Literature DB >> 33065754

Artificial intelligence for the management of pancreatic diseases.

Myrte Gorris1, Sanne A Hoogenboom1, Michael B Wallace2, Jeanin E van Hooft1,3.   

Abstract

Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.
© 2020 The Authors. Digestive Endoscopy published by John Wiley & Sons Australia, Ltd on behalf of Japan Gastroenterological Endoscopy Society.

Entities:  

Keywords:  artificial intelligence; diagnosis, computer-assisted; diagnostic imaging; endoscopy; pancreatic diseases

Mesh：

Year:  2020        PMID： 33065754      PMCID： PMC7898901          DOI： 10.1111/den.13875

Source DB:  PubMed          Journal:  Dig Endosc        ISSN： 0915-5635            Impact factor:   7.559


  66 in total

1.  A new descriptor for computer-aided diagnosis of EUS imaging to distinguish autoimmune pancreatitis from chronic pancreatitis.

Authors:  Jianwei Zhu; Lei Wang; Yining Chu; Xiaojia Hou; Ling Xing; Fanyang Kong; Yinghuo Zhou; Yuanyuan Wang; Zhendong Jin; Zhaoshen Li
Journal:  Gastrointest Endosc       Date:  2015-05-04       Impact factor: 9.427

2.  Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task.

Authors:  Titus J Brinker; Achim Hekler; Alexander H Enk; Joachim Klode; Axel Hauschild; Carola Berking; Bastian Schilling; Sebastian Haferkamp; Dirk Schadendorf; Tim Holland-Letz; Jochen S Utikal; Christof von Kalle
Journal:  Eur J Cancer       Date:  2019-04-10       Impact factor: 9.162

3.  News Feature: What are the limits of deep learning?

Authors:  M Mitchell Waldrop
Journal:  Proc Natl Acad Sci U S A       Date:  2019-01-22       Impact factor: 11.205

Review 4.  Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.

Authors:  Jeremy R Burt; Neslisah Torosdagli; Naji Khosravan; Harish RaviPrakash; Aliasghar Mortazi; Fiona Tissavirasingham; Sarfaraz Hussein; Ulas Bagci
Journal:  Br J Radiol       Date:  2018-04-10       Impact factor: 3.039

5.  Deep Learning to Classify Intraductal Papillary Mucinous Neoplasms Using Magnetic Resonance Imaging.

Authors:  Juan E Corral; Sarfaraz Hussein; Pujan Kandel; Candice W Bolan; Ulas Bagci; Michael B Wallace
Journal:  Pancreas       Date:  2019-07       Impact factor: 3.327

6.  A multimodality test to guide the management of patients with a pancreatic cyst.

Authors:  Simeon Springer; David L Masica; Marco Dal Molin; Christopher Douville; Christopher J Thoburn; Bahman Afsari; Lu Li; Joshua D Cohen; Elizabeth Thompson; Peter J Allen; David S Klimstra; Mark A Schattner; C Max Schmidt; Michele Yip-Schneider; Rachel E Simpson; Carlos Fernandez-Del Castillo; Mari Mino-Kenudson; William Brugge; Randall E Brand; Aatur D Singhi; Aldo Scarpa; Rita Lawlor; Roberto Salvia; Giuseppe Zamboni; Seung-Mo Hong; Dae Wook Hwang; Jin-Young Jang; Wooil Kwon; Niall Swan; Justin Geoghegan; Massimo Falconi; Stefano Crippa; Claudio Doglioni; Jorge Paulino; Richard D Schulick; Barish H Edil; Walter Park; Shinichi Yachida; Susumu Hijioka; Jeanin van Hooft; Jin He; Matthew J Weiss; Richard Burkhart; Martin Makary; Marcia I Canto; Michael G Goggins; Janine Ptak; Lisa Dobbyn; Joy Schaefer; Natalie Sillman; Maria Popoli; Alison P Klein; Cristian Tomasetti; Rachel Karchin; Nickolas Papadopoulos; Kenneth W Kinzler; Bert Vogelstein; Christopher L Wolfgang; Ralph H Hruban; Anne Marie Lennon
Journal:  Sci Transl Med       Date:  2019-07-17       Impact factor: 17.956

7.  Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue.

Authors:  Ananya Das; Cuong C Nguyen; Feng Li; Baoxin Li
Journal:  Gastrointest Endosc       Date:  2008-01-07       Impact factor: 9.427

8.  Neural network analysis of dynamic sequences of EUS elastography used for the differential diagnosis of chronic pancreatitis and pancreatic cancer.

Authors:  Adrian Săftoiu; Peter Vilmann; Florin Gorunescu; Dan Ionuţ Gheonea; Marina Gorunescu; Tudorel Ciurea; Gabriel Lucian Popescu; Alexandru Iordache; Hazem Hassan; Sevastiţa Iordache
Journal:  Gastrointest Endosc       Date:  2008-07-24       Impact factor: 9.427

9.  Preoperative Prediction of Pancreatic Neuroendocrine Neoplasms Grading Based on Enhanced Computed Tomography Imaging: Validation of Deep Learning with a Convolutional Neural Network.

Authors:  Yanji Luo; Xin Chen; Jie Chen; Chenyu Song; Jingxian Shen; Huanhui Xiao; Minhu Chen; Zi-Ping Li; Bingsheng Huang; Shi-Ting Feng
Journal:  Neuroendocrinology       Date:  2019-09-13       Impact factor: 4.914

10.  Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis.

Authors:  Qiu Qiu; Yong-Jian Nian; Yan Guo; Liang Tang; Nan Lu; Liang-Zhi Wen; Bin Wang; Dong-Feng Chen; Kai-Jun Liu
Journal:  BMC Gastroenterol       Date:  2019-07-04       Impact factor: 3.067
View more
  2 in total

Review 1.  Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review.

Authors:  Gaowu Yan; Gaowen Yan; Hongwei Li; Hongwei Liang; Chen Peng; Anup Bhetuwal; Morgan A McClure; Yongmei Li; Guoqing Yang; Yong Li; Linwei Zhao; Xiaoping Fan
Journal:  Front Med (Lausanne)       Date:  2022-06-23

2.  Early prediction of acute necrotizing pancreatitis by artificial intelligence: a prospective cohort-analysis of 2387 cases.

Authors:  Péter Hegyi; Andrea Szentesi; Szabolcs Kiss; József Pintér; Roland Molontay; Marcell Nagy; Nelli Farkas; Zoltán Sipos; Péter Fehérvári; László Pecze; Mária Földi; Áron Vincze; Tamás Takács; László Czakó; Ferenc Izbéki; Adrienn Halász; Eszter Boros; József Hamvas; Márta Varga; Artautas Mickevicius; Nándor Faluhelyi; Orsolya Farkas; Szilárd Váncsa; Rita Nagy; Stefania Bunduc; Péter Jenő Hegyi; Katalin Márta; Katalin Borka; Attila Doros; Nóra Hosszúfalusi; László Zubek; Bálint Erőss; Zsolt Molnár; Andrea Párniczky
Journal:  Sci Rep       Date:  2022-05-12       Impact factor: 4.996
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.