Introduction
A differential diagnosis of inflammatory bowel diseases (IBD) and irritable bowel syndrome (IBS) is one of the unresolved problems in modern gastroenterology. The absence of clear criteria for IBD detection leads to delay in the diagnostic process for months and years.
Aims & Methods
The aim of the study was to identify clinical and laboratory markers that could distinguish between IBD and IBS with a subsequent development of an artificial neural network (ANN).
We retrospectively evaluated the clinical symptoms, anamnesis data and laboratory tests of patients with verified exacerbation of Crohn`s disease (CD) and ulcerative colitis (UC), and IBS. We found parameters that could distinguish IBD and IBS and then CD and UC, and used them to create the ANN.
Results
Of the 1006 examined, 32% were patients with IBS and 68% with IBD. Patients with IBS were significantly more likely to have a tendency to constipation (34.8% compared with 5.0% in IBD patients), loose stool up to 2 times (26.9% compared with 17.0%), the absence of extraintestinal (EIMs) (94.1% compared to 66.3%) and perianal manifestations (PAM) (96.0% compared to 83.5%) (p<0.001). The patients with IBD had diarrheal syndrome ≥ 5 times a day more frequently (40.0% and 6.2% in patients with IBS, respectively), blood admixture (65.3% and 6.8%, respectively) and higher level of fecal calprotectin (p<0.001). All these data were used to create and train the ANN on general and test (15% of all cases) dataset. Multilayer perceptron showed the best results in IBD detection: the sensitivity was 95.8% and the specificity was 90.2%.
To create the ANN for IBD differentiation, we analyzed medical histories of 350 patients (51%) with UC and 333 (49%) with CD. Statistical analysis in the group with UC significantly more often had severe diarrheal syndrome (≥ 5 times a day) (48.3% compared to 42.3% among the patients with CD), blood in the stool (87.1% compared to 79.0%, respectively), the absence of EIMs (73,4% compared to 58,9%, respectively) and PAMs (97,4% compared to 58,9%, respectively) (p<0,001). Patients with CD compared with UC were significantly more likely to have constipation (7.2% compared to 2.9%, respectively) (p=0.002), increased stool up to 2 times (21,6% compared to 12,6%, respectively), subfebrile temperature (25.8% compared to 16.9%, respectively) (p=0.004) and surgical intervention on the small and large intestines in the anamnesis (20.1% in compared to 1.4%, respectively) (p<0.001). The clinical and anamnestic parameters identified in our study were used to create and train the ANN. The best results were shown by a linear set with 5 input neurons: the accuracy of detecting CD was 78.7% and UC - 75.4%, which shows a moderate ability of the ANN to differentiate UC and CD.
Conclusion
As a result of the study, we created the ANNs with high sensitivity and specificity in the differentiation of organic and functional intestinal diseases and with moderate accuracy in IBD differentiation. The developed ANN can help clinicians in the diagnosis of IBD and IBS, but further work for improving its accuracy and the possibility of implementation into clinical practice is required.
Disclosure
All authors have declared no conflict of interest.