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Computer-aided diagnosis for ((123)I)FP-CIT imaging: impact on clinical reporting

BACKGROUND: For ((123)I)FP-CIT imaging, a number of algorithms have shown high performance in distinguishing normal patient images from those with disease, but none have yet been tested as part of reporting workflows. This study aims to evaluate the impact on reporters’ performance of a computer-aid...

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Autores principales: Taylor, Jonathan Christopher, Romanowski, Charles, Lorenz, Eleanor, Lo, Christine, Bandmann, Oliver, Fenner, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940985/
https://www.ncbi.nlm.nih.gov/pubmed/29740722
http://dx.doi.org/10.1186/s13550-018-0393-5
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author Taylor, Jonathan Christopher
Romanowski, Charles
Lorenz, Eleanor
Lo, Christine
Bandmann, Oliver
Fenner, John
author_facet Taylor, Jonathan Christopher
Romanowski, Charles
Lorenz, Eleanor
Lo, Christine
Bandmann, Oliver
Fenner, John
author_sort Taylor, Jonathan Christopher
collection PubMed
description BACKGROUND: For ((123)I)FP-CIT imaging, a number of algorithms have shown high performance in distinguishing normal patient images from those with disease, but none have yet been tested as part of reporting workflows. This study aims to evaluate the impact on reporters’ performance of a computer-aided diagnosis (CADx) tool developed from established machine learning technology. Three experienced ((123)I)FP-CIT reporters (two radiologists and one clinical scientist) were asked to visually score 155 reconstructed clinical and research images on a 5-point diagnostic confidence scale (read 1). Once completed, the process was then repeated (read 2). Immediately after submitting each image score for a second time, the CADx system output was displayed to reporters alongside the image data. With this information available, the reporters submitted a score for the third time (read 3). Comparisons between reads 1 and 2 provided evidence of intra-operator reliability, and differences between reads 2 and 3 showed the impact of the CADx. RESULTS: The performance of all reporters demonstrated a degree of variability when analysing images through visual analysis alone. However, inclusion of CADx improved consistency between reporters, for both clinical and research data. The introduction of CADx increased the accuracy of the radiologists when reporting (unfamiliar) research images but had less impact on the clinical scientist and caused no significant change in accuracy for the clinical data. CONCLUSIONS: The outcomes for this study indicate the value of CADx as a diagnostic aid in the clinic and encourage future development for more refined incorporation into clinical practice.
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spelling pubmed-59409852018-05-14 Computer-aided diagnosis for ((123)I)FP-CIT imaging: impact on clinical reporting Taylor, Jonathan Christopher Romanowski, Charles Lorenz, Eleanor Lo, Christine Bandmann, Oliver Fenner, John EJNMMI Res Original Research BACKGROUND: For ((123)I)FP-CIT imaging, a number of algorithms have shown high performance in distinguishing normal patient images from those with disease, but none have yet been tested as part of reporting workflows. This study aims to evaluate the impact on reporters’ performance of a computer-aided diagnosis (CADx) tool developed from established machine learning technology. Three experienced ((123)I)FP-CIT reporters (two radiologists and one clinical scientist) were asked to visually score 155 reconstructed clinical and research images on a 5-point diagnostic confidence scale (read 1). Once completed, the process was then repeated (read 2). Immediately after submitting each image score for a second time, the CADx system output was displayed to reporters alongside the image data. With this information available, the reporters submitted a score for the third time (read 3). Comparisons between reads 1 and 2 provided evidence of intra-operator reliability, and differences between reads 2 and 3 showed the impact of the CADx. RESULTS: The performance of all reporters demonstrated a degree of variability when analysing images through visual analysis alone. However, inclusion of CADx improved consistency between reporters, for both clinical and research data. The introduction of CADx increased the accuracy of the radiologists when reporting (unfamiliar) research images but had less impact on the clinical scientist and caused no significant change in accuracy for the clinical data. CONCLUSIONS: The outcomes for this study indicate the value of CADx as a diagnostic aid in the clinic and encourage future development for more refined incorporation into clinical practice. Springer Berlin Heidelberg 2018-05-08 /pmc/articles/PMC5940985/ /pubmed/29740722 http://dx.doi.org/10.1186/s13550-018-0393-5 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Research
Taylor, Jonathan Christopher
Romanowski, Charles
Lorenz, Eleanor
Lo, Christine
Bandmann, Oliver
Fenner, John
Computer-aided diagnosis for ((123)I)FP-CIT imaging: impact on clinical reporting
title Computer-aided diagnosis for ((123)I)FP-CIT imaging: impact on clinical reporting
title_full Computer-aided diagnosis for ((123)I)FP-CIT imaging: impact on clinical reporting
title_fullStr Computer-aided diagnosis for ((123)I)FP-CIT imaging: impact on clinical reporting
title_full_unstemmed Computer-aided diagnosis for ((123)I)FP-CIT imaging: impact on clinical reporting
title_short Computer-aided diagnosis for ((123)I)FP-CIT imaging: impact on clinical reporting
title_sort computer-aided diagnosis for ((123)i)fp-cit imaging: impact on clinical reporting
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940985/
https://www.ncbi.nlm.nih.gov/pubmed/29740722
http://dx.doi.org/10.1186/s13550-018-0393-5
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