Literature DB >> 31503083

A Practical Guide to Artificial Intelligence-Based Image Analysis in Radiology.

Thomas Weikert1, Joshy Cyriac, Shan Yang, Ivan Nesic, Victor Parmar, Bram Stieltjes.   

Abstract

The use of artificial intelligence (AI) is a powerful tool for image analysis that is increasingly being evaluated by radiology professionals. However, due to the fact that these methods have been developed for the analysis of nonmedical image data and data structure in radiology departments is not "AI ready", implementing AI in radiology is not straightforward. The purpose of this review is to guide the reader through the pipeline of an AI project for automated image analysis in radiology and thereby encourage its implementation in radiology departments. At the same time, this review aims to enable readers to critically appraise articles on AI-based software in radiology.

Mesh：

Year:  2020        PMID： 31503083     DOI： 10.1097/RLI.0000000000000600

Source DB:  PubMed          Journal:  Invest Radiol        ISSN： 0020-9996            Impact factor:   6.016


  10 in total

1.  Logistic Regression-Based Model Is More Efficient Than U-Net Model for Reliable Whole Brain Magnetic Resonance Imaging Segmentation.

Authors:  Henry Dieckhaus; Rozanna Meijboom; Serhat Okar; Tianxia Wu; Prasanna Parvathaneni; Yair Mina; Siddharthan Chandran; Adam D Waldman; Daniel S Reich; Govind Nair
Journal:  Top Magn Reson Imaging       Date:  2022-06-28

Review 2.  High-dimensional role of AI and machine learning in cancer research.

Authors:  Enrico Capobianco
Journal:  Br J Cancer       Date:  2022-01-10       Impact factor: 9.075

3.  Deep Convolutional Neural Network-Based Diagnosis of Anterior Cruciate Ligament Tears: Performance Comparison of Homogenous Versus Heterogeneous Knee MRI Cohorts With Different Pulse Sequence Protocols and 1.5-T and 3-T Magnetic Field Strengths.

Authors:  Christoph Germann; Giuseppe Marbach; Francesco Civardi; Sandro F Fucentese; Jan Fritz; Reto Sutter; Christian W A Pfirrmann; Benjamin Fritz
Journal:  Invest Radiol       Date:  2020-08       Impact factor: 10.065

4.  A Glimpse on Trends and Characteristics of Recent Articles Published in the Korean Journal of Radiology.

Authors:  Yeon Hyeon Choe
Journal:  Korean J Radiol       Date:  2019-12       Impact factor: 3.500

Review 5.  Radiomics, machine learning, and artificial intelligence-what the neuroradiologist needs to know.

Authors:  Matthias W Wagner; Khashayar Namdar; Asthik Biswas; Suranna Monah; Farzad Khalvati; Birgit B Ertl-Wagner
Journal:  Neuroradiology       Date:  2021-09-18       Impact factor: 2.804

6.  [Construction of a Standard Dataset for Liver Tumors for Testing the Performance and Safety of Artificial Intelligence-Based Clinical Decision Support Systems].

Authors:  Seung-Seob Kim; Dong Ho Lee; Min Woo Lee; So Yeon Kim; Jaeseung Shin; Jin-Young Choi; Byoung Wook Choi
Journal:  Taehan Yongsang Uihakhoe Chi       Date:  2021-08-05

7.  Performance of a deep learning tool to detect missed aortic dilatation in a large chest CT cohort.

Authors:  Maurice Pradella; Rita Achermann; Jonathan I Sperl; Rainer Kärgel; Saikiran Rapaka; Joshy Cyriac; Shan Yang; Gregor Sommer; Bram Stieltjes; Jens Bremerich; Philipp Brantner; Alexander W Sauter
Journal:  Front Cardiovasc Med       Date:  2022-08-22

8.  Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning.

Authors:  Lorraine Abel; Jakob Wasserthal; Thomas Weikert; Alexander W Sauter; Ivan Nesic; Marko Obradovic; Shan Yang; Sebastian Manneck; Carl Glessgen; Johanna M Ospel; Bram Stieltjes; Daniel T Boll; Björn Friebe
Journal:  Diagnostics (Basel)       Date:  2021-05-19

9.  Ethical Implications of Alzheimer's Disease Prediction in Asymptomatic Individuals through Artificial Intelligence.

Authors:  Frank Ursin; Cristian Timmermann; Florian Steger
Journal:  Diagnostics (Basel)       Date:  2021-03-04

Review 10.  Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence.

Authors:  Akifumi Hagiwara; Shohei Fujita; Yoshiharu Ohno; Shigeki Aoki
Journal:  Invest Radiol       Date:  2020-09       Impact factor: 10.065
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.