Introduction
Laser surgery provides several advantages. Lasers allow cutting biological tissue with high precision and minimal trauma. Furthermore, the ability to work remotely allows a high level of sterility [
1‐
3]. However, these advantages come along with a lack of feedback: the surgeon does not receive sufficient information about the penetration depth of the laser cut or the type of tissue being ablated at the bottom of the laser cut. Hence, there is a risk of iatrogenic damage or the destruction of anatomical structures such as peripheral nerves [
4‐
6].
Oral and maxillofacial surgery in particular has to deal with complex anatomy in the head and neck region, including major sensory and motor nerves. Damaging those can immensely affect function and aesthetics.
Two types of oral and maxillofacial surgeries are known for having an inherent risk of iatrogenic nerve damage, also in the case of conventional surgery techniques: First of all, removing the parotid gland can be accompanied by a damage of the facial nerve in 10 to 50% of cases. This leads to a temporary or permanent ipsilateral facial paralysis with an insufficient closure of eyelid and mouth [
7,
8]. Due to the fact that the branches of the facial nerve run directly through the parotid gland and both tissue types look very much alike, it is not easy for the surgeon to reliably differentiate the nerve from the gland. One opportunity for nerve identification is electrical stimulation. However, iatrogenic nerve damage could not even be significantly reduced by using advanced techniques of intra-operative neuromonitoring [
9]. Secondly, orthognathic surgery performs a sagittal split through the lower jaw to correct the dental occlusion and the position of the mandible. The surgery can cause lesions to the lower alveolar nerve in 13 to 83% of the cases, with temporary or permanent numbness of the equilateral half of the lower lip and chin [
10], which can hamper ingestion and speech production: The lower alveolar nerve runs through the mandible hidden in a bony canal. Consequently, cutting the bone has to be performed using specialized surgical techniques without a direct view on the nerve.
To benefit from laser cutting in oral and maxillofacial surgery and to simultaneously reduce iatrogenic nerve damage in the facial region, additional means are required that will automatically control the laser ablation through intra-operative nerve detection. Concerning the two surgeries mentioned above, there is to differentiate between nerves and salivary gland tissue as well as between nerves and bone. Several approaches for tissue specific laser ablation control using optical feedback systems have been described [
11‐
13]. The basic idea is to regulate the laser ablation using optical tissue differentiation and to stop the laser cut when it reaches the vicinity of nerve tissue.
Diffuse Reflectance Spectroscopy (DRS) provides a relatively simple and cost-effective approach for tissue differentiation. The light applied is absorbed or scattered, depending on the optical properties of each tissue type. In the visible range, the main tissue absorbers are melanin and hemoglobin [
14], and the main tissue scatterers are cell organelles (such as mitochondria, etc.) and cells [
14]. Several types of normal healthy tissues from animals and humans have been described in terms of their optical properties by means of diffuse reflectance spectra
ex vivo[
15‐
17] and
in vivo[
18]. However, there is little information about the differentiation between different types of healthy tissue so far. Recently, the general feasability of optical tissue differentiation by a remote diffuse reflectance spectroscopy set up could be shown by our work group [
19].
The goal of this study was to apply this diffuse reflectance spectroscopy technique on the differentiation between nerves and salivary gland tissue as well as between nerves and hard tissue. The differentiation between these tissue types is challenging due to their bioptic similarity. Also, it is highly relevant concerning the clinical application in the facial region, evolving the mentioned prior research and expanding it towards a clinical problem solution. The experiments deliver a basis for developing a remote optical nerve detection to control the surgical laser cut, with the intent of nerve preservation in oral and maxillofacial surgery.
Discussion
For feedback controlled laser surgery, tissue differentiation is a crucial step. Especially in oral and maxillofacial surgery, the identification of major peripheral nerves is essential to avoid iatrogenic damage to these anatomical structures. Laser light may destroy neural structures through direct ablation or overheating due to laser cutting of adjacent tissue. Both can lead to reduced or missing nerve function [
4‐
6].
