Package: RJafroc
Type: Package
Title: Artificial Intelligence Systems and Observer Performance
Version: 2.0.1
Date: 2020-12-15
Authors@R: c(person("Dev", "Chakraborty", role = c("cre","aut","cph"), email = "dpc10ster@gmail.com"),
             person("Peter", "Phillips", role = c("aut"), email = "peter.phillips@cumbria.ac.uk"),
             person("Xuetong", "Zhai", role = c("aut"), email = "xuetong.zhai@gmail.com"))
Depends: R (>= 3.5.0)
Imports: bbmle, binom, dplyr, ggplot2, mvtnorm, numDeriv, openxlsx,
        readxl, Rcpp, stats, stringr, tools, utils
LinkingTo: Rcpp
Description: Analyzing the performance of artificial intelligence (AI) systems/algorithms characterized 
    by a "search-and-report" strategy. While historically observer performance has dealt with measuring radiologists' 
    performance in search tasks – i.e., searching for lesions in medical images and reporting them - the software described 
    here applies equally to any task involving searching for and reporting arbitrary targets in images. The package can be 
    used to analyze the performance of AI systems, compare AI performance to a group of human readers or optimize the reporting 
    threshold of an AI system. In addition to performing conventional receiver operating characteristic (ROC) analysis 
    (localization information ignored), the software also performs free-response receiver operating characteristic (FROC) 
    analysis, where lesion localization information is integral to the analyzed data. 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. An online update of this book is at <https://dpc10ster.github.io/RJafrocBook/>. 
    Illustrations of the software (vignettes) are at <https://dpc10ster.github.io/RJafroc/>. 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. RJafroc 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 treatment 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 treatment factors and the aim is to determined performance in each treatment 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 version corrects bugs, simplifies usage of 
    the software and updates the dataset structure. All changes are noted in NEWS.
License: GPL-3
LazyData: true
URL: https://dpc10ster.github.io/RJafroc/
RoxygenNote: 7.1.1
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2020-12-15 16:07:00 UTC; Dev
Author: Dev Chakraborty [cre, aut, cph],
  Peter Phillips [aut],
  Xuetong Zhai [aut]
Maintainer: Dev Chakraborty <dpc10ster@gmail.com>
Repository: CRAN
Date/Publication: 2020-12-15 17:00:02 UTC
Built: R 3.6.3; x86_64-w64-mingw32; 2021-05-12 23:01:20 UTC; windows
Archs: i386, x64
