Clinical Endoscopy

The Korean Society of Gastrointestinal Endoscopy (대한소화기내시경학회)
권호 표지
DOI QR코드

DOI QR Code

As how artificial intelligence is revolutionizing endoscopy

  • Received : 2023.09.13
  • Accepted : 2023.10.15
  • Published : 2024.05.30
Download PDF
⟨ Previous Next ⟩

Abstract

With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.

Keywords

References

  1. Parasa S, Repici A, Berzin T, et al. Framework and metrics for the clinical use and implementation of artificial intelligence algorithms into endoscopy practice: recommendations from the American Society for Gastrointestinal Endoscopy Artificial Intelligence Task Force. Gastrointest Endosc 2023;97:815-824.
  2. Hassan AR, Haque MA. Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos. Comput Methods Programs Biomed 2015;122:341-353. https://doi.org/10.1016/j.cmpb.2015.09.005
  3. Xiao Jia, Meng MQ. A deep convolutional neural network for bleeding detection in wireless capsule endoscopy images. Annu Int Conf IEEE Eng Med Biol Soc 2016;2016:639-642.
  4. Ding Z, Shi H, Zhang H, et al. Gastroenterologist-level identification of small-bowel diseases and normal variants by capsule endoscopy using a deep-learning model. Gastroenterology 2019;157:1044-1054. https://doi.org/10.1053/j.gastro.2019.06.025
  5. Zhang Y, Zhang Y, Huang X. Development and application of magnetically controlled capsule endoscopy in detecting gastric lesions. Gastroenterol Res Pract 2021;2021:2716559.
  6. Rey JF. Gastric examination by guided capsule endoscopy: a new era. Lancet Gastroenterol Hepatol 2021;6:879-880. https://doi.org/10.1016/S2468-1253(21)00310-1
  7. Gimeno-Garcia AZ, Hernandez Negrin D, Hernandez A, et al. Usefulness of a novel computer-aided detection system for colorectal neoplasia: a randomized controlled trial. Gastrointest Endosc 2023;97:528-536. https://doi.org/10.1016/j.gie.2022.09.029
  8. Rex DK, Berzin TM, Mori Y. Artificial intelligence improves detection at colonoscopy: why aren't we all already using it? Gastroenterology 2022;163:35-37. https://doi.org/10.1053/j.gastro.2022.04.042
  9. Maeda Y, Kudo SE, Ogata N, et al. Use of advanced endoscopic technology for optical characterization of neoplasia in patients with ulcerative colitis: systematic review. Dig Endosc 2022;34:1297-1310. https://doi.org/10.1111/den.14335
  10. Ichimasa K, Kudo SE, Lee JW, et al. Artificial intelligence-assisted treatment strategy for T1 colorectal cancer after endoscopic resection. Gastrointest Endosc 2023;97:1148-1152. https://doi.org/10.1016/j.gie.2023.01.057
  11. Mori Y, Misawa M, Kudo SE. Challenges in artificial intelligence for polyp detection. Dig Endosc 2022;34:870-871. https://doi.org/10.1111/den.14279
  12. Glissen Brown JR, Mansour NM, Wang P, et al. Deep learning computer-aided polyp detection reduces adenoma miss rate: a UnitedStates multi-center randomized tandem colonoscopy study (CADeT-CS Trial). Clin Gastroenterol Hepatol 2022;20:1499-1507. https://doi.org/10.1016/j.cgh.2021.09.009
  13. Wallace MB, Sharma P, Bhandari P, et al. Impact of artificial intelligence on miss rate of colorectal neoplasia. Gastroenterology 2022;163:295-304. https://doi.org/10.1053/j.gastro.2022.03.007
  14. Liu W, Wu Y, Yuan X, et al. Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination. Endoscopy 2022;54:972-979.
  15. Shaukat A, Lichtenstein DR, Somers SC, et al. Computer-aided detection improves adenomas per colonoscopy for screening and surveillance colonoscopy: a randomized trial. Gastroenterology 2022;163:732-741. https://doi.org/10.1053/j.gastro.2022.05.028
  16. Sivananthan A, Nazarian S, Ayaru L, et al. Does computer-aided diagnostic endoscopy improve the detection of commonly missed polyps?: a meta-analysis. Clin Endosc 2022;55:355-364. https://doi.org/10.5946/ce.2021.228
  17. Hossain E, Abdelrahim M, Tanasescu A, et al. Performance of a novel computer-aided diagnosis system in the characterization of colorectal polyps, and its role in meeting Preservation and Incorporation of Valuable Endoscopic Innovations standards set by the American Society of Gastrointestinal Endoscopy. DEN Open 2022;3:e178.
