{Reference Type}: Journal Article
{Title}: Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening.
{Author}: Du Y;Greuter MJW;Prokop MW;de Bock GH;
{Journal}: Insights Imaging
{Volume}: 14
{Issue}: 1
{Year}: 2023 Nov 27
{Factor}: 5.036
{DOI}: 10.1186/s13244-023-01561-z
{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.
UNASSIGNED: 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.
CONCLUSIONS: • 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.