The rate of mucosal healing by azathioprine therapy and prediction by artificial systems

Abstract

Background/Aims: We aimed to assess the effect of azathioprine on mucosal healing in patients with inflammatory bowel diseases (IBD). Artificial neural networks were applied to IBD data for predicting mucosal remission. Materials and Methods: Two thousand seven hundred patients with IBD were evaluated. According to the computer-based study, data of 129 patients with IBD were used. Artificial neural networks were performed and tested. Results: Endoscopic mucosal healing was found in 37% patients with IBD. Male gender group showed a negative impact on the efficacy of azathioprine (p<0.05). Responder patients with IBD were older than the nonresponder (p<0.05) patients. According to this study, the cascade-forward neural network study provides 79.1% correct results. In addition to a 0.16033 training error, mean square error (MSE) was taken at the 16 th epoch from the feed-forward back-propagation neural network. This neural structure, used for predicting mucosal remission with azathioprine, was also validated. Conclusion: Analyzing all parameters within each other to azathioprine therapy were shown that which parameters gave better healing were determined by statistical, and for the most weighted six input parameters, artificial neural network structures were constructed. In this study, feed-forward back-propagation and cascade-forward artificial neural network models were used.

Application of artificial intelligence in gastroenterology

“…Takayama et al[17] established an ANN model for the prediction of prognosis in patients with ulcerative colitis after cytoapheresis therapy and achieved a sensitivity and specificity for the need of an operation of 96% and 97%, respectively. Hardalaç et al[18] established an ANN model to predict mucosal healing by azathioprine therapy in patients with inflammatory bowel disease (IBD) and achieved 79.1% correct classifications. Peng et al[19] used an ANN model to predict the frequency of the onset, relapse, and severity of IBD.…”

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

“…In the validation cohort, the model predicted the onset frequency and the frequency of relapse of the IBD with a mean absolute percentage error of 37.58% and 17.1%, respectively[ 100 ]. A study focusing on CD developed an ANN model to predict mucosal remission for patients treated with azathioprine 16 wk following treatment[ 101 ]. In a study focusing on UC, an ANN was developed employing clinical data to predict the patients with UC treated with cytoapheresis, who will eventually require operation[ 102 ].…”

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“…The model provided the fourth best accuracy when compared to other machine learning techniques used by the researchers. 7) Hardalaçet al used a neural network model to evaluate the impact of azathioprine treatment on mucosal healing (Hardalac et al, 2015). 8) Albarakati and colleagues used an artificial neural network to classify genes as interacting or not interacting with BRCA-1DNA repair gene among patients underwent to the pharmacological treatment with cisplatin for breast cancer (Albarakati et al, 2015).…”