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@dpc10ster dpc10ster released this 15 Dec 16:58
· 1 commit to cran3 since this release
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Package name change: Artificial Intelligence Systems and Observer Performance

Reason for submission

  • This is an update to CRAN version (v1.3.2) which installed with no errors, warnings, or notes (2020-03-06) on all platforms. The package is still passing all checks on all platforms (as of 2020-12-10).
  • This update (v2.0.1) includes includes many 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 since the last successful submission.

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.

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