Artificial Intelligence in Ultrasound Diagnosis of Bowel Diseases: Modern Possibilities
https://doi.org/10.22416/1382-4376-2025-35-6-27-34
Abstract
Aim. Review of current achievements, opportunities and challenges in applying artificial intelligence (AI) for analyzing intestinal ultrasound images.
Key points. Ultrasound examination is a highly informative, safe, and widely accessible method for bowel pathology diagnosis. The integration of AI, particularly deep learning and radiomics methods, aims to overcome the operator-dependence of ultrasound, standardize diagnosis, and enhance its efficiency. This article reviews the development and validation of AI algorithms for key areas: inflammatory bowel diseases, acute appendicitis, bowel intussusception and colorectal cancer. Limitations and concerns that require resolution for the successful integration of AI into clinical practice are also discussed.
Conclusion. The integration of AI into ultrasound diagnosis of bowel diseases has significant potency for improving accuracy, reproducibility, and operational efficiency.
Keywords
About the Authors
D. D. MukhametovaRussian Federation
Dilyara D. Mukhametova — Cand. Sci. (Med.), Associate Professor of the Department of Hospital Therapy
420012, Kazan, Butlerova str., 49
O. E. Akchurina
Russian Federation
Olga E. Tabakchi — Postgraduate of the Department of Hospital Therapy
420012, Kazan, Butlerova str., 49
D. I. Abdulganieva
Russian Federation
Diana I. Abdulganieva — Dr. Sci. (Med.), Professor, Head of the Department of Hospital Therapy
420012, Kazan, Butlerova str., 49
References
1. Report for 2020 by the chief freelance specialist of the Ministry of Health of Russia on radiation and instrumental diagnostics Tyurin I.E. (In Russ.). URL: https://static-0.minzdrav.gov.ru/system/attachments/attaches/000/056/620/original/Отчет_за_2020_год_Тюрин.pdf?1624967722 (accessed: 07.12.2022).
2. Lebedev G.S., Shaderkin I.A., Shaderkina A.I. Digital transformation of ultrasound diagnostics. Russian Journal of Telemedicine and E-Health. 2022;8(3):21–45. (In Russ.). DOI: 10.29188/2712-9217-2022-8-4-21-45
3. Nylund K., Maconi G., Hollerweger A., Ripolles T., Pallotta N., Higginson A., et al. EFSUMB recommendations and guidelines for gastrointestinal ultrasound — part 1: examination techniques and normal findings (Short version). Ultraschall in Med. 2017;38(3):1–15. DOI: 10.1055/s-0042–115853
4. Parasa S., Wallace M., Bagci U., Antonino M., Berzin T., Byrne M., et al. Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit. Gastrointest Endosc. 2020;92(4):938–45.e1. DOI: 10.1016/j.gie.2020.04.044
5. Cannarozzi A.L., Latiano A., Massimino L., Bossa F., Giuliani F., Riva M., et al. Inflammatory bowel disease genomics, transcriptomics, proteomics and metagenomics meet artificial intelligence. United European Gastroenterol J. 2024;12(10):1461–80. DOI: 10.1002/ueg2.12655
6. Christou C.D., Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol. 2021;27(37):6191–223. DOI: 10.3748/wjg.v27.i37.6191
7. Yang Y.J., Bang C.S. Application of artificial intelligence in gastroenterology. World J Gastroenterol. 2019;25(14):1666–83. DOI: 10.3748/wjg.v25.i14.1666
8. Shelygin Yu.A., Ivashkin V.T., Achkasov S.I., Reshetov I.V., Maev I.V., Belousova E.A., et al. Clinical guidelines. Crohn’s disease (К50), adults. Koloproktologia. 2023;22(3):10–49. (In Russ.). DOI: 10.33878/2073-7556-2023-22-3-10-49
9. Shelygin Yu.A., Ivashkin V.T., Belousova E.A., Reshetov I.V., Maev I.V., Achkasov S.I., et al. Ulcerative colitis (K51), adults. Koloproktologia. 2023;22(1):10–44. (In Russ.). DOI: 10.33878/2073-7556-2023-22-1-10-44
