OBJECTIVE: We have used an artificial neural network in an attempt to classify and predict the progression of thyroid-associated ophthalmopathy (TAO) at the first clinical examination. DESIGN: This retrospective comparative case series included a group of patients examined by the ophthalmologist only once because of the absence of signs of progressive disease (GR1), as subsequently monitored by an endocrinologist, and a group of patients on follow-up because of progressive disease (GR2). PARTICIPANTS AND METHODS: We examined 242 patients, of whom 207 were women and 35 were men. GR1 included 129 patients (257 eyes) who, on ophthalmologic assessment, were further classified as having no TAO (n = 53; GR1a) and only lid signs or inactive, stable TAO (n = 76; GR1b). GR2 included 113 patients (219 eyes). One hundred three normal subjects (205 eyes), 50 women and 53 men, were tested to provide normal ranges for proptosis values. We applied a model of back propagation neural network with 17 input variables, a training matrix of 414 observations, a randomly selected test group of 115 observations, and, as output, the progression of disease. 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, and (9) orbital computed tomography scan or ultrasonography. Other parameters included in the neural analysis were gender and age of the patients, their cigarette smoking, and the interval between follow-up visits. RESULTS: The prevalence of smokers among patients without TAO was significantly lower than that among those with TAO (P < 0.03). Mean proptosis values (Hertel) were significantly different in GR1, in GR2, and in a group of normal eyes (P < 0.0001), and the changes of values in consecutive measurements were associated with progression of the disease (P < 0.01). Differences of the proptosis values in the two groups of patients were not related to smoking. The neural network correctly classified 78.3% of 115 eyes (87 patients) and predicted TAO progression in 69.2% of 39 eyes (28 patients). CONCLUSIONS: In our opinion, neural network analysis can be successfully applied for classifying TAO and predicting progression at the first clinical examination.
OBJECTIVE: We have used an artificial neural network in an attempt to classify and predict the progression of thyroid-associated ophthalmopathy (TAO) at the first clinical examination. DESIGN: This retrospective comparative case series included a group of patients examined by the ophthalmologist only once because of the absence of signs of progressive disease (GR1), as subsequently monitored by an endocrinologist, and a group of patients on follow-up because of progressive disease (GR2). PARTICIPANTS AND METHODS: We examined 242 patients, of whom 207 were women and 35 were men. GR1 included 129 patients (257 eyes) who, on ophthalmologic assessment, were further classified as having no TAO (n = 53; GR1a) and only lid signs or inactive, stable TAO (n = 76; GR1b). GR2 included 113 patients (219 eyes). One hundred three normal subjects (205 eyes), 50 women and 53 men, were tested to provide normal ranges for proptosis values. We applied a model of back propagation neural network with 17 input variables, a training matrix of 414 observations, a randomly selected test group of 115 observations, and, as output, the progression of disease. 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, and (9) orbital computed tomography scan or ultrasonography. Other parameters included in the neural analysis were gender and age of the patients, their cigarette smoking, and the interval between follow-up visits. RESULTS: The prevalence of smokers among patients without TAO was significantly lower than that among those with TAO (P < 0.03). Mean proptosis values (Hertel) were significantly different in GR1, in GR2, and in a group of normal eyes (P < 0.0001), and the changes of values in consecutive measurements were associated with progression of the disease (P < 0.01). Differences of the proptosis values in the two groups of patients were not related to smoking. The neural network correctly classified 78.3% of 115 eyes (87 patients) and predicted TAO progression in 69.2% of 39 eyes (28 patients). CONCLUSIONS: In our opinion, neural network analysis can be successfully applied for classifying TAO and predicting progression at the first clinical examination.