In order to create the basis for optical nerve identification, we extracted diffuse reflectance spectra of 4 different types of tissue, i.e., nerve tissue, salivary gland tissue, cortical bone and cancellous bone from
ex vivo pig samples. The selection of tissue types followed a clinical approach. Chosen were the tissue types which can be found on two typical oral and maxillofacial operations with a high risk of iatrogenic nerve damage: surgeries on the parotid gland jeopardize the facial nerve; orthognathic surgery on the lower jaw jeopardizes the lower alveolar nerve. To keep the experiments straightforward, only tissues in direct anatomical contact with the particular nerves were chosen for optical tissue differentiation. Five branches of the facial nerve go directly through the parotid gland and are fully surrounded by salivary gland tissue in the pre-auricular region [
25]. The lower alveolar nerve runs through a mandibular canal and is directly surrounded by a bony canal. It is therefore surrounded by bone structure consisting of thin cortical bone. In rare cases of reduced or missing cortical bone the nerve may be directly surrounded by cancellous bone [
26].
As the averaged spectra of the four tissues turned out not to be too distinct, advanced methods of analysis were used to differentiate the spectral curves. Analyzing the principal components of the spectra, we found 5 PCs that contributed significantly to the differentiation of the four types of tissue. These PCs were consistently chosen in all cross validation steps. PC 1 is responsible for more than 85% of the variance of the diffuse reflectance spectra derived from the four tissue types. For PC 1, the results of the PCA demonstrated a consistent contribution along the investigated wavelength range, without any remarkable peaks. PC 2 and PC 4 demonstrated prominent peaks at 410, 540 and 580 nm, which are meant to be related to the peaks of oxyhemoglobin and deoxyhemoglobin (Figure
4). Consequently, it is assumed that PC 1 provided information about the absorption/scattering contribution other than blood. This means that PC 1 can basically represent the bio-morphological variety of the tissues, such as size and number of cells and cell nuclei, cell organelles (e.g. mitochondria) and the amount and density of the extracellular matrix (ECM) including collagen. All of these are known to contribute to overall amounts of diffuse reflectance apart from blood [
27‐
29]. The shape of the curve of PC 2 and PC 4 is similar to the spectral shape of blood, reflecting the contribution of blood absorption, reflection and backscattering in the visible range [
30]. However, compared to PC 1, PC 2 and PC 4 are commonly responsible for only 9% of the variance between the reflectance spectra of different types of tissue.
Regarding the AUC results of the tissue differentiation, nerve tissue could be identified with a probability of 88.8% to 100%. The best result could be determined between salivary gland tissue and cancellous bone. However, it was not the major goal of this study to differentiate between these tissue types. The differentiation between nerves and cancellous bone reached high values of 98.8%. The lowest value was found for the differentiation between nerves and cortical bone (88.8%). An exclusively pairwise differentiation of tissue types may have yielded even better results. However, a pairwise differentiation of known tissue types does not meet the requirements of a clinical application with potential inter-individual variations of anatomy. Therefore, we chose a more complex mathematical approach implicating a multiclass analysis.
A high rate of correct tissue differentiation is only one part of nerve identification and preservation. The crucial step prior to a transfer of the technique to a clinical application may be the sensitivity of tissue differentiation. In this study, the sensitivity of nerve differentiation was found to be rather high with values ranging between 83.5% - 100%. The lowest result was achieved for the differentiation between cortical bone and salivary gland. However, the differentiation of that tissue pair was not a major goal of this study which focused on nerve tissue according to the clinical approach. A high sensitivity with over 90% could be demonstrated for the differentiation between nerves and cancellous bone as well as between nerves and salivary gland tissue. The differentiation of both tissue pairs is of high relevance considering clinical conditions. A similarly high result was achieved for the tissue pair nerves and cancellous bone yielding a specificity of tissue differentiation of 100%. The specificity of tissue differentiation between nerves and salivary gland tissue demonstrated only 84%, between nerves and cortical bone only 78%. However, a reduced specificity may be tolerated in favor of a high sensitivity if the aim is a precise and reliable preservation of major nerves. Additionally, a specificity of more than 70% may still allow for an uncomplicated performance in a clinical set-up.