10. Sturm A., Maaser C., Calabrese E., Annese V., Fiorino G., Kucharzik T., at al. ECCO-ESGAR Guideline for Diagnostic Assessment in IBD Part 2: IBD scores and general principles and technical aspects. Journal of Crohn’s & colitis. 2019;13(3):273–84. DOI: 10.1093/ecco-jcc/jjy114
11. Horsthuis K., Bipat S., Bennink R. J., Stoker J. Inflammatory bowel disease diagnosed with US, MR, scintigraphy, and CT: Meta-analysis of prospective studies. Radiology. 2008;247(1):64–79. DOI: 10.1148/radiol.2471070611
12. Panes J., Bouhnik Y., Reinisch W., Stoker J., Taylor S. A., Baumgart D. C., et al. Imaging techniques for assessment of inflammatory bowel disease: Joint ECCO and ESGAR evidence-based consensus guidelines. J Crohns Colitis. 2013;7:556–85. DOI: 10.1016/j.crohns.2013.02.020
13. Sasaki T., Kunisaki R., Kinoshita H., Yamamoto H., Kimura H., Hanzawa A., et al. Use of color Doppler ultrasonography for evaluating vascularity of small intestinal lesions in Crohn’s disease: Correlation with endoscopic and surgical macroscopic findings. Scand J Gastroenterol. 2014;49:295–301. DOI: 10.3109/00365521.2013.871744
14. Maconi G., Nylund K., Ripolles T., Calabrese E., Dirks K., Dietrich C. F., et al. EFSUMB recommendations and clinical guidelines for intestinal ultrasound (GIUS) in inflammatory bowel diseases. Ultraschall Med. 2018;39(3):304–17. DOI: 10.1055/s-0043-125329
15. Carter D., Albshesh A., Shimon C., Segal B., Yershov A., Kopylov U., et al. Automatized detection of crohn's disease in intestinal ultrasound using convolutional neural network. Inflamm Bowel Dis. 2023;29(12):1901–6. DOI: 10.1093/ibd/izad014
16. Tagliamonte G., Santagata F., Fraquelli M. Current developments and role of intestinal ultrasound including the advent of AI. Diagnostics. 2024;14(7):759. DOI: 10.3390/diagnostics14070759
17. Hameed M., Taylor S.A. Small bowel imaging in inflammatory bowel disease: Updates for 2023. Expert Review of Gastroenterology & Hepatology. 2023;17(11):1117–34. DOI: 10.1080/17474124.2023.2274926
18. Lin S., Lin X., Li X., Chen M., Mao R. Making qualitative intestinal stricture quantitative: Embracing radiomics in IBD. Inflamm Bowel Dis. 2020;26(5):743–5. DOI: 10.1093/ibd/izz197
19. Xiao M.J., Pan Y.T., Tan J.H., Li H.O., Wang H.Y. Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease. World J Gastroenterol. 2024;30(25):3155–65. DOI: 10.3748/wjg.v30.i25.3155
20. Gu P., Chang J.H., Carter D., McGovern D.P.B., Moore J., Wang P., et al. Radiomics-based Analysis of intestinal ultrasound images for inflammatory bowel disease: a feasibility study. Crohns Colitis 360. 2024;6(2):otae034. DOI: 10.1093/crocol/otae034
21. Dirks K., Calabrese E., Dietrich C.F., Gilja O.H., Hausken T., Higginson A., et al. EFSUMB Position Paper: Recommendations for gastrointestinal ultrasound (GIUS) in Acute appendicitis and diverticulitis. Ultraschall Med. 2019;40(2):163–75. DOI: 10.1055/a-0824-6952
22. Cai L., Pfob A. Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications. Abdom Radiol. 2025;50(4):1775–89. DOI: 10.1007/s00261-024-04640-x
23. Marcinkevičs R., Reis Wolfertstetter P., Klimiene U., Chin-Cheong K., Paschke A., Zerres J., et al. Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis. Med Image Anal. 2024;91:103042. DOI: 10.1016/j.media.2023.103042
24. Stiel C., Elrod J., Klinke M., Herrmann J., Junge C.M., et al. The modified Heidelberg and the AI appendicitis score are superior to current scores in predicting appendicitis in children: A two-center cohort study. Front Pediatr. 2020;8:592892. DOI: 10.3389/fped.2020.592892
