Laser-induced breakdown spectroscopy as a potential tool for autocarbonization detection in laserosteotomy

Abstract

In laserosteotomy, it is vital to avoid thermal damage of the surrounding tissue, such as carbonization, since carbonization does not only deteriorate the ablation efficiency but also prolongs the healing process. The state-of-the-art method to avoid carbonization is irrigation systems; however, it is difficult to determine the desired flow rate of the air and cooling water based on previous experiments without online monitoring of the bone surface. Lack of such feedback during the ablation process can cause carbonization in case of a possible error in the irrigation system or slow down the cutting process when irrigating with too much cooling water. The aim of this paper is to examine laser-induced breakdown spectroscopy as a potential tool for autocarbonization detection in laserosteotomy. By monitoring the laser-driven plasma generated during nanosecond pulse ablation of porcine bone samples, carbonization is hypothesized to be detectable. For this, the collected spectra were analyzed based on variation of a specific pair of emission line ratios in both groups of samples: normal and carbonized bone. The results confirmed a high accuracy of over 95% in classifying normal and carbonized bone.

Differentiation of femur bone from surrounding soft tissue using laser-induced breakdown spectroscopy as a feedback system for smart laserosteotomy

“…Later on, the ratio between the intensity of the stored peaks was calculated and used as input of Discriminant Function Analysis (DFA) to generate functions to classify sample groups. This ratio-based analysis allows for more robust results, as it is more stable than data based on absolute intensity values in the spectra of a given tissue type [18]. Afterwards, the performance of the employed classifier was evaluated using ROC analysis.…”

“…In order to advance the range of applications for laserosteotomes, there is a need for a feedback control system that can provide accurate information on the type and properties of the tissue being cut. Such feedback mechanisms can rely on photoacoustic, spectroscopic or OCT-based measurements [16][17][18][19][20][21]. Hereby, the potential spectroscopic methods include diffuse reflectance, laser-induced breakdown, Raman, and fluorescence spectroscopy [22][23][24][25][26].…”

“…Among them, laser-induced breakdown spectroscopy (LIBS), as a powerful analytical technique, seems to be most promising to us since it enables using the same laser as during the cutting process. Authors have recently shown that LIBS feedback systems for laserosteotomy could help surgeons avoid carbonizing bone [18]. Moreover, LIBS has shown its potential to differentiate between healthy and carious teeth [27,28], bone and spinal cord [29,30], bone and cartilage [31], nerve and fat [32], as well as nerve and gland [33].…”

Quantification of toxic metallic elements using machine learning techniques and spark emission spectroscopy

“…Moreover, ICP-MS is most suitable for heavier elements and metals so has a high LOD for lighter toxic metals and is not available in field-deployed, real-time applications. Gottfried et al (2009), pharmaceuticals Mukherjee and Cheng (2008a,b);St-Onge et al (2002), and biomedical Abbasi et al (2018); Baudelet et al (2006);Davari et al (2018). Particularly, Fisher et al Fisher et al (2001) studied various toxic metals in aerosols by optimizing the spectrometer response with respect to gate delay.…”

Plasma plume expansion dynamics in nanosecond Nd:YAG laserosteotome

“…LIBS feedback systems for laserosteotomy could help surgeons avoid cutting a wrong tissue or make it carbonized [1][2][3][4][5][6][7][8][9]. Laserosteotomes connected to such a feedback system are so-called smart laserosteotomes [10].…”