Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening
Abstract 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 det...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-11-01
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| Series: | Insights into Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13244-023-01561-z |
| _version_ | 1827603488542031872 |
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| author | Yihui Du Marcel J. W. Greuter Mathias W. Prokop Geertruida H. de Bock |
| author_facet | Yihui Du Marcel J. W. Greuter Mathias W. Prokop Geertruida H. de Bock |
| author_sort | Yihui Du |
| collection | DOAJ |
| description | Abstract 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 |
| first_indexed | 2024-03-09T05:41:23Z |
| format | Article |
| id | doaj.art-a3146910efb042dd947bc6a116e6f81d |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-03-09T05:41:23Z |
| publishDate | 2023-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-a3146910efb042dd947bc6a116e6f81d2023-12-03T12:25:11ZengSpringerOpenInsights into Imaging1869-41012023-11-011411710.1186/s13244-023-01561-zPricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screeningYihui Du0Marcel J. W. Greuter1Mathias W. Prokop2Geertruida H. de Bock3Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Normal UniversityDepartment of Radiology, University Medical Center Groningen, University of GroningenDepartment of Radiology, University Medical Center Groningen, University of GroningenDepartment of Epidemiology, University Medical Center Groningen, University of GroningenAbstract 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 Abstracthttps://doi.org/10.1186/s13244-023-01561-zDeep learningComputed aid detectionPricingLung noduleLung cancer screening |
| spellingShingle | Yihui Du Marcel J. W. Greuter Mathias W. Prokop Geertruida H. de Bock Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening Insights into Imaging Deep learning Computed aid detection Pricing Lung nodule 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 | Deep learning Computed aid detection Pricing Lung nodule Lung cancer screening |
| url | https://doi.org/10.1186/s13244-023-01561-z |
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