Literature DB >> 8130512

A decision aid for diagnosis of liver lesions on MRI.

R Tombropoulos1, S Shiffman, C Davidson.   

Abstract

Abdominal magnetic resonance imaging (MRI) plays an important role in the evaluation of liver abnormalities. The interpretation of MR images requires expert training in a rapidly changing field. DAFODILL (Decision Aid for Diagnosing Liver Lesions) is a decision-support tool designed to aid radiologists in the diagnosis of hepatic lesions seen on MRI. DAFODILL uses a knowledge base of MRI findings and a belief-network inference engine to generate probabilistic differential diagnoses of the most commonly encountered hepatic lesions. DAFODILL performs limited image processing to identify clinically relevant features, which are presented to the user for confirmation before they are used by the network. Preliminary evaluation of an initial version of the system suggests that DAFODILL may be a useful tool for radiology residents and nonexpert radiologists in interpreting MR images of the liver.

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Mesh:

Year:  1993        PMID: 8130512      PMCID: PMC2248547     

Source DB:  PubMed          Journal:  Proc Annu Symp Comput Appl Med Care        ISSN: 0195-4210


  9 in total

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Authors:  T J O'Leary; U V Mikel; R L Becker
Journal:  Mod Pathol       Date:  1992-07       Impact factor: 7.842

2.  An evaluation of the diagnostic accuracy of Pathfinder.

Authors:  D E Heckerman; B N Nathwani
Journal:  Comput Biomed Res       Date:  1992-02

Review 3.  Magnetic resonance imaging of diseases of the liver and biliary system.

Authors:  G K Kanzer; J C Weinreb
Journal:  Radiol Clin North Am       Date:  1991-11       Impact factor: 2.303

Review 4.  Integrated expert systems and videodisc in surgical pathology: an overview.

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Journal:  Hum Pathol       Date:  1990-01       Impact factor: 3.466

5.  Application of an artificial neural network in radiographic diagnosis.

Authors:  D W Piraino; S C Amartur; B J Richmond; J P Schils; J M Thome; G H Belhobek; M D Schlucter
Journal:  J Digit Imaging       Date:  1991-11       Impact factor: 4.056

Review 6.  Medical images and automated interpretation.

Authors:  J A Newell
Journal:  J Biomed Eng       Date:  1988-11

Review 7.  Magnetic resonance imaging of the liver.

Authors:  R Weissleder; D D Stark
Journal:  Magn Reson Q       Date:  1989-04

Review 8.  MR imaging of focal liver masses.

Authors:  D D Stark
Journal:  Radiology       Date:  1988-08       Impact factor: 11.105

Review 9.  Complications of percutaneous abdominal fine-needle biopsy. Review.

Authors:  E H Smith
Journal:  Radiology       Date:  1991-01       Impact factor: 11.105

  9 in total
  4 in total

1.  A Bayesian network for diagnosis of primary bone tumors.

Authors:  C E Kahn; J J Laur; G F Carrera
Journal:  J Digit Imaging       Date:  2001-06       Impact factor: 4.056

2.  Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer.

Authors:  C E Kahn; L M Roberts; K Wang; D Jenks; P Haddawy
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1995

3.  Application of an artificial neural network to the computer-aided differentiation of focal liver disease in MR imaging.

Authors:  Xuejun Zhang; Masayuki Kanematsu; Hiroshi Fujita; Xiangrong Zhou; Takeshi Hara; Ryujiro Yokoyama; Hiroaki Hoshi
Journal:  Radiol Phys Technol       Date:  2009-05-14

4.  Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.

Authors:  Jeffrey D Rudie; Jeffrey Duda; Michael Tran Duong; Po-Hao Chen; Long Xie; Robert Kurtz; Jeffrey B Ware; Joshua Choi; Raghav R Mattay; Emmanuel J Botzolakis; James C Gee; R Nick Bryan; Tessa S Cook; Suyash Mohan; Ilya M Nasrallah; Andreas M Rauschecker
Journal:  J Digit Imaging       Date:  2021-06-15       Impact factor: 4.903

  4 in total

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