Clin Endosc.  2020 Jul;53(4):387-394. 10.5946/ce.2020.133.

The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

• Yang YJ

• KMID: 2504941
• DOI: http://doi.org/10.5946/ce.2020.133

Abstract

Capsule endoscopy has revolutionized the management of small-bowel diseases owing to its convenience and noninvasiveness. Capsule endoscopy is a common method for the evaluation of obscure gastrointestinal bleeding, Crohn’s disease, small-bowel tumors, and polyposis syndrome. However, the laborious reading process, oversight of small-bowel lesions, and lack of locomotion are major obstacles to expanding its application. Along with recent advances in artificial intelligence, several studies have reported the promising performance of convolutional neural network systems for the diagnosis of various small-bowel lesions including erosion/ulcers, angioectasias, polyps, and bleeding lesions, which have reduced the time needed for capsule endoscopy interpretation. Furthermore, colon capsule endoscopy and capsule endoscopy locomotion driven by magnetic force have been investigated for clinical application, and various capsule endoscopy prototypes for active locomotion, biopsy, or therapeutic approaches have been introduced. In this review, we will discuss the recent advancements in artificial intelligence in the field of capsule endoscopy, as well as studies on other technological improvements in capsule endoscopy.

Keyword

Artificial intelligence; Capsule endoscopy; Convolutional neural network; Locomotion

Figure

• 

  Fig. 1. Magnetically guided (assisted) capsule endoscope. (A) MiroCam NAVI system (Intromedic Co., Ltd., Seoul, Korea). (B, C) NaviCam capsule endoscope and NaviCam magnetic control system (Ankon Technologies Co., Ltd., Wuhan, China).

Reference

1. Iddan G, Meron G, Glukhovsky A, Swain P. Wireless capsule endoscopy. Nature. 2000; 405:417.

2. Kopylov U, Seidman EG. Diagnostic modalities for the evaluation of small bowel disorders. Curr Opin Gastroenterol. 2015; 31:111–117.

3. Eliakim R. Video capsule endoscopy of the small bowel. Curr Opin Gastroenterol. 2013; 29:133–139.

4. Pennazio M, Spada C, Eliakim R, et al. Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) clinical guideline. Endoscopy. 2015; 47:352–376.

5. ASGE Technology Committee, Wang A, Banerjee S, et al. Wireless capsule endoscopy. Gastrointest Endosc. 2013; 78:805–815.

6. Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol. 2019; 25:1666–1683.

7. Horie Y, Yoshio T, Aoyama K, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc. 2019; 89:25–32.

8. Cho BJ, Bang CS, Park SW, et al. Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network. Endoscopy. 2019; 51:1121–1129.

9. Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019; 68:1813–1819.

10. McAlindon ME, Ching HL, Yung D, Sidhu R, Koulaouzidis A. Capsule endoscopy of the small bowel. Ann Transl Med. 2016; 4:369.

11. Le Berre C, Sandborn WJ, Aridhi S, et al. Application of artificial Intelligence to gastroenterology and hepatology. Gastroenterology. 2020; 158:76–94.e2.

12. Liu G, Yan G, Kuang S, Wang Y. Detection of small bowel tumor based on multi-scale curvelet analysis and fractal technology in capsule endoscopy. Comput Biol Med. 2016; 70:131–138.

13. Li B, Meng MQ. Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection. IEEE Trans Inf Technol Biomed. 2012; 16:323–329.

14. Eid A, Charisis VS, Hadjileontiadis LJ, Sergiadis GD. A curvelet-based lacunarity approach for ulcer detection from wireless capsule endoscopy images. In : Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems; 2013 Jun 20-22; Porto, Portugal. Piscataway (NJ): IEEE;2013. 273–278.

15. Yuan Y, Wang J, Li B, Meng MQ. Saliency based ulcer detection for wireless capsule endoscopy diagnosis. IEEE Trans Med Imaging. 2015; 34:2046–2057.

