ARTIFICIAL INTELLIGENCE IN GASTROINTESTINAL SURGERY

Artificial_Intelligence_in_Gastrointestinalkapak

ARTIFICIAL INTELLIGENCE (AI) IN GASTROINTESTINAL RADIOLOGY

Abdülkadir Deniz

Van Training and Research Hospital, Department of Gastroenterology Surgery, Van, Türkiye

Deniz A. Artificial Intelligence (AI) in Gastrointestinal Radiology. Çaycı HM, ed. Artificial Intelligence (AI) in Gastrointestinal Surgery. 1st ed. Ankara: Türkiye Klinikleri; 2025. p.187-193.

ABSTRACT

The application of artificial intelligence (AI) in gastrointestinal (GI) radiology represents a transformative leap, enhancing diagnostic accuracy and clinical efficiency. AI-powered imaging systems, such as computed tomography (CT) and magnetic resonance imaging (MRI), reduce radiation exposure while delivering high-quality images. For instance, in suspected pancreatic cancer cases, AI algorithms provide clearer details, improving surgical planning and reliability.

AI significantly contributes to automated disease detection and early diagnosis. It enhances the staging and assessment of cancers such as colorectal, gastric, esophageal, liver, and pancreatic cancers. For example, in rectal cancer, AI-supported systems facilitate tumor localization and metastasis detection, outperforming conventional methods.

The use of radiomic biomarkers is another major advancement introduced by AI. For instance, in liver fibrosis, AI enables non-invasive staging, eliminating the need for biopsies. Additionally, AI-supported decision-making systems prioritize critical cases and optimize treatment plans, improving clinical workflows and patient outcomes.

Future directions include advanced AI models that integrate genetic and clinical data, paving the way for personalized medicine. Collaborative AI systems designed to work alongside radiologists promise to enhance diagnostic reliability, especially in complex cases. Moreover, AI’s scalability offers diagnostic support in underserved areas, bridging healthcare disparities.

AI is more than a technological tool; it symbolizes hope for patients and healthcare providers alike. By enhancing speed, accuracy, and personalized care, AI is reshaping the future of GI radiology and revolutionizing healthcare delivery.

