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type: software
license: GPL-3.0-only
title: 'RJafroc: Artificial Intelligence Systems and Observer Performance'
version: 2.1.3
doi: 10.32614/CRAN.package.RJafroc
abstract: 'Analyzing the performance of artificial intelligence (AI) systems/algorithms
  characterized by a ''search-and-report'' strategy. Historically observer performance
  has dealt with measuring radiologists'' performances in search tasks, e.g., searching
  for lesions in medical images and reporting them, but the implicit location information
  has been ignored. The implemented methods apply to analyzing the absolute and relative
  performances of AI systems, comparing AI performance to a group of human readers
  or optimizing the reporting threshold of an AI system. In addition to performing
  historical receiver operating receiver operating characteristic (ROC) analysis (localization
  information ignored), the software also performs free-response receiver operating
  characteristic (FROC) analysis, where lesion localization information is used. A
  book using the software has been published: Chakraborty DP: Observer Performance
  Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based
  Examples, Taylor-Francis LLC; 2017: <https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840>.
  Online updates to this book, which use the software, are at <https://dpc10ster.github.io/RJafrocQuickStart/>,
  <https://dpc10ster.github.io/RJafrocRocBook/> and at <https://dpc10ster.github.io/RJafrocFrocBook/>.
  Supported data collection paradigms are the ROC, FROC and the location ROC (LROC).
  ROC data consists of single ratings per images, where a rating is the perceived
  confidence level that the image is that of a diseased patient. An ROC curve is a
  plot of true positive fraction vs. false positive fraction. FROC data consists of
  a variable number (zero or more) of mark-rating pairs per image, where a mark is
  the location of a reported suspicious region and the rating is the confidence level
  that it is a real lesion. LROC data consists of a rating and a location of the most
  suspicious region, for every image. Four models of observer performance, and curve-fitting
  software, are implemented: the binormal model (BM), the contaminated binormal model
  (CBM), the correlated contaminated binormal model (CORCBM), and the radiological
  search model (RSM). Unlike the binormal model, CBM, CORCBM and RSM predict ''proper''
  ROC curves that do not inappropriately cross the chance diagonal. Additionally,
  RSM parameters are related to search performance (not measured in conventional ROC
  analysis) and classification performance. Search performance refers to finding lesions,
  i.e., true positives, while simultaneously not finding false positive locations.
  Classification performance measures the ability to distinguish between true and
  false positive locations. Knowing these separate performances allows principled
  optimization of reader or AI system performance. This package supersedes Windows
  JAFROC (jackknife alternative FROC) software V4.2.1, <https://github.com/dpc10ster/WindowsJafroc>.
  Package functions are organized as follows. Data file related function names are
  preceded by ''Df'', curve fitting functions by ''Fit'', included data sets by ''dataset'',
  plotting functions by ''Plot'', significance testing functions by ''St'', sample
  size related functions by ''Ss'', data simulation functions by ''Simulate'' and
  utility functions by ''Util''. Implemented are figures of merit (FOMs) for quantifying
  performance and functions for visualizing empirical or fitted operating characteristics:
  e.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC (wAFROC) curves. For
  fully crossed study designs significance testing of reader-averaged FOM differences
  between modalities is implemented via either Dorfman-Berbaum-Metz or the Obuchowski-Rockette
  methods. Also implemented is single modality analysis, which allows comparison of
  performance of a group of radiologists to a specified value, or comparison of AI
  to a group of radiologists interpreting the same cases. Crossed-modality analysis
  is implemented wherein there are two crossed modality factors and the aim is to
  determined performance in each modality factor averaged over all levels of the second
  factor. Sample size estimation tools are provided for ROC and FROC studies; these
  use estimates of the relevant variances from a pilot study to predict required numbers
  of readers and cases in a pivotal study to achieve the desired power. Utility and
  data file manipulation functions allow data to be read in any of the currently used
  input formats, including Excel, and the results of the analysis can be viewed in
  text or Excel output files. The methods are illustrated with several included datasets
  from the author''s collaborations. This update includes improvements to the code,
  some as a result of user-reported bugs and new feature requests, and others discovered
  during ongoing testing and code simplification.'
authors:
- family-names: Chakraborty
  given-names: Dev
  email: dpc10ster@gmail.com
- family-names: Zhai
  given-names: Xuetong
  email: xuetong.zhai@gmail.com
repository: https://dpc10ster.r-universe.dev
commit: edb840e5f0ed0fdf53d5413797ec6f3defe0bbbe
url: https://dpc10ster.github.io/RJafroc/
date-released: '2023-07-31'
contact:
- family-names: Chakraborty
  given-names: Dev
  email: dpc10ster@gmail.com