Application of artificial intelligence in pancreaticobiliary diseases

被引:21
作者
Goyal, Hemant [1 ]
Mann, Rupinder [2 ]
Gandhi, Zainab [3 ]
Perisetti, Abhilash [4 ]
Zhang, Zhongheng [5 ]
Sharma, Neil [6 ,7 ]
Saligram, Shreyas [8 ]
Inamdar, Sumant [9 ]
Tharian, Benjamin [9 ]
机构
[1] Wright Ctr Grad Med Educ, 501 S Washington Ave, Scranton, PA 18505 USA
[2] St Agnes Med Ctr, Fresno, CA USA
[3] Geisinger Community Med Ctr, Dept Med, Scranton, PA USA
[4] Univ Arkansas Med Sci, Dept Gastroenterol & Hepatol, Little Rock, AR 72205 USA
[5] Zhejiang Univ, Sch Med, Sir Run Run Shaw Hosp, Dept Emergency Med, Hangzhou, Peoples R China
[6] Parkview Canc Inst, Div Intervent Oncol & Surg Endoscopy IOSE, Ft Wayne, IN USA
[7] Indiana Univ Sch Med, Ft Wayne, IN USA
[8] Univ Texas Hlth, Div Adv Endoscopy Gastroenterol Hepatol & Nutr, Dept Med, San Antonio, TX USA
[9] Univ Arkansas Med Sci, Little Rock, AR 72205 USA
关键词
artificial intelligence; choledocholithiasis; computer-aided diagnosis; endoscopic ultrasound; pancreatic cancer; ENDOSCOPIC ULTRASOUND ELASTOGRAPHY; CHRONIC-PANCREATITIS; CYST FLUID; DIFFERENTIAL-DIAGNOSIS; EUS; BENIGN; SYSTEM; CANCER; ERCP; TOOL;
D O I
10.1177/2631774521993059
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other artificial intelligence techniques like radiomics-based computer-aided diagnosis systems could help to differentiate between various types of cystic pancreatic lesions. Artificial intelligence and computer-aided systems also showing promising results in the diagnosis of cholangiocarcinoma and the prediction of choledocholithiasis. In this review, we discuss the role of artificial intelligence in establishing diagnosis, prognosis, predicting response to treatment, and guiding therapeutics in the pancreaticobiliary system.
引用
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页数:12
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