  18. Kwak MS, Cha JM, Jeon JW, et al. Artificial intelligence-based measurement outperforms current methods for colorectal polyp size measurement. Dig Endosc 2022;34:1188-1195. https://doi.org/10.1111/den.14318
  19. Yao L, Zhang L, Liu J, et al. Effect of an artificial intelligence-based quality improvement system on efficacy of a computer-aided detection system in colonoscopy: a four-group parallel study. Endoscopy 2022;54:757-768. https://doi.org/10.1055/a-1706-6174
  20. Mori Y, East JE, Hassan C, et al. Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement. Dig Endosc 2023;35:422-429. https://doi.org/10.1111/den.14531
  21. Rondonotti E, Hassan C, Tamanini G, et al. Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the Artificial intelligence BLI Characterization (ABC) study. Endoscopy 2023;55:14-22. https://doi.org/10.1055/a-1852-0330
  22. Abe S, Oda I. How can endoscopists adapt and collaborate with artificial intelligence for early gastric cancer detection? Dig Endosc 2021;33:98-99. https://doi.org/10.1111/den.13751
  23. Ishioka M, Osawa H, Hirasawa T, et al. Performance of an artificial intelligence-based diagnostic support tool for early gastric cancers: retrospective study. Dig Endosc 2023;35:483-491. https://doi.org/10.1111/den.14455
  24. Wu L, He X, Liu M, et al. Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial. Endoscopy 2021;53:1199-1207. https://doi.org/10.1055/a-1350-5583
  25. Siripoppohn V, Pittayanon R, Tiankanon K, et al. Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach. Clin Endosc 2022;55:390-400. https://doi.org/10.5946/ce.2022.005
  26. Everson MA, Garcia-Peraza-Herrera L, Wang HP, et al. A clinically interpretable convolutional neural network for the real-time prediction of early squamous cell cancer of the esophagus: comparing diagnostic performance with a panel of expert European and Asian endoscopists. Gastrointest Endosc 2021;94:273-281. https://doi.org/10.1016/j.gie.2021.01.043
  27. Yuan XL, Zeng XH, Liu W, et al. Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video). Gastrointest Endosc 2023;97:664-672. https://doi.org/10.1016/j.gie.2022.12.003
  28. Meinikheim M, Messmann H, Ebigbo A. Role of artificial intelligence in diagnosing Barrett's esophagus-related neoplasia. Clin Endosc 2023;56:14-22. https://doi.org/10.5946/ce.2022.247
  29. Abdelrahim M, Saiko M, Maeda N, et al. Development and validation of artificial neural networks model for detection of Barrett's neoplasia: a multicenter pragmatic nonrandomized trial (with video). Gastrointest Endosc 2023;97:422-434. https://doi.org/10.1016/j.gie.2022.10.031
  30. Murino A, Rimondi A. Automated artificial intelligence scoring systems for the endoscopic assessment of ulcerative colitis: how far are we from clinical application? Gastrointest Endosc 2023;97:347-349. https://doi.org/10.1016/j.gie.2022.10.010
  31. Marya NB, Powers PD, Petersen BT, et al. Identification of patients with malignant biliary strictures using a cholangioscopy-based deep learning artificial intelligence (with video). Gastrointest Endosc 2023;97:268-278. https://doi.org/10.1016/j.gie.2022.08.021
  32. Huang L, Xu Y, Chen J, et al. An artificial intelligence difficulty scoring system for stone removal during ERCP: a prospective validation. Endoscopy 2023;55:4-11. https://doi.org/10.1055/a-1850-6717
  33. Kuwahara T, Hara K, Mizuno N, et al. Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses. Endoscopy 2023;55:140-149. https://doi.org/10.1055/a-1873-7920
  34. Houwen BB, Nass KJ, Vleugels JL, et al. Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability. Gastrointest Endosc 2023;97:184-199. https://doi.org/10.1016/j.gie.2022.08.043
  35. Rex DK. Making a resect-and-discard strategy work for diminutive colorectal polyps: let's get real. Endoscopy 2022;54:364-366. https://doi.org/10.1055/a-1616-0659
  36. Yang CB, Kim SH, Lim YJ. Preparation of image databases for artificial intelligence algorithm development in gastrointestinal endoscopy. Clin Endosc 2022;55:594-604. https://doi.org/10.5946/ce.2021.229
  37. Feldman K, Nehme F. Beyond clinical accuracy: considerations for the use of generative artificial intelligence models in gastrointestinal care. Gastroenterology 2023;165:336-338. https://doi.org/10.1053/j.gastro.2023.06.006