16. Tenório JM, Hummel AD, Cohrs FM, Sdepanian VL, Pisa IT, de Fátima Marin H. Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease. Int J Med Inform. 2011; 80:793–802.

17. Li B, Meng MQ. Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments. Comput Biol Med. 2009; 39:141–147.

18. Li B, Meng MQ, Xu L. A comparative study of shape features for polyp detection in wireless capsule endoscopy images. Conf Proc IEEE Eng Med Biol Soc. 2009; 2009:3731–3734.

19. Sainju S, Bui FM, Wahid KA. Automated bleeding detection in capsule endoscopy videos using statistical features and region growing. J Med Syst. 2014; 38:25.

20. Zhou T, Han G, Li BN, et al. Quantitative analysis of patients with celiac disease by video capsule endoscopy: a deep learning method. Comput Biol Med. 2017; 85:1–6.

21. Wu X, Chen H, Gan T, Chen J, Ngo CW, Peng Q. Automatic hookworm detection in wireless capsule endoscopy images. IEEE Trans Med Imaging. 2016; 35:1741–1752.

22. Noya F, Alvarez-Gonzalez MA, Benitez R. Automated angiodysplasia detection from wireless capsule endoscopy. Conf Proc IEEE Eng Med Biol Soc. 2017; 2017:3158–3161.

23. Hassan AR, Haque MA. Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos. Comput Methods Programs Biomed. 2015; 122:341–353.

24. Chao WL, Manickavasagan H, Krishna SG. Application of artificial intelligence in the detection and differentiation of colon polyps: a technical review for physicians. Diagnostics (Basel). 2019; 9:99.

25. Aoki T, Yamada A, Aoyama K, et al. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc. 2019; 89:357–363.e2.

26. Aoki T, Yamada A, Aoyama K, et al. Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading. Dig Endosc. 2020; 32:585–591.

27. Klang E, Barash Y, Margalit RY, et al. Deep learning algorithms for automated detection of Crohn’s disease ulcers by video capsule endoscopy. Gastrointest Endosc. 2020; 91:606–613.e2.

28. Leenhardt R, Vasseur P, Li C, et al. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. Gastrointest Endosc. 2019; 89:189–194.

29. Tsuboi A, Oka S, Aoyama K, et al. Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images. Dig Endosc. 2020; 32:382–390.

30. Aoki T, Yamada A, Kato Y, et al. Automatic detection of blood content in capsule endoscopy images based on a deep convolutional neural network. J Gastroenterol Hepatol. 2020; 35:1196–1200.

31. Saito H, Aoki T, Aoyama K, et al. Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc. 2020; 92:144–151.e1.

32. Iakovidis DK, Koulaouzidis A. Automatic lesion detection in capsule endoscopy based on color saliency: closer to an essential adjunct for reviewing software. Gastrointest Endosc. 2014; 80:877–883.

33. 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.e5.

34. Soffer S, Klang E, Shimon O, et al. Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis. Gastrointest Endosc. 2020 Apr 22 [Epub]. https://10.1016/j.gie.2020.04.039.

35. Spada C, Pasha SF, Gross SA, et al. Accuracy of first- and second-generation colon capsules in endoscopic detection of colorectal polyps: a systematic review and meta-analysis. Clin Gastroenterol Hepatol. 2016; 14:1533–1543.e8.

36. Hall B, Holleran G, McNamara D. PillCam COLON 2© as a pan-enteroscopic test in Crohn’s disease. World J Gastrointest Endosc. 2015; 7:1230–1232.

37. Ye CA, Gao YJ, Ge ZZ, et al. PillCam colon capsule endoscopy versus conventional colonoscopy for the detection of severity and extent of ulcerative colitis. J Dig Dis. 2013; 14:117–124.

