Application of artificial intelligence for endoscopic image analysis in inflammatory bowel diseases


DOI: https://dx.doi.org/10.18565/therapy.2022.7.7-14

Bakulin I.G., Rasmagina I.A., Skalinskaya M.I., Mashevskiy G.A., Shelyakina N.M.

1) I.I. Mechnikov North-Western State Medical University of the Ministry of Healthcare of Russia, Saint Petersburg; 2) V.I. Ulyanov (Lenin) Saint Petersburg State Electrotechnical University «LETI»
Abstract. Ulcerative colitis (UC) and Crohn’s disease (CD) often lead to the development of complications when the basic therapy is delayed. The delay in disease verification is up to 2 years for CD and up to 10 months for UC. Up to 25% of cases of inflammatory bowel disease (IBD) are diagnosed 2 years after the onset of symptoms. In recent years, methods for detecting IBD based on artificial networks have been created. They increase the accuracy of diagnosis and thereby improve the prognosis of patients.
The aim of the study: to create a method for the diagnosis and differential diagnosis of IBD based on the analysis of the artificial networks of endoscopic images.
Material and methods. The study included patients aged 18 years with colonic CD and UC after excluding intestinal infections, who had endoscopic exacerbation. The comparison group consisted of patients with visually unchanged colon mucosa. Patients underwent videocolonoscopy, during which the obtained digital images were filtered using a non-local mean filter and contrast improved by adaptive contrast-limited histogram equalization. We developed the artificial networks based on the VGG16 network: the first network determined the presence of mucosal changes, the second gave a conclusion about the type of IBD. The input element was digital images, and the output layer of the artificial network gave a conclusion about the presence of pathology and the type of IBD.
Results. The artificial network detected pathology with an accuracy of 89,3% and differentiated IBD with an accuracy of 81,9%. The artificial network detected the norm with an accuracy of 88% and, in comparison with other classes, determined the UC better (90% accuracy), and also most well distinguished the CD class from other classes (92% accuracy). The overall accuracy was 84,6%. On the basis of the developed artificial network, the doctor’s decision support system (DDSS) was created.
Conclusion. The developed models showed moderate accuracy in the detection of IBD and high accuracy in the determination of UC and CD. The created DDSS can reduce the time of IBD verification and can be used as an additional tool in the practice of physicians.
Keywords: artificial intelligence, Crohn’s disease, ulcerative colitis, inflammatory bowel disease
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About the Autors

Igor G. Bakulin, Dr. med. habil., professor, head of the Department of propaedeutics of internal diseases, gastroenterology and dietology named after S.M. Ryss, I.I. Mechnikov North-Western State Medical University of the Ministry of Healthcare of Russia. Address: 195067, Saint Petersburg, 47 Piskarevsky Avenue. E-mail: igbakulin@yandex.ru.
ORCID: https://orcid.org/0000-0002-6151-202
Irina A. Rasmagina, postgraduate student of the 2nd year of study in the specialty «Internal medicine», I.I. Mechnikov North-Western State Medical University of the Ministry of Healthcare of Russia. Address: 195067, Saint Petersburg, 47 Piskarevsky Avenue. E-mail: irenerasmagina@gmail.com. ORCID: https://orcid.org/0000-0003-3525-3289
Maria I. Skalinskaya, PhD in Medicine, associate professor, associate professor of the Department of propaedeutics of internal diseases, gastroenterology and dietology named after S.M. Ryss, I.I. Mechnikov North-Western State Medical University of the Ministry of Healthcare of Russia. Address: 195067, Saint Petersburg, 47 Piskarevsky Avenue. E-mail: mskalinskaya@yahoo.com. ORCID: https://orcid.org/0000-0003-0769-8176
Gleb A. Mashevskiy, PhD in Medicine, associate professor of the Department of biotechnical systems, V.I. Ulyanov (Lenin) Saint Petersburg State Electrotechnical University «LETI». Address: 197022, Saint Petersburg, 5 Professora Popova Str. E-mail: aniket@list.ru. ORCID: https://orcid.org/0000-0001-9380-9543
Natalia M. Shelyakina, systems analyst. E-mail: n.sheliakina@gmail.com


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