Characterization of Ablated Bone and Muscle for Long-Pulsed Laser Ablation in Dry and Wet Conditions

Kenhagho, Hervé Nguendon; Shevchik, Sergey; Saeidi, Fatemeh; Faivre, Neige; Meylan, Bastian; Rauter, Georg; Guzman, Raphael; Cattin, Philippe C.; Wasmer, Kilian; Zam, Azhar

doi:10.3390/ma12081338

Cited by 16 publications

(15 citation statements)

References 46 publications

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“…Similar to LIBS and laser-induced shockwave measurement methods, the procedure is based on generating plasma at the local spot of the tissue. However, in our method, the laser energy applied (4.10 mJ) is much lower than that used for LIBS (38 mJ for ns-Excimer laser [32,33], 73-108 mJ for ns-Nd:YAG laser [31,[34][35][36][37][38], and 75-200 mJ for CO 2 -LIBS [41]) or shockwave measurements (200 mJ for ns-Nd:YAG [27,28], and 0.75-15 J for ms-fiber laser [29]). Also, the energy applied is well below the reported energy required to pump the tissue for random lasing (100 mJ for ns-Nd:YAG [30]), and pyrolysis analysis (300-2000 mJ for µs-Er:YAG [43,44]), which occurs inside the ablation zone in the laser-tissue interaction map.…”

Section: Advantage and Disadvantage Of The Introduced Methodsmentioning

confidence: 98%

“…In order to preserve the adjacent soft tissue, several approaches to such differentiation have been developed using the optical properties of the ablated tissues. These methods include optical coherence tomography (OCT) [12,13], Raman spectroscopy [14][15][16][17], autofluorescence spectroscopy [18,19], diffuse reflectance spectroscopy (DRS) [20][21][22][23], ablative optoacoustic techniques [24][25][26][27][28][29], random lasing [30], laser-induced breakdown spectroscopy (LIBS) [31][32][33][34][35][36][37][38][39][40][41][42], and combustion/pyrolysis light analysis [43,44]. However, many of these methods have not been tested in combination with an ablating laser; studies have focused on tissue differentiation only.…”

Section: Introductionmentioning

confidence: 99%

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Combined Nd:YAG and Er:YAG lasers for real-time closed-loop tissue-specific laser osteotomy

Abbasi¹,

Bernal²,

Hamidi³

et al. 2020

Biomed. Opt. Express

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A novel real-time and non-destructive method for differentiating soft from hard tissue in laser osteotomy has been introduced and tested in a closed-loop fashion. Two laser beams were combined: a low energy frequency-doubled nanosecond Nd:YAG for detecting the type of tissue, and a high energy microsecond Er:YAG for ablating bone. The working principle is based on adjusting the energy of the Nd:YAG laser until it is low enough to create a microplasma in the hard tissue only (different energies are required to create plasma in different tissue types). Analyzing the light emitted from the generated microplasma enables real-time feedback to a shutter that prevents the Er:YAG laser from ablating the soft tissue.

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Section: Advantage and Disadvantage Of The Introduced Methodsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Combined Nd:YAG and Er:YAG lasers for real-time closed-loop tissue-specific laser osteotomy

Abbasi¹,

Bernal²,

Hamidi³

et al. 2020

Biomed. Opt. Express

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“…Therefore, for the sake of clarity and concision, we decided to present hereinafter only the results under dry conditions. Additional details on experimental data collection are in Nguendon Kenhagho et al [7].…”

Section: Methodsmentioning

confidence: 99%

“…The use of pulsed lasers with a proper temperature stabilization system is a promising alternative, which can provide minimal‐invasive hard tissue ablation while performing clean tissue removal with minimal carbonization [1, 8, 9]. This method has several other considerable advantages over traditional mechanical tools [1, 7–13]. First, the laser cut is characterized by completely intact tissues at the exposed surfaces, which is beneficial for bone healing.…”

Section: Introductionmentioning

confidence: 99%

“…It is based on the detection of laser‐induced acoustic emission (AE). Laser‐induced AE is a well‐known phenomenon [7, 13] and the modulations of AE are strongly dependent from the chemical composition, mechanical, and optical properties of the tissues that are exposed to laser irradiation [7, 13]. The same approach has been well established in industrial applications such as laser welding and additive manufacturing [15–19].…”