38. Li Z, Chiu PW. Robotic endoscopy. Visc Med. 2018; 34:45–51.

39. Shamsudhin N, Zverev VI, Keller H, et al. Magnetically guided capsule endoscopy. Med Phys. 2017; 44:e91–e111.

40. Zhao AJ, Qian YY, Sun H, et al. Screening for gastric cancer with magnetically controlled capsule gastroscopy in asymptomatic individuals. Gastrointest Endosc. 2018; 88:466–474.e1.

41. Liao Z, Hou X, Lin-Hu EQ, et al. Accuracy of magnetically controlled capsule endoscopy, compared with conventional gastroscopy, in detection of gastric diseases. Clin Gastroenterol Hepatol. 2016; 14:1266–1273.e1.

42. Ching HL, Hale MF, Kurien M, et al. Diagnostic yield of magnetically assisted capsule endoscopy versus gastroscopy in recurrent and refractory iron deficiency anemia. Endoscopy. 2019; 51:409–418.

43. Beg S, Card T, Warburton S, et al. Diagnosis of Barrett’s esophagus and esophageal varices using a magnetically assisted capsule endoscopy system. Gastrointest Endosc. 2020; 91:773–781.e1.

44. Fontana R, Mulana F, Cavallotti C, et al. An innovative wireless endoscopic capsule with spherical shape. IEEE Trans Biomed Circuits Syst. 2017; 11:143–152.

45. Fu Q, Guo S, Guo J. Conceptual design of a novel magnetically actuated hybrid microrobot. In : 2017 IEEE International Conference on Mechatronics and Automation (ICMA); 2017 Aug 6-9; Takamatsu, Japan. Piscataway (NJ): IEEE;2017. 1001–1005.

46. Guo J, Liu P, Guo S, Wang L, Sun G. Development of a novel wireless spiral capsule robot with modular structure. In : 2017 IEEE International Conference on Mechatronics and Automation (ICMA); 2017 Aug 6-9; Takamatsu, Japan. Piscataway (NJ): IEEE;2017. 439–444.

47. Jang J, Lee J, Lee K, et al. 4-Camera VGA-resolution capsule endoscope with 80Mb/s body-channel communication transceiver and Sub-cm range capsule localization. In : 2018 IEEE International Solid - State Circuits Conference - (ISSCC); 2018 Feb 11-15; San Francisco (CA), USA. Piscataway (NJ): IEEE;2018. 282–284.

48. Demosthenous P, Pitris C, Georgiou J. Infrared fluorescence-based cancer screening capsule for the small intestine. IEEE Trans Biomed Circuits Syst. 2016; 10:467–476.

49. Yim S, Gultepe E, Gracias DH, Sitti M. Biopsy using a magnetic capsule endoscope carrying, releasing, and retrieving untethered microgrippers. IEEE Trans Biomed Eng. 2014; 61:513–521.

50. Chen W-W, Yan G-Z, Liu H, Jiang P-P, Wang Z-W. Design of micro biopsy device for wireless autonomous endoscope. International Journal of Precision Engineering and Manufacturing. 2014; 15:2317–2325.

51. Son D, Dogan MD, Sitti M. Magnetically actuated soft capsule endoscope for fine-needle aspiration biopsy. In : 2017 IEEE International Conference on Robotics and Automation (ICRA); 2017 May 29-Jun 3; Singapore. Piscataway (NJ): IEEE;2017. 1132–1139.

52. Stewart FR, Newton IP, Näthke I, Huang Z, Cox BF, Cochran S. Development of a therapeutic capsule endoscope for treatment in the gastrointestinal tract: bench testing to translational trial. In : 2017 IEEE International Ultrasonics Symposium (IUS); 2017 Sep 6-9; Washington, D.C., USA. Piscataway (NJ): IEEE;2017. 1–4.

53. Leung BHK, Poon CCY, Zhang R, et al. A therapeutic wireless capsule for treatment of gastrointestinal haemorrhage by balloon tamponade effect. IEEE Trans Biomed Eng. 2017; 64:1106–1114.