Computer assisted planning of autologous fat grafting in breast.
Patete P., Rigotti G., Marchi A., Baroni G.
Laboratory Study, published in Comput Aided Surg (2012) — summary generated from the PubMed abstract.
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Laboratory Study
- Journal
- Comput Aided Surg (2012)
- Country
- England
- Reported sample size
- —
- Source database
- PubMed
- PMID
- 23253184
- DOI
- 10.3109/10929088.2012.745169
- Citations
- 1
Abstract (original English)
Autologous fat grafting is an emerging and promising surgical technique in regenerative medicine, and its application is quickly spreading in plastic and reconstructive surgery of the breast. However, despite the advantages of the technique, surgical complications may occur, such as implanted tissue necrosis and resorption and onset of microcalcifications. In view of the hypothesis that the uniformity of the lipoaspirate transplantation is related to graft survival and a lower probability of complications, we developed an interactive lipomodeling planning software application based on a genetic algorithm that allows automatic optimization of the uniformity of fat tissue distribution. The input dataset consists of a 3D model of the patient's thorax, created from MRI scans, on which relevant structures are segmented. The developed software was tested starting from either an automatically generated plan or an initial guess of the optimal surgical plan, and in both cases the application yielded a consistent improvement in the planned fat tissue distribution by optimizing the position of the insertion points and the direction of the insertion pathways. On the basis of the simulations performed, the use of genetic algorithms for optimization of the geometry of autologous fat transfer in the breast proved to be effective. These results will foster further activities focused on the com
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
- • This is preclinical work; animal or laboratory results cannot be applied to humans.
Evidence level
Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.
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