Artificial intelligence support in MR imaging of incidental renal masses: an early health technology assessment
OBJECTIVE: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up. - MATERIALS AND METHODS: For estimation of quality-adjusted life y...
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| Hauptverfasser: | , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
23 February 2024
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| In: |
European radiology
Year: 2024, Jahrgang: 34, Heft: 9, Pages: 5856-5865 |
| ISSN: | 1432-1084 |
| DOI: | 10.1007/s00330-024-10643-5 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00330-024-10643-5 |
| Verfasserangaben: | Alexander W. Marka, Johanna Luitjens, Florian T. Gassert, Lisa Steinhelfer, Egon Burian, Johannes Rübenthaler, Vincent Schwarze, Matthias F. Froelich, Marcus R. Makowski and Felix G. Gassert |
| Zusammenfassung: | OBJECTIVE: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up. - MATERIALS AND METHODS: For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of |
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| Beschreibung: | Gesehen am 26.07.2024 |
| Beschreibung: | Online Resource |
| ISSN: | 1432-1084 |
| DOI: | 10.1007/s00330-024-10643-5 |