A First Approach to Miniaturized Optoacoustic Feedback Sensor for Smart Laser Osteotome : Fiber-Coupled Fabry-Pérot Etalon Sensor

Kenhagho, Hervé Nguendon; Canbaz, Ferda; Rauter, Georg; Guzman, Raphael; Cattin, Philippe C.; Zam, Azhar

doi:10.1109/sensors43011.2019.8956743

Cited by 1 publication

(13 citation statements)

References 16 publications

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“…In our previous studies, we showed that the frequency band of 1.67-2.08 MHz (FR5) yielded the best results in terms of high average classification accuracy and AUCs with cross-validation between tissues. 24,27,41 The higher values were theoretically expected, as the ablation with the Nd:YAG is based on plasma mediation, which increases the pressure energy measured by the optical sensor. The authors believe by adding nerve tissues the frequency range that provided the best classification accuracy shifted to 1.25-1.67 MHz (FR4).…”

Section: Discussionmentioning

confidence: 95%

“…The laser beam was focused at a 7.5 cm focal distance (LA5042; Thorlabs), using an antireflection coated biconvex lens. A detailed explanation of the experimental setup, including calibration steps, was reported in previous work 37 . The focal point was positioned 2 mm under the sample surface to achieve deeper ablation.…”

Section: Methodsmentioning

confidence: 99%

“…Depending on how the interferometer is configured, interference pattern variations are triggered by changes in the mean free path, the optical wavelength, or the optical phase 24 . The resulting variations in intensity or frequency of the interferometer output are measured by a photodiode and generate information about the ASW signals 3,27 …”

Section: Introductionmentioning

confidence: 99%

“…In this study, we used our custom‐made fiber‐coupled Fabry–Pérot etalon system to measure ASWs during laser ablation 27 . Among the advantages of using etalon sensors are their compactness and broad usable bandwidths (exceeding 2.5 MHz).…”

Section: Introductionmentioning

confidence: 99%

“…24 The resulting variations in intensity or frequency of the interferometer output are measured by a photodiode and generate information about the ASW signals. 3,27 Also, according to the literature reviews, support vector machine (SVM) and artificial neural network (ANN) are effective and practical solutions for biomedical signal recognition. [28][29][30] The ANN behavior mostly depends on weights and transfer function.…”

Section: Introductionmentioning

confidence: 99%

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Toward optoacoustic sciatic nerve detection using an all‐fiber interferometric‐based sensor for endoscopic smart laser surgery

et al. 2021

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Objectives Laser surgery requires efficient tissue classification to reduce the probability of undesirable or unwanted tissue damage. This study aimed to investigate acoustic shock waves (ASWs) as a means of classifying sciatic nerve tissue. Materials and Methods In this study, we classified sciatic nerve tissue against other tissue types—hard bone, soft bone, fat, muscle, and skin extracted from two proximal and distal fresh porcine femurs—using the ASWs generated by a laser during ablation. A nanosecond frequency‐doubled Nd:YAG laser at 532 nm was used to create 10 craters on each tissue type's surface. We used a fiber‐coupled Fabry–Pérot sensor to measure the ASWs. The spectrum's amplitude from each ASW frequency band measured was used as input for principal component analysis (PCA). PCA was combined with an artificial neural network to classify the tissue types. A confusion matrix and receiver operating characteristic (ROC) analysis was used to calculate the accuracy of the testing‐data‐based scores from the sciatic nerve and the area under the ROC curve (AUC) with a 95% confidence‐level interval. Results Based on the confusion matrix and ROC analysis of the model's tissue classification results (leave‐one‐out cross‐validation), nerve tissue could be classified with an average accuracy rate and AUC result of 95.78 ± 1.3% and 99.58 ± 0.6%, respectively. Conclusion This study demonstrates the potential of using ASWs for remote classification of nerve and other tissue types. The technique can serve as the basis of a feedback control system to detect and preserve sciatic nerves in endoscopic laser surgery.

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Section: Discussionmentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%