Soft urinary bladder phantom for endoscopic training
Abstract: Bladder cancer (BC) is the main disease in the urinary tract with a high recurrence rate and it is diagnosed by cystoscopy (CY). To train the CY procedures, a realistic bladder phantom with correct anatomy and physiological properties is highly required. Here, we report a soft bladder phantom (FlexBlad) that mimics many important features of a human bladder. Under filling, it shows a large volume expansion of more than 300% with a tunable compliance in the range of 12.2 ± 2.8 – 32.7 ± 5.4 mL cmH2O−1 by engineering the thickness of the bladder wall. By 3D printing and multi-step molding, detailed anatomical structures are represented on the inner bladder wall, including sub-millimeter blood vessels and reconfigurable bladder tumors. Endoscopic inspection and tumor biopsy were successfully performed. A multi-center study was carried out, where two groups of urologists with different experience levels executed consecutive CYs in the phantom and filled in questionnaires. The learning curves reveal that the FlexBlad has a positive effect in the endourological training across different skill levels. The statistical results validate the usability of the phantom as a valuable educational tool, and the dynamic feature expands its use as a versatile endoscopic training platform
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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Annals of biomedical engineering. - 49 (2021) , 2412–2420, ISSN: 1573-9686
- Ereignis
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Veröffentlichung
- (wo)
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Freiburg
- (wer)
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Universität
- (wann)
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2021
- Urheber
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Choi, Eunjin
Waldbillig, Frank
Jeong, Moonkwang
Li, Dandan
Goyal, Rahul
Weber, Patricia
Miernik, Arkadiusz
Grüne, Britta
Hein, Simon
Suarez-Ibarrola, Rodrigo
Kriegmair, Maximilian
Qiu, Tian
- DOI
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10.1007/s10439-021-02793-0
- URN
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urn:nbn:de:bsz:25-freidok-2187561
- Rechteinformation
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Kein Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:36 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Choi, Eunjin
- Waldbillig, Frank
- Jeong, Moonkwang
- Li, Dandan
- Goyal, Rahul
- Weber, Patricia
- Miernik, Arkadiusz
- Grüne, Britta
- Hein, Simon
- Suarez-Ibarrola, Rodrigo
- Kriegmair, Maximilian
- Qiu, Tian
- Universität
Entstanden
- 2021