 eyes (20%) were randomly selected as a test group. Correlation between expected and calculated patients' classification was highly significant (p < 0.00001). Concordance between clinical assessment and the neural network prediction was obtained in 78 of 117 eyes (67%). We have developed a neural model that allows classification of TAO and preliminary prediction of disease progression at the first clinical examination. The results are validating the classification into the two groups on which our initial assumption was based.