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Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening

OBJECTIVE: An increasing number of commercial deep learning computer-aided detection (DL-CAD) systems are available but their cost-saving potential is largely unknown. This study aimed to gain insight into appropriate pricing for DL-CAD in different reading modes to be cost-saving and to determine t...

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Autores principales: Du, Yihui, Greuter, Marcel J. W., Prokop, Mathias W., de Bock, Geertruida H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682324/
https://www.ncbi.nlm.nih.gov/pubmed/38010436
http://dx.doi.org/10.1186/s13244-023-01561-z
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author Du, Yihui
Greuter, Marcel J. W.
Prokop, Mathias W.
de Bock, Geertruida H.
author_facet Du, Yihui
Greuter, Marcel J. W.
Prokop, Mathias W.
de Bock, Geertruida H.
author_sort Du, Yihui
collection PubMed
description OBJECTIVE: An increasing number of commercial deep learning computer-aided detection (DL-CAD) systems are available but their cost-saving potential is largely unknown. This study aimed to gain insight into appropriate pricing for DL-CAD in different reading modes to be cost-saving and to determine the potentially most cost-effective reading mode for lung cancer screening. METHODS: In three representative settings, DL-CAD was evaluated as a concurrent, pre-screening, and second reader. Scoping review was performed to estimate radiologist reading time with and without DL-CAD. Hourly cost of radiologist time was collected for the USA (€196), UK (€127), and Poland (€45), and monetary equivalence of saved time was calculated. The minimum number of screening CTs to reach break-even was calculated for one-time investment of €51,616 for DL-CAD. RESULTS: Mean reading time was 162 (95% CI: 111–212) seconds per case without DL-CAD, which decreased by 77 (95% CI: 47–107) and 104 (95% CI: 71–136) seconds for DL-CAD as concurrent and pre-screening reader, respectively, and increased by 33–41 s for DL-CAD as second reader. This translates into €1.0–4.3 per-case cost for concurrent reading and €0.8–5.7 for pre-screening reading in the USA, UK, and Poland. To achieve break-even with a one-time investment, the minimum number of CT scans was 12,300–53,600 for concurrent reader, and 9400–65,000 for pre-screening reader in the three countries. CONCLUSIONS: Given current pricing, DL-CAD must be priced substantially below €6 in a pay-per-case setting or used in a high-workload environment to reach break-even in lung cancer screening. DL-CAD as pre-screening reader shows the largest potential to be cost-saving. CRITICAL RELEVANCE STATEMENT: Deep-learning computer-aided lung nodule detection (DL-CAD) software must be priced substantially below 6 euro in a pay-per-case setting or must be used in high-workload environments with one-time investment in order to achieve break-even. DL-CAD as a pre-screening reader has the greatest cost savings potential. KEY POINTS: • DL-CAD must be substantially below €6 in a pay-per-case setting to reach break-even. • DL-CAD must be used in a high-workload screening environment to achieve break-even. • DL-CAD as a pre-screening reader shows the largest potential to be cost-saving. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01561-z.
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spelling pubmed-106823242023-11-30 Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening Du, Yihui Greuter, Marcel J. W. Prokop, Mathias W. de Bock, Geertruida H. Insights Imaging Original Article OBJECTIVE: An increasing number of commercial deep learning computer-aided detection (DL-CAD) systems are available but their cost-saving potential is largely unknown. This study aimed to gain insight into appropriate pricing for DL-CAD in different reading modes to be cost-saving and to determine the potentially most cost-effective reading mode for lung cancer screening. METHODS: In three representative settings, DL-CAD was evaluated as a concurrent, pre-screening, and second reader. Scoping review was performed to estimate radiologist reading time with and without DL-CAD. Hourly cost of radiologist time was collected for the USA (€196), UK (€127), and Poland (€45), and monetary equivalence of saved time was calculated. The minimum number of screening CTs to reach break-even was calculated for one-time investment of €51,616 for DL-CAD. RESULTS: Mean reading time was 162 (95% CI: 111–212) seconds per case without DL-CAD, which decreased by 77 (95% CI: 47–107) and 104 (95% CI: 71–136) seconds for DL-CAD as concurrent and pre-screening reader, respectively, and increased by 33–41 s for DL-CAD as second reader. This translates into €1.0–4.3 per-case cost for concurrent reading and €0.8–5.7 for pre-screening reading in the USA, UK, and Poland. To achieve break-even with a one-time investment, the minimum number of CT scans was 12,300–53,600 for concurrent reader, and 9400–65,000 for pre-screening reader in the three countries. CONCLUSIONS: Given current pricing, DL-CAD must be priced substantially below €6 in a pay-per-case setting or used in a high-workload environment to reach break-even in lung cancer screening. DL-CAD as pre-screening reader shows the largest potential to be cost-saving. CRITICAL RELEVANCE STATEMENT: Deep-learning computer-aided lung nodule detection (DL-CAD) software must be priced substantially below 6 euro in a pay-per-case setting or must be used in high-workload environments with one-time investment in order to achieve break-even. DL-CAD as a pre-screening reader has the greatest cost savings potential. KEY POINTS: • DL-CAD must be substantially below €6 in a pay-per-case setting to reach break-even. • DL-CAD must be used in a high-workload screening environment to achieve break-even. • DL-CAD as a pre-screening reader shows the largest potential to be cost-saving. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-023-01561-z. Springer Vienna 2023-11-27 /pmc/articles/PMC10682324/ /pubmed/38010436 http://dx.doi.org/10.1186/s13244-023-01561-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Original Article
Du, Yihui
Greuter, Marcel J. W.
Prokop, Mathias W.
de Bock, Geertruida H.
Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening
title Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening
title_full Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening
title_fullStr Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening
title_full_unstemmed Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening
title_short Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening
title_sort pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in ct lung cancer screening
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682324/
https://www.ncbi.nlm.nih.gov/pubmed/38010436
http://dx.doi.org/10.1186/s13244-023-01561-z
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