ARTIFICIAL NEURAL NETWORKS - A NEW APPROACH IN NON-INVASIVE MONITORING OF INFLAMMATORY BOWEL DISEASES
Our aim was to use artificial neural networks (ANN) for assessing inflammatory bowel disease (IBD) activity using various biological and historical data. Material and methods: The study group included 100 patients aged 18-80 years, diagnosed with ulcerative colitis (UC) - 59% and Crohn’s Disease (CD) - 41% based on clinical, biological, imaging and histopathological criteria. Both pre-diagnosed and newly diagnosed cases were included. Study database contains multiple parameters obtained through anamnesis, physical exam, evaluation of nutritional status (body mass index), laboratory tests, imaging investigations. We built the neural network using MATLAB 7.0. Results: The accuracy of the mathematical model was measured in terms of mean square error (MSE) and mean absolute percentage error (MAPE). Calculated MAPE values for Ulcerative Colitis Disease Activity Index (UCDAI) score and Rachmilewitz score were 52,34% and 47,15% respectively. None of the scores were estimated with excellent or high precision. The Rachmilewitz score was estimated with average accuracy because the network included currently few registered patients. All other scores were estimated with low precision. Conclusions: The partial results obtained in the first stage of this study are encouraging given the small number of patients enrolled. Thus, for the next phases of the study, with the ongoing development of the database, we expect to achieve results with higher accuracy.
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