Literature DB >> 11568944

Can electronic medical images replace hard-copy film? Defining and testing the equivalence of diagnostic tests.

N A Obuchowski1.   

Abstract

Electronic medical images are an efficient and convenient format in which to display, store and transmit radiographic information. Before electronic images can be used routinely to screen and diagnose patients, however, it must be shown that readers have the same diagnostic performance with this new format as traditional hard-copy film. Currently, there exist no suitable definitions of diagnostic equivalence. In this paper we propose two criteria for diagnostic equivalence. The first criterion ('population equivalence') considers the variability between and within readers, as well as the mean reader performance. This criterion is useful for most applications. The second criterion ('individual equivalence') involves a comparison of the test results for individual patients and is necessary when patients are followed radiographically over time. We present methods for testing both individual and population equivalence. The properties of the proposed methods are assessed in a Monte Carlo simulation study. Data from a mammography screening study is used to illustrate the proposed methods and compare them with results from more conventional methods of assessing equivalence and inter-procedure agreement. Copyright 2001 John Wiley & Sons, Ltd.

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Year:  2001        PMID: 11568944     DOI: 10.1002/sim.929

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  3 in total

1.  Semi-automatic CT-angiography based evaluation of the aortic annulus in patients prior to TAVR: interchangeability with manual measurements.

Authors:  Dominik Zinsser; Alena B Baumann; Katharina Stella Winter; Fabian Bamberg; Philipp Lange; Konstantin Nikolaou; Maximilian Reiser; Christian Kupatt; Thomas Kröncke; Florian Schwarz
Journal:  Int J Cardiovasc Imaging       Date:  2018-06-05       Impact factor: 2.357

2.  Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms.

Authors:  Katherine Elfer; Sarah Dudgeon; Victor Garcia; Kim Blenman; Evangelos Hytopoulos; Si Wen; Xiaoxian Li; Amy Ly; Bruce Werness; Manasi S Sheth; Mohamed Amgad; Rajarsi Gupta; Joel Saltz; Matthew G Hanna; Anna Ehinger; Dieter Peeters; Roberto Salgado; Brandon D Gallas
Journal:  J Med Imaging (Bellingham)       Date:  2022-07-27

3.  Nodule Classification on Low-Dose Unenhanced CT and Standard-Dose Enhanced CT: Inter-Protocol Agreement and Analysis of Interchangeability.

Authors:  Kyung Hee Lee; Kyung Won Lee; Ji Hoon Park; Kyunghwa Han; Jihang Kim; Sang Min Lee; Chang Min Park
Journal:  Korean J Radiol       Date:  2018-04-06       Impact factor: 3.500

  3 in total

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