Keywords: Radiomics; Radiology; Artificial intelligence

Referanslar

  1. Chang KP, Lin SH, Chu YW. Artificial intelligence in gastrointestinal radiology: A review with special focus on recent development of magnetic resonance and computed tomography. Artif Intell Gastroenterol 2021;2(2):27-41 [Crossref]
  2. Li P, Song G, Wu R, Li H, Zhang R, Zuo P, et al. Multiparametric MRI-based machine learning models for preoperatively predicting rectal adenoma with canceration. MAGMA. 2021;34:707-16. [Crossref]  [PubMed]
  3. Yin Z, Yao C, Zhang L, Qi S. Application of artificial intelligence in diagnosis and treatment of colorectal cancer: a novel prospect. Frontiers in Medicine, 2023;10:1128084. [Crossref]  [PubMed]  [PMC]
  4. Hamabe A, Ishii M, Kamoda R, Sasuga S, Okuya K, Okita K, et al. Artificial intelligence-based technology for semi-automated segmentation of rectal cancer using high-resolution MRI. PLoS One. 2022;17:e269931. [Crossref]  [PubMed]  [PMC]
  5. 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 Meth Prog Bio. 2022;215:106634. [Crossref]  [PubMed]
  6. Wong C, Fu Y, Li M, Mu S, Chu X, Fu J, et al. MRI-based artificial intelligence in rectal cancer. J Magn Reson Imaging. 2022;57:45-56. [Crossref]  [PubMed]
  7. Koh D. Using deep learning for MRI to identify responders to Chemoradiotherapy in rectal cancer. Radiology. 2020;296:65-6. [Crossref]  [PubMed]
  8. Liu X, Zhang D, Liu Z, Li Z, Xie P, Sun K, et al. Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: a multicentre study. EBioMedicine. 2021;69:103442. [Crossref]  [PubMed]  [PMC]
  9. Ferrari R, Mancini-Terracciano C, Voena C, Rengo M, Zerunian M, Ciardiello A, et al. MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer. Eur J Radiol. 2019;118:1-9. [Crossref]  [PubMed]
  10. Wang R, Dai W, Gong J, Huang M, Hu T, Li H, et al. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol. 2022;15:11. [Crossref]  [PubMed]  [PMC]
  11. Wei J, Cheng J, Gu D, Chai F, Hong N, Wang Y, et al. Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases. Med Phys. 2020;48:513-22. [Crossref]  [PubMed]
  12. Kim DJ, Hyung WJ, Park YK, Lee HJ, An JY, Kim HI, et al. Accuracy of preoperative clinical staging for locally advanced gastric cancer in KLASS-02 randomized clinical trial. Front Surg. 2022;9:1001245. [Crossref]  [PubMed]  [PMC]
  13. Wang ZL, Li YL, Tang L, Li XT, Bu ZD, Sun YS. Utility of the gastric window in computed tomography for differentiation of early gastric cancer (T1 stage) from muscularis involvement (T2 stage) Abdom Radiol (NY) 2021;46:1478-1486. [Crossref]  [PubMed]
  14. Zeng Q, Feng Z, Zhu Y, Zhang Y, Shu X, Wu A, et al. Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images. Front Oncol. 2022;12:1065934. [Crossref]  [PubMed]  [PMC]
  15. Kim KW, Huh J, Urooj B, Lee J, Lee IS, Park H, Na S, Ko Y. Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry. J Gastric Cancer. 2023;23:388-399 [Crossref]  [PubMed]  [PMC]
  16. Bangolo A, Wadhwani N, Nagesh VK, Dey S, Tran HHV, Aguilar IK, et al. Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies. Artif Intell Gastrointest Endosc 2024;5(2):90704 [Crossref]
  17. Mei J, Zhou T, Huang K, Zhang Y, Zhou Y, Wu Y, Fu H. A survey on deep learning for polyp segmentation: techniques, challenges and future trends. Vis. Intell. 2025;3:1. [Crossref]
  18. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71:209-49. [Crossref]  [PubMed]
  19. Chau I, Doki Y, Ajani JA, et al. Nivolumab (NIVO) plus ipilimumab (IPI) or NIVO plus chemotherapy (chemo) versus chemo as first-line (1L) treatment for advanced esophageal squamous cell carcinoma (ESCC): First results of the CheckMate 648 study. J Clin Oncol. 2021;39:LBA4001. [Crossref]
  20. Zhang YH, Guo LJ, Yuan XL, et al. Artificial intelligence-assisted esophageal cancer management: Now and future. World J Gastroenterol 2020;26:5256-71. [Crossref]  [PubMed]  [PMC]
  21. Inoue H, Kaga M, Ikeda H, et al. Magnification endoscopy in esophageal squamous cell carcinoma: a review of the intrapapillary capillary loop classification. Ann Gastroenterol. 2015;28:41-8. [PubMed]
  22. Gora MJ, Suter MJ, Tearney GJ, et al. Endoscopic optical coherence tomography: technologies and clinical applications [Invited]. Biomed Opt Express 2017;8:2405-44. [Crossref]  [PubMed]  [PMC]
  23. Dong D, Fang MJ, Tang L, et al. Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study. Ann Oncol. 2020;31(7):912-920. [Crossref]  [PubMed]
  24. Johnson H, Lee K. Ultrasound Innovations in Hepatic Imaging. Ultrasound Res. 2021;50(1):12-20. [PubMed]
  25. Rawla P, Sunkara T, Gaduputi V. Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. World J Oncol. 2019;10(1):10-27. [Crossref]  [PubMed]  [PMC]
  26. Hu C, Li M. In advanced pancreatic cancer: The value and significance of interventional therapy. J Interv Med. 2020;3(3):118-121. Published 2020 Jul 9. [Crossref]  [PubMed]  [PMC]
  27. Liu SL, Li S, Guo YT, et al. Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network. Chin Med J (Engl). 2019;132(23):2795-2803. [Crossref]  [PubMed]  [PMC]
  28. Kang JD, Clarke SE, Costa AF. Factors associated with missed and misinterpreted cases of pancreatic ductal adenocarcinoma. Eur Radiol. 2021;31(4):2422-2432. [Crossref]  [PubMed]
  29. Kaissis G, Ziegelmayer S, Lohöfer F, et al. A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging. Eur Radiol Exp. 2019;3(1):41. Published 2019 Oct 17. [Crossref]  [PubMed]  [PMC]
  30. Gao X, Wang X. Performance of Deep Learning for Differentiating Pancreatic Diseases on Contrast-Enhanced Magnetic Resonance Imaging: A Preliminary Study. Diagn. Interv. Imaging. 2020;101:91-100. [Crossref]  [PubMed]
  31. Zhang Y, Wang S, Qu S, Zhang H. Support Vector Machine Combined with Magnetic Resonance Imaging for Accurate Diagnosis of Paediatric Pancreatic Cancer. IET Image Process. 2020;14:1233-1239. [Crossref]
  32. Hameed BS, Krishnan UM. Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer. Cancers (Basel). 2022;14(21):5382. Published 2022 Oct 31. [Crossref]  [PubMed]  [PMC]
  33. Dana J, Venkatasamy A, Saviano A, et al. Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease. Hepatol Int. 2022;16(3):509-522. [Crossref]  [PubMed]  [PMC]
  34. Iqbal J, Cortés Jaimes DC, Makineni P, et al. Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine. Cureus. 2023;15(9):e44658. Published 2023 Sep 4. [PMC]
  35. Goyal H, Sherazi SAA, Mann R, et al. Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel). 2021;13(21):5494. Published 2021 Nov 1. [Crossref]  [PubMed]  [PMC]