Section: Introductionmentioning

confidence: 99%

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Machine learning monitoring for laser osteotomy

Shevchik

Kenhagho

Le-Quang

et al. 2021

Journal of Biophotonics

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This work proposes a new online monitoring method for an assistance during laser osteotomy. The method allows differentiating the type of ablated tissue and the applied dose of laser energy. The setup analyzes the laser‐induced acoustic emission, detected by an airborne microphone sensor. The analysis of the acoustic signals is carried out using a machine learning algorithm that is pre‐trained in a supervised manner. The efficiency of the method is experimentally evaluated with several types of tissues, which are: skin, fat, muscle, and bone. Several cutting‐edge machine learning frameworks are tested for the comparison with the resulting classification accuracy in the range of 84–99%. It is shown that the datasets for the training of the machine learning algorithms are easy to collect in real‐life conditions. In the future, this method could assist the doctors during laser osteotomy, minimizing the damage of the nearby healthy tissues and provide cleaner pathologic tissue removal.

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Machine Learning‐Based Optoacoustic Tissue Classification Method for Laser Osteotomes Using an Air‐Coupled Transducer

et al. 2020

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Background and Objectives: Using lasers instead of mechanical tools for bone cutting holds many advantages, including functional cuts, contactless interaction, and faster wound healing. To fully exploit the benefits of lasers over conventional mechanical tools, a real-time feedback to classify tissue is proposed. Study Design/Materials and Methods: In this paper, we simultaneously classified five tissue types-hard and soft bone, muscle, fat, and skin from five proximal and distal fresh porcine femurs-based on the laser-induced acoustic shock waves (ASWs) generated. For laser ablation, a nanosecond frequency-doubled Nd:YAG laser source at 532 nm and a microsecond Er:YAG laser source at 2940 nm were used to create 10 craters on the surface of each proximal and distal femur. Depending on the application, the Nd:YAG or Er:YAG can be used for bone cutting. For ASW recording, an air-coupled transducer was placed 5 cm away from the ablated spot. For tissue classification, we analyzed the measured acoustics by looking at the amplitude-frequency band of 0.11-0.27 and 0.27-0.53 MHz, which provided the least average classification error for Er:YAG and Nd:YAG, respectively. For data reduction, we used the amplitude-frequency band as an input of the principal component analysis (PCA). On the basis of PCA scores, we compared the performance of the artificial neural network (ANN), the quadratic-and Gaussian-support vector machine (SVM) to classify tissue types. A set of 14,400 data points, measured from 10 craters in four proximal and distal femurs, was used as training data, while a set of 3,600 data points from 10 craters in the remaining proximal and distal femur was considered as testing data, for each laser. Results: The ANN performed best for both lasers, with an average classification error for all tissues of 5.01 ± 5.06% and 9.12 ± 3.39%, using the Nd:YAG and Er:YAG lasers, respectively. Then, the Gaussian-SVM performed better than the quadratic SVM during the cutting with both lasers. The Gaussian-SVM yielded average classification errors of 15.17 ± 13.12% and 16.85 ± 7.59%, using the Nd:YAG and Er:YAG lasers, respectively. The worst performance was achieved with the quadratic-SVM with a classification error of 50.34 ± 35.04% and 69.96 ± 25.49%, using the Nd:YAG and Er:YAG lasers. Conclusion: We foresee using the ANN to differentiate tissues in real-time during laser osteotomy. Lasers Surg. Med.

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Characterization of Ablated Bone and Muscle for Long-Pulsed Laser Ablation in Dry and Wet Conditions

Cited by 16 publications

References 46 publications

Combined Nd:YAG and Er:YAG lasers for real-time closed-loop tissue-specific laser osteotomy

Combined Nd:YAG and Er:YAG lasers for real-time closed-loop tissue-specific laser osteotomy

Machine learning monitoring for laser osteotomy

Machine Learning‐Based Optoacoustic Tissue Classification Method for Laser Osteotomes Using an Air‐Coupled Transducer

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