Artificial intelligence in gastroenterology: where are we heading?

Abstract

Artificial intelligence (AI) is coming to medicine in a big wave. From making diagnosis in various medical conditions, following the latest advancements in scientific literature, suggesting appropriate therapies, to predicting prognosis and outcome of diseases and conditions, AI is offering unprecedented possibilities to improve care for patients. Gastroenterology is a field that AI can make a significant impact. This is partly because the diagnosis of gastrointestinal conditions relies a lot on image-based investigations and procedures (endoscopy and radiology). AI-assisted image analysis can make accurate assessment and provide more information than conventional analysis. AI integration of genomic, epigenetic, and metagenomic data may offer new classifications of gastrointestinal cancers and suggest optimal personalized treatments. In managing relapsing and remitting diseases such as inflammatory bowel disease, irritable bowel syndrome, and peptic ulcer bleeding, convoluted neural network may formulate models to predict disease outcome, enhancing treatment efficacy. AI and surgical robots can also assist surgeons in conducting gastrointestinal operations. While the advancement and new opportunities are exciting, the responsibility and liability issues of AI-assisted diagnosis and management need much deliberations.

Keywords

artificial intelligence / endoscopy / robotics / gastrointestinal diseases

References

[1] Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316(22): 2402–2410

[2] Rodríguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Köbrunner SH, Sechopoulos I, Mann RM. Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology 2019; 290(2): 305–314

[3] Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115–118

[4] Mori Y, Kudo SE, Berzin TM, Misawa M, Takeda K. Computer-aided diagnosis for colonoscopy. Endoscopy 2017; 49(8): 813–819

[5] Ciccone MM, Aquilino A, Cortese F, Scicchitano P, Sassara M, Mola E, Rollo R, Caldarola P, Giorgino F, Pomo V, Bux F. Feasibility and effectiveness of a disease and care management model in the primary health care system for patients with heart failure and diabetes (Project Leonardo). Vasc Health Risk Manag 2010; 6: 297–305

[6] Byrne MF, Chapados N, Soudan F, Oertel C, Linares Pérez M, Kelly R, Iqbal N, Chandelier F, Rex DK. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 2019; 68(1): 94–100

[7] Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 2018; 155(4): 1069–1078.e8

[8] Wang P, Xiao X, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, Liu PX, Li LP, Song Y, Zhang D, Li Y, Xu GR, Tu MT, Liu XG. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomized controlled study. Gut 2019; 68: 1813–1819

[9] Misawa M, Kudo SE, Mori Y, Cho T, Kataoka S, Yamauchi A, Ogawa Y, Maeda Y, Takeda K, Ichimasa K, Nakamura H, Yagawa Y, Toyoshima N, Ogata N, Kudo T, Hisayuki T, Hayashi T, Wakamura K, Baba T, Ishida F, Itoh H, Roth H, Oda M, Mori K. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology 2018; 154(8): 2027–2029.e3

[10] Mori Y, Kudo SE, Misawa M, Saito Y, Ikematsu H, Hotta K, Ohtsuka K, Urushibara F, Kataoka S, Ogawa Y, Maeda Y, Takeda K, Nakamura H, Ichimasa K, Kudo T, Hayashi T, Wakamura K, Ishida F, Inoue H, Itoh H, Oda M, Mori K. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Ann Intern Med 2018; 169(6): 357–366

[11] AI-based medical diagnostic services to be launched in Japan.Kyodo news Tokyo/Feb 5 2019

[12] Ikenoyama Y, Hirasawa T, Ishioka M, . Comparing artificial intelligence using deep learning throught convolutional neural networks and endoscopist’s diagnostic ability for detecting early gastric cancer. DDW abstract 2019; 379

[13] Ishioka M, Hirasawa T, Tada T. Detecting gastric cancer from video images using convolutional neural networks. Dig Endosc 2019; 31(2): e34–e35

[14] Iwagami H, Ishihara R, Fukuda H, . Artificial intelligence for the diagnosis of Siewert type I and II esophagogastric junction adenocarcinomas. DDW 2019; Tu 1954

[15] Tokai Y, Yoskio T, Fujisaki J, . Application of artificial intelligence using convolutional neural networks in diagnosing the invasion depth of esophageal squamous cell carcinoma. DDW 2019; 1209

[16] Ichimasa K, Kudo S, Mori Y, . Artificial intelligence with help in determining the need for additional surgery after endoscopic resection of T1 colorectal cancer—analysis based on a big data for machine learning. DDW 2019; 475

[17] Aoki T, Yamada A, Aoyama K, Saito H, Tsuboi A, Nakada A, Niikura R, Fujishiro M, Oka S, Ishihara S, Matsuda T, Tanaka S, Koike K, Tada T. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 2019; 89(2): 357–363.e2

[18] Leenhardt R, Vasseur P, Li C, Saurin JC, Rahmi G, Cholet F, Becq A, Marteau P, Histace A, Dray X; CAD-CAP Database Working Group. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. Gastrointest Endosc 2019; 89(1): 189–194

[19] Kakadiaris IA, Vrigkas M, Yen AA, Kuznetsova T, Budoff M, Naghavi M. Machine learning outperforms ACC/AHA CVD risk calculator in MESA. J Am Heart Assoc 2018; 7(22): e009476

[20] Kwon JM, Kim KH, Jeon KH, Park J. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography 2019; 36(2): 213–218

[21] Bandaria J, Boussios C, Donadio G, . Findings from a non-alcoholic steatohepatitis (NAS) cohort developed via artificial intelligence in a large representative population in the US. DDW 2019; Sa 1631

[22] Shung D, Au B, Taylor R, . Development and validation of machine learning models to predict outcomes in UGIB with comparison to clinical risk score. DDW 2019; 325

[23] Poon NC, Sung JJ. Self-driving cars and AI-assisted endoscopy: who should take the responsibility when things go wrong? J Gastroenterol Hepatol 2019; 34(4): 625–626

[24] Yang GZ, Cambias J, Cleary K, Daimler E, Drake J, Dupont PE, Hata N, Kazanzides P, Martel S, Patel RV, Santos VJ, Taylor RH. Medical robotics—regulatory, ethical, and legal considerations for increasing levels of autonomy. Sci Robot 2017; 2(4): eaam8638

[25] Price WN II. Artificial intelligence in health care: applications and legal implications. SciTech Lawyer 2017; 14(1): 10–13

[26] Lupton M. Some ethical and legal consequences of the application of artificial intelligence in the field of medicine. Trends Med 2018; 18(4): 1–7

Compliance with ethics guidelines

Joseph JY Sung and Nicholas CH Poon declare that they have no conflict of interest. This manuscript is a commentary article and does not involve a research protocol requiring approval of a relevant institutional review board or ethics committee. The article does not contain any studies with human or animal subjects.

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