2022
DOI: 10.1007/s11548-022-02713-0
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Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation

Abstract: Purpose During brain tumor surgery, care must be taken to accurately differentiate between tumorous and healthy tissue, as inadvertent resection of functional brain areas can cause severe consequences. Since visual assessment can be difficult during tissue resection, neurosurgeons have to rely on the mechanical perception of tissue, which in itself is inherently challenging. A commonly used instrument for tumor resection is the ultrasonic aspirator, whose system behavior is already dependent on t… Show more

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Cited by 7 publications
(1 citation statement)
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“…Models were able to distinguish signal differences between native tissue and brain tumor, achieving a mean F1 of 0.900 using a neural network approach. Intraoperative deployment of these tissue models can help surgeons to resect malignancies while preserving as much healthy neural tissue as possible [ 9 ]. A final example of surgical margin management was presented by Bianchi et al in which they used preoperative multiparametric magnetic resonance imaging (mpMRI) to guide intraoperative frozen tissue sampling for margin detection during robot-assisted radical prostatectomy.…”
Section: Ai In Intelligent Assistance and Robotic Autonomymentioning
confidence: 99%
“…Models were able to distinguish signal differences between native tissue and brain tumor, achieving a mean F1 of 0.900 using a neural network approach. Intraoperative deployment of these tissue models can help surgeons to resect malignancies while preserving as much healthy neural tissue as possible [ 9 ]. A final example of surgical margin management was presented by Bianchi et al in which they used preoperative multiparametric magnetic resonance imaging (mpMRI) to guide intraoperative frozen tissue sampling for margin detection during robot-assisted radical prostatectomy.…”
Section: Ai In Intelligent Assistance and Robotic Autonomymentioning
confidence: 99%