25. Ghareeb W.M., Emile S.H., Elshobaky A. Artificial intelligence compared to alvarado scoring system alone or combined with ultrasound criteria in the diagnosis of acute appendicitis. J Gastrointest Surg. 2022;26:655–8. DOI: 10.1007/s11605-021-05147-2
26. Hryhorczuk A.L., Strouse P.J. Validation of US as a firstline diagnostic test for assessment of pediatric ileocolic intussusception. Pediatr Radiol. 2009;39(10):1075–9. DOI: 10.1007/s00247-009-1353-z
27. Chen X., You G., Chen Q., Zhang X., Wang N., He X., et al. Development and evaluation of an artificial intelligence system for children intussusception diagnosis using ultrasound images. iScience. 2023;26(4):106456. DOI: 10.1016/j.isci.2023.106456
28. Pei Y., Wang G., Cao H., Jiang S., Wang D., Wang H., et al. A deep-learning pipeline to diagnose pediatric intussusception and assess severity during ultrasound scanning: A multicenter retrospective-prospective study. NPJ Digit Med. 2023;6(1):182. DOI: 10.1038/s41746-023-00930-8
29. Sawicki T., Ruszkowska M., Danielewicz A., Nied- źwiedzka E., Arłukowicz T., Przybyłowicz K.E. A review of colorectal cancer in terms of epidemiology, risk factors, development, symptoms and diagnosis. Cancers (Basel). 2021;13(9):2025. DOI: 10.3390/cancers13092025
30. Bor R., Fábián A., Szepes Z. Role of ultrasound in colorectal diseases. World J Gastroenterol. 2016;22(43):9477–87. DOI: 10.3748/wjg.v22.i43.9477
31. Ueno H., Nagtegaal I.D., Quirke P., Sugihara K., Ajioka Y. Tumor deposits in colorectal cancer: Refining their definition in the TNM system. Ann Gastroenterol Surg. 2023;7(2):225–35. DOI: 10.1002/ags3.12652
32. Khan H., Radomski S.N., Siddiqi A., Zhou N., Paneitz D.C., Johnston F.M. Tumor deposits are associated with a higher risk of peritoneal disease in non-metastatic colorectal cancer patients. J Surg Oncol. 2023;127(6):975– 82. DOI: 10.1002/jso.27207
33. Chen L.D., Li W., Xian M.F., Zheng X., Lin Y., Liu B.X., et al. Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model. Eur Radiol. 2020;30(4):1969–79. DOI: 10.1007/s00330-019-06558-1
34. Song D., Zhang Z., Li W., Yuan L., Zhang W. Judgment of benign and early malignant colorectal tumors from ultrasound images with deep multi-View fusion. Comput Methods Programs Biomed. 2022;215:106634. DOI: 10.1016/j.cmpb.2022.106634
35. Kim D.W., Jang H.Y., Kim K.W., Shin Y., Park S.H. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol. 2019;20(3):405–10. DOI: 10.3348/kjr.2019.0025
36. Mervak B.M., Fried J.G., Wasnik A.P. A review of the clinical applications of artificial intelligence in abdominal imaging. diagnostics (Basel). 2023;13(18):2889. DOI: 10.3390/diagnostics13182889
37. Coppola F., Faggioni L., Gabelloni M., De Vietro F., Mendola V., Cattabriga A., et al. Human, all too human? an all-around appraisal of the “artificial intelligence revolution” in medical imaging. Front Psychol. 2021;12:710982. DOI: 10.3389/fpsyg.2021.710982
38. Akkus Z., Cai J., Boonrod A., Zeinoddini A., Weston A.D., Philbrick K.A., et al. A survey of deep-learning applications in ultrasound: artificial intelligence – powered ultrasound for improving clinical workflow. J Am Coll Radiol. 2019;16(B):1318–28. DOI: 10.1016/j.jacr.2019.06.004
Review
For citations:
Mukhametova D.D., Akchurina O.E., Abdulganieva D.I. Artificial Intelligence in Ultrasound Diagnosis of Bowel Diseases: Modern Possibilities. Russian Journal of Gastroenterology, Hepatology, Coloproctology. 2025;35(6):27-34. https://doi.org/10.22416/1382-4376-2025-35-6-27-34
JATS XML




























