|
|
Artificial intelligence in gastroenterology: where are we heading? |
Joseph JY Sung1( ), Nicholas CH Poon2 |
1. Institute of Digestive Disease, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China 2. Big Data Decision Analytic Cancer, The Chinese University of Hong Kong, Hong Kong, China |
|
|
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
|
Corresponding Author(s):
Joseph JY Sung
|
Just Accepted Date: 13 February 2020
Online First Date: 27 May 2020
Issue Date: 26 August 2020
|
|
1 |
V Gulshan, L Peng, M Coram, MC Stumpe, D Wu, A Narayanaswamy, S Venugopalan, K Widner, T Madams, J Cuadros, R Kim, R Raman, PC Nelson, JL Mega, DR Webster. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316(22): 2402–2410
https://doi.org/10.1001/jama.2016.17216
pmid: 27898976
|
2 |
A Rodríguez-Ruiz, E Krupinski, JJ Mordang, K Schilling, SH Heywang-Köbrunner, I Sechopoulos, RM Mann. Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology 2019; 290(2): 305–314
https://doi.org/10.1148/radiol.2018181371
pmid: 30457482
|
3 |
A Esteva, B Kuprel, RA Novoa, J Ko, SM Swetter, HM Blau, S Thrun. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115–118
https://doi.org/10.1038/nature21056
pmid: 28117445
|
4 |
Y Mori, SE Kudo, TM Berzin, M Misawa, K Takeda. Computer-aided diagnosis for colonoscopy. Endoscopy 2017; 49(8): 813–819
https://doi.org/10.1055/s-0043-109430
pmid: 28561195
|
5 |
MM Ciccone, A Aquilino, F Cortese, P Scicchitano, M Sassara, E Mola, R Rollo, P Caldarola, F Giorgino, V Pomo, F Bux. 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
https://doi.org/10.2147/VHRM.S9252
pmid: 20479952
|
6 |
MF Byrne, N Chapados, F Soudan, C Oertel, M Linares Pérez, R Kelly, N Iqbal, F Chandelier, DK Rex. 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
https://doi.org/10.1136/gutjnl-2017-314547
pmid: 29066576
|
7 |
G Urban, P Tripathi, T Alkayali, M Mittal, F Jalali, W Karnes, P Baldi. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 2018; 155(4): 1069–1078.e8
https://doi.org/10.1053/j.gastro.2018.06.037
pmid: 29928897
|
8 |
P Wang, X Xiao, JR Glissen Brown, S Bharadwaj, A Becq, X Xiao, PX Liu, LP Li, Y Song, D Zhang, Y Li, GR Xu, MT Tu, XG Liu. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomized controlled study. Gut 2019; 68: 1813–1819
https://doi.org/10.1136/gutjnl-2018-317500
|
9 |
M Misawa, SE Kudo, Y Mori, T Cho, S Kataoka, A Yamauchi, Y Ogawa, Y Maeda, K Takeda, K Ichimasa, H Nakamura, Y Yagawa, N Toyoshima, N Ogata, T Kudo, T Hisayuki, T Hayashi, K Wakamura, T Baba, F Ishida, H Itoh, H Roth, M Oda, K Mori. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology 2018; 154(8): 2027–2029.e3
https://doi.org/10.1053/j.gastro.2018.04.003
pmid: 29653147
|
10 |
Y Mori, SE Kudo, M Misawa, Y Saito, H Ikematsu, K Hotta, K Ohtsuka, F Urushibara, S Kataoka, Y Ogawa, Y Maeda, K Takeda, H Nakamura, K Ichimasa, T Kudo, T Hayashi, K Wakamura, F Ishida, H Inoue, H Itoh, M Oda, K Mori. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Ann Intern Med 2018; 169(6): 357–366
https://doi.org/10.7326/M18-0249
pmid: 30105375
|
11 |
AI-based medical diagnostic services to be launched in Japan.Kyodo news Tokyo/Feb 5 2019
|
12 |
Y Ikenoyama, T Hirasawa, M, Ishioka et al.. 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 |
M Ishioka, T Hirasawa, T Tada. Detecting gastric cancer from video images using convolutional neural networks. Dig Endosc 2019; 31(2): e34–e35
https://doi.org/10.1111/den.13306
pmid: 30449050
|
14 |
H Iwagami, R Ishihara, H, Fukuda et al.. Artificial intelligence for the diagnosis of Siewert type I and II esophagogastric junction adenocarcinomas. DDW 2019; Tu 1954
|
15 |
Y Tokai, T Yoskio, J Fujisaki, et al.. Application of artificial intelligence using convolutional neural networks in diagnosing the invasion depth of esophageal squamous cell carcinoma. DDW 2019; 1209
|
16 |
K Ichimasa, S Kudo, Y Mori, et al.. 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 |
T Aoki, A Yamada, K Aoyama, H Saito, A Tsuboi, A Nakada, R Niikura, M Fujishiro, S Oka, S Ishihara, T Matsuda, S Tanaka, K Koike, T Tada. 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
https://doi.org/10.1016/j.gie.2018.10.027
pmid: 30670179
|
18 |
R Leenhardt, P Vasseur, C Li, JC Saurin, G Rahmi, F Cholet, A Becq, P Marteau, A Histace, X Dray; 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
https://doi.org/10.1016/j.gie.2018.06.036
pmid: 30017868
|
19 |
IA Kakadiaris, M Vrigkas, AA Yen, T Kuznetsova, M Budoff, M Naghavi. Machine learning outperforms ACC/AHA CVD risk calculator in MESA. J Am Heart Assoc 2018; 7(22): e009476
https://doi.org/10.1161/JAHA.118.009476
pmid: 30571498
|
20 |
JM Kwon, KH Kim, KH Jeon, J Park. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography 2019; 36(2): 213–218
https://doi.org/10.1111/echo.14220
pmid: 30515886
|
21 |
J Bandaria, C Boussios, G, Donadio et al.. 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 |
D Shung, B Au, R Taylor, et al.. Development and validation of machine learning models to predict outcomes in UGIB with comparison to clinical risk score. DDW 2019; 325
|
23 |
NC Poon, JJ Sung. Self-driving cars and AI-assisted endoscopy: who should take the responsibility when things go wrong? J Gastroenterol Hepatol 2019; 34(4): 625–626
https://doi.org/10.1111/jgh.14641
pmid: 30920688
|
24 |
GZ Yang, J Cambias, K Cleary, E Daimler, J Drake, PE Dupont, N Hata, P Kazanzides, S Martel, RV Patel, VJ Santos, RH Taylor. Medical robotics—regulatory, ethical, and legal considerations for increasing levels of autonomy. Sci Robot 2017; 2(4): eaam8638
https://doi.org/10.1126/scirobotics.aam8638
|
25 |
WN Price II. Artificial intelligence in health care: applications and legal implications. SciTech Lawyer 2017; 14(1): 10–13
|
26 |
M Lupton. Some ethical and legal consequences of the application of artificial intelligence in the field of medicine. Trends Med 2018; 18(4): 1–7
https://doi.org/10.15761/TiM.1000147
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|