| |  Online-Ressource |
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| Verfasst von: | Knödler, Leonard [VerfasserIn]  |
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| | Odenthal, Jan [VerfasserIn] |
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| | Prantl, Lukas [VerfasserIn] |
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| | Özdemir, Berkin [VerfasserIn] |
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| | Kehrer, Andreas [VerfasserIn] |
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| | Kauke-Navarro, Martin [VerfasserIn] |
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| | Matar, Dany Y. [VerfasserIn] |
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| | Obed, Doha [VerfasserIn] |
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| | Panayi, Adriana C. [VerfasserIn] |
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| | Broer, P. Niclas [VerfasserIn] |
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| | Chartier, Christian [VerfasserIn] |
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| | Knoedler, Samuel [VerfasserIn] |
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| Titel: | Artificial intelligence-enabled simulation of gluteal augmentation |
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| Titelzusatz: | a helpful tool in preoperative outcome simulation? |
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| Verf.angabe: | Leonard Knoedler, Jan Odenthal, Lukas Prantl, Berkin Oezdemir, Andreas Kehrer, Martin Kauke-Navarro, Dany Y. Matar, Doha Obed, Adriana C. Panayi, P. Niclas Broer, Christian Chartier, Samuel Knoedler |
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| E-Jahr: | 2023 |
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| Jahr: | May 2023 |
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| Umfang: | 8 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Online verfügbar: 9. Februar 2023, Artikelversion: 30. März 2023 ; Gesehen am 23.08.2023 |
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| Titel Quelle: | Enthalten in: Journal of plastic, reconstructive & aesthetic surgery |
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| Ort Quelle: | Amsterdam [u.a.] : Elsevier, 2006 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 80(2023) vom: Mai, Seite 94-101 |
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| ISSN Quelle: | 1878-0539 |
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| Abstract: | Background - While the buttock region is considered an esthetic hallmark, the Brazilian butt lift (BBL) remains controversially discussed in the plastic surgery community. This is due to its contentious safety profile. Thus, informed consent and patient education play a key role in preoperative planning. To this end, we aimed to program an easy-to-use, widely accessible, and low-budget algorithm that produces reliable outcome simulations. - Methods - The conditional generative adversarial network (GAN) was trained using pre- and postoperative images from 1628 BBL patients. To validate outcome simulation, 25 GAN-generated images were assessed deploying 67 Amazon Mechanical Turk Workers (Mturks). - Results - Mturks could not differentiate between GAN-generated and real patient images in approximately 49.4% of all trials. - Conclusion - This study presents a free-to-use, widely accessible, and reliable algorithm to visualize potential surgical outcomes that could potentially be applied in other fields of plastic surgery. |
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| DOI: | doi:10.1016/j.bjps.2023.01.039 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1016/j.bjps.2023.01.039 |
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| | Volltext: https://www.sciencedirect.com/science/article/pii/S1748681523000530 |
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| | DOI: https://doi.org/10.1016/j.bjps.2023.01.039 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Artificial intelligence |
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| | Autologous fat grafting |
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| | Brazilian butt lifting |
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| | Gluteal augmentation |
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| K10plus-PPN: | 1857669118 |
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| Verknüpfungen: | → Zeitschrift |
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Artificial intelligence-enabled simulation of gluteal augmentation / Knödler, Leonard [VerfasserIn]; May 2023 (Online-Ressource)
