Multimodality tissue identification for neurosurgery: the NASA Smart Probe project

Andrews, Russell J.; Mah, Robert; Jeffrey, Stefanie S.; Aghevli, Arash; Freitas, Kátia Nêyla de; Guerrero, M.; Papasin, Richard; Reed, C.W.

doi:10.1109/aiprw.2000.953618

Cited by 1 publication

(2 citation statements)

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“…The majority of studies that we found in this category ( 19 – 28 ) used spectroscopic methods for delineating the brain tumor regions ( Table 3 ) by showing that the spectroscopic information can successfully characterize tumor tissues during neurosurgery. Furthermore, Kenhagho et al ( 29 ) demonstrated that optoacoustic properties can characterize the tissue type during laser osteotomy.…”

Section: Resultsmentioning

confidence: 99%

“…Spectroscopic sensing methods found in the reviewed articles can be categorized into the following technologies: optical scattering spectroscopy probe ( 19 ), high-resolution magic angle spinning nuclear resonance spectrometer ( 21 ), Raman spectrometers (i.e. coherent anti- Stokes Raman scattering, visible resonance Raman, stimulated Raman scattering) ( 11 , 20 , 22 – 27 ), diffuse reflectance spectroscopy ( 30 , 31 ), selective narrow-band imaging ( 32 ), clinical endomicroscopy ( 28 ), and fluorescence imaging ( 33 ).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

Massalimova

Timmermans²,

Esfandiari³

et al. 2022

Front. Surg.

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Accurate tissue differentiation during orthopedic and neurological surgeries is critical, given that such surgeries involve operations on or in the vicinity of vital neurovascular structures and erroneous surgical maneuvers can lead to surgical complications. By now, the number of emerging technologies tackling the problem of intraoperative tissue classification methods is increasing. Therefore, this systematic review paper intends to give a general overview of existing technologies. The review was done based on the PRISMA principle and two databases: PubMed and IEEE Xplore. The screening process resulted in 60 full-text papers. The general characteristics of the methodology from extracted papers included data processing pipeline, machine learning methods if applicable, types of tissues that can be identified with them, phantom used to conduct the experiment, and evaluation results. This paper can be useful in identifying the problems in the current status of the state-of-the-art intraoperative tissue classification methods and designing new enhanced techniques.

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Section: Resultsmentioning

confidence: 99%