ARTIFICIAL NEURAL NETWORKS - A NEW APPROACH IN NON-INVASIVE MONITORING OF INFLAMMATORY BOWEL DISEASES
Keywords:
INFLAMMATORY BOWEL DISEASE, NON-INVASIVE METHODS, ARTIFICIAL NEURAL NETWORKSAbstract
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.
References
2. Cioffi M, Rosa AD, Serao R, Picone I, Vietri MT. Laboratory markers in ulcerative colitis: Current insights and future advances. World J Gastroint Pathophysiol 2015; 6(1): 13-22.
3. Conklin L, Alex P. New serological biomarkers of inflammatory bowel disease. World J Gastroenterol 2008; 14: 5115-5124.
4. Dranga M, Dumitrescu G, Badea M, et al. The semi-quantitative calprotectin rapid test - it is useful in inflammatory bowel disease? Rev Med Chir Soc Med Nat Iasi 2012; 116(3): 761-765.
5. Gavrilescu O, Dranga M, Mihaic C, Cijevschi Prelipcean C. Quality of life in Crohn’s disease patients. Rev Med Chir Soc Med Nat Iasi 2015; 119(2): 340-345.
6. Heard BJ, Rosvold JM, Fritzler M J, El-Gabalawy H, Wiley JP, Krawetz R J. A computational method to differentiate normal individuals, osteoarthritis and rheumatoid arthritis patients using serum biomarkers. J R Soc Interface 2014; 11(97): 1234-1254.
7. Shepherd SF, McGuire ND, De Lacy Costello BPJ et al. The use of a gas chromatograph coupled to a metal oxide sensor for rapid assessment of stool samples from irritable bowel syndrome and inflammatory bowel disease patients. J Breath Res 2014; 8(2): 1034-1054.
8. Peng JC, Ran ZH, Shen J. Seasonal variation in onset and relapse of IBD and a model to predict the frequency of onset, relapse, and severity of IBD based on artificial neural network. Int J Colorectal Dis 2015; 30(9): 1267-1273.
9. Hardalaç F, Basaranoglu M, Yüksel M, Kutbay U. The rate of mucosal healing by azathioprine therapy and prediction by artificial systems. Turk.J Gastroenterol 2015; 26(4): 315-321.
10. Zheng MH, Shi KQ, Lin XF, Xiao DD, Chen LL, Liu WY, Fan YC, Chen YP. A model to predict 3-month mortality risk of acute-on-chronic hepatitis B liver failure using artificial neural network. J Viral Hepat 2013; 20(4): 248-255.
11. Liu X, Li NS, Lv LS, Huang JH, Tang H, Chen JX, Ma HJ, Wu XM, Lou TQ. A comparison of the performances of an artificial neural network and a regression model for GFR estimation. Am J Kidney Dis 2013; 62(6): 1109-1115.
12. Baxt WG (1995) Application of artificial neural networks to clinical medicine. Lancet 1995; 46(8983): 1135-1138.
13. Wang D, Wang Q, Shan F, Liu B, Lu C (2010) Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers. BMC Infect Dis 2010; 5: 12456-1362.
14. Baxt WG. Application of artificial neural networks to clinical medicine. Lancet 1995; 346(8983): 1135-1138.
15. Cross SS, Harrison RF, Kennedy RL. Introduction to neural networks. Lancet 1995; 346(8982): 1075-1079.
16. Traeger M, Eberhart A, Geldner G, Morin AM, Putzke C, Wulf H, Eberhart LH. Artificial neural networks. Theory and applications in anesthesia, intensive care and emergency medicine. Anaesthesist 2003; 52(11): 1055-1061.
17. Wei CH, Lee Y. Sequential forecast of incident duration using artificial neural network models. Accid Anal Prev 2007; 39(5): 944-954.
Additional Files
Published
Issue
Section
License
COPYRIGHT
Once an article is accepted for publication, MSJ requests a transfer of copyrights for published articles.
COPYRIGHT TRANSFER FORM FOR
REVISTA MEDICO-CHIRURGICALĂ A SOCIETĂȚII DE MEDICI ȘI NATURALIȘTI DIN IAȘI /
THE MEDICAL-SURGICAL JOURNAL OF THE SOCIETY OF PHYSICIANS AND NATURALISTS FROM IASI
We, the undersigned authors of the manuscript entitled
_____________________________________________________________________________________
_____________________________________________________________________________________
warrant that this manuscript, which is submitted for publication in the REVISTA MEDICO-CHIRURGICALĂ, has not been published and it is not under consideration for publication in another journal.
- we give the consent for publication in the REVISTA MEDICO-CHIRURGICALĂ, in printed and electronic format and we transfer unconditioned and complete the copyright of this manuscript to the REVISTA MEDICO-CHIRURGICALĂ, in the event of its acceptance.
- the manuscript does not break the intellectual property rights of any other person.
- we have read the submitted version of the manuscript and we are fully responsible for the content.
Names and signatures of authors / copyright owners (the following sequence is the authorship of the article):
- ______________________________/_________________________
- ______________________________/_________________________
- ______________________________/_________________________
N.B. All the authors must sign this form