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How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization
Many studies have shown that using a computer-aided detection (CAD) system does not significantly improve diagnostic accuracy in radiology, possibly because radiologists fail to interpret the CAD results properly. We tested this possibility using screening mammography as an illustrative example. We...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209598/ https://www.ncbi.nlm.nih.gov/pubmed/35723763 http://dx.doi.org/10.1186/s41235-022-00375-9 |
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author | Branch, Fallon Williams, K. Matthew Santana, Isabella Noel Hegdé, Jay |
author_facet | Branch, Fallon Williams, K. Matthew Santana, Isabella Noel Hegdé, Jay |
author_sort | Branch, Fallon |
collection | PubMed |
description | Many studies have shown that using a computer-aided detection (CAD) system does not significantly improve diagnostic accuracy in radiology, possibly because radiologists fail to interpret the CAD results properly. We tested this possibility using screening mammography as an illustrative example. We carried out two experiments, one using 28 practicing radiologists, and a second one using 25 non-professional subjects. During each trial, subjects were shown the following four pieces of information necessary for evaluating the actual probability of cancer in a given unseen mammogram: the binary decision of the CAD system as to whether the mammogram was positive for cancer, the true-positive and false-positive rates of the system, and the prevalence of breast cancer in the relevant patient population. Based only on this information, the subjects had to estimate the probability that the unseen mammogram in question was positive for cancer. Additionally, the non-professional subjects also had to decide, based on the same information, whether to recall the patients for additional testing. Both groups of subjects similarly (and significantly) overestimated the cancer probability regardless of the categorical CAD decision, suggesting that this effect is not peculiar to either group. The misestimations were not fully attributable to causes well-known in other contexts, such as base rate neglect or inverse fallacy. Non-professional subjects tended to recall the patients at high rates, even when the actual probably of cancer was at or near zero. Moreover, the recall rates closely reflected the subjects’ estimations of cancer probability. Together, our results show that subjects interpret CAD system output poorly when only the probabilistic information about the underlying decision parameters is available to them. Our results also highlight the need for making the output of CAD systems more readily interpretable, and for providing training and assistance to radiologists in evaluating the output. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41235-022-00375-9. |
format | Online Article Text |
id | pubmed-9209598 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-92095982022-06-22 How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization Branch, Fallon Williams, K. Matthew Santana, Isabella Noel Hegdé, Jay Cogn Res Princ Implic Brief Report Many studies have shown that using a computer-aided detection (CAD) system does not significantly improve diagnostic accuracy in radiology, possibly because radiologists fail to interpret the CAD results properly. We tested this possibility using screening mammography as an illustrative example. We carried out two experiments, one using 28 practicing radiologists, and a second one using 25 non-professional subjects. During each trial, subjects were shown the following four pieces of information necessary for evaluating the actual probability of cancer in a given unseen mammogram: the binary decision of the CAD system as to whether the mammogram was positive for cancer, the true-positive and false-positive rates of the system, and the prevalence of breast cancer in the relevant patient population. Based only on this information, the subjects had to estimate the probability that the unseen mammogram in question was positive for cancer. Additionally, the non-professional subjects also had to decide, based on the same information, whether to recall the patients for additional testing. Both groups of subjects similarly (and significantly) overestimated the cancer probability regardless of the categorical CAD decision, suggesting that this effect is not peculiar to either group. The misestimations were not fully attributable to causes well-known in other contexts, such as base rate neglect or inverse fallacy. Non-professional subjects tended to recall the patients at high rates, even when the actual probably of cancer was at or near zero. Moreover, the recall rates closely reflected the subjects’ estimations of cancer probability. Together, our results show that subjects interpret CAD system output poorly when only the probabilistic information about the underlying decision parameters is available to them. Our results also highlight the need for making the output of CAD systems more readily interpretable, and for providing training and assistance to radiologists in evaluating the output. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41235-022-00375-9. Springer International Publishing 2022-06-20 /pmc/articles/PMC9209598/ /pubmed/35723763 http://dx.doi.org/10.1186/s41235-022-00375-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Brief Report Branch, Fallon Williams, K. Matthew Santana, Isabella Noel Hegdé, Jay How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization |
title | How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization |
title_full | How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization |
title_fullStr | How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization |
title_full_unstemmed | How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization |
title_short | How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization |
title_sort | how well do practicing radiologists interpret the results of cad technology? a quantitative characterization |
topic | Brief Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9209598/ https://www.ncbi.nlm.nih.gov/pubmed/35723763 http://dx.doi.org/10.1186/s41235-022-00375-9 |
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