OBJECTIVE: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar column radiographs in order to aid in the process of diagnosing primary osteoarthritis. METHODS: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographs of the lumbar column, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar column and those with patterns that were difficult to characterize were discarded, thus resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographs for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. RESULTS: After 90 cycles, the validation was carried out on the best results, thereby reaching accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. CONCLUSIONS: Even though the effectiveness shown was moderate, this study is of innovative nature. Hence, the values show that the technique used has a promising future, thus pointing towards further studies covering the image and cycle processing methodology with a larger quantity of radiographs.
Osteoarthritis; Artificial Intelligence; Medical Informatics; Diagnosis, Computer-Assisted