Although it was not the goal of this study to investigate the differing optical properties of tissues, the differences of optical spectra may be explained by some considerations based on the similarity or the diversity of anatomical and biochemical structures: The single nerve fiber of peripheral nerves, like the infra-orbital nerve, is surrounded by a myelin sheath that contains 75% lipids (25% cholesterol, 20% galactocerebroside, 5% galactosulfatide, 50% phospholipids). Salivary gland tissue of humans and other mammals consists of epithelial cells, fibrous connective tissue and a high percentage of fat cells with 25% of volume on average [
31]. The percentage of fat increases with age and can reach up to 60% of volume [
32]. Lipocytes, the major cell population of fat tissue, predominantly consist of lipids. The compounds are triglycerides, cholesterol and other fatty acids [
33]. It is assumed that the high proportion of lipids of both tissue types - salivary gland and the myelin sheath of peripheral nerves - leads to a similarity of optical properties, followed by a reduction of optical contrast of the derived diffuse reflectance spectra.
Bone tissue consists of 65% inorganic elements (calcium phosphate compounds, mainly hydroxyapatite [CA
10 (PO
4)
6 (OH)
2]) and 35% organic elements (collagen fibers, water, proteins). Cancellous bone demonstrates a porosity with an average of 80%, due to the intertrabecular marrow spaces [
34]. Only 20% of the cancellous bone volume is built of bone tissue forming an osseous scaffold. The interspace of the osseous scaffold is filled with bone marrow. In adolescent beings, like the animals used in this study, the bone marrow is involved in the hematopoesis. Hence, it is highly vascular and mainly consists of hematopoietic cells and erythrocytes [
35]. Therefore, the main optical properties of the cancellous bone are assumed to be constituted by hemoglobin and the hematopoietic cells. This may even be the case under
ex vivo conditions, due to the fact that blood cells are fixed in the osseous scaffold of the cancellous bone. The fixation prevents the cells from descending to deeper tissue layers through gravity after circulatory arrest. After adolescence, aging is followed by a reduction of hematopoietic cells replaced by fat cells - indicating the transition from red to yellow bone marrow. Yellow bone marrow can contain an amount of fat cells of up to 80% [
36]. That has to be kept in mind concerning a transition of the results to clinical conditions involving mature human beings.
Cortical bone demonstrates a very dense and homogeneous structure with a porosity of only 3.5% on average [
34]. Therefore, the main optical properties of cortical bone are assumed to be constituted by the inorganic elements calcium and phosphate. In contrast, the reduced tissue differentiation between nerves and cortical bone does not reflect the biological diversity of the two tissue types. One possible explanation may be the reduced blood content of both tissues under
ex vivo conditions, considering the fact that blood is known to be one major optical absorber and reflector of biological tissue [
37]. Different types of tissue demonstrate different degrees of blood flow under
in vivo conditions, which may considerably change the diffuse reflectance spectra [
38]. In the presence of microcirculation, the blood content of the myelin sheath is different from the blood content of a salivary gland as well as of bone tissue. We expect that the discrimination algorithm based on diffuse reflectance spectra will work more reliably
in vivo[
29,
39]. However, the findings have to be evaluated in further
in vivo experiments.
For tissue differentiation a spectral range of 350-650 nm was used. Diffuse reflectance spectra over 650 nm showed high noise. Hence, the infrared spectral region was excluded in this study. The high noise may be a result of the presence of a large number of emission lines in the Xenon-lamp emission spectra or of the relatively low intensity of our light source. Other light sources, e.g., the tungsten halogen lamp, are known for a smooth emission profile showing favorable results for diffuse reflectance spectroscopy measurements [
40,
41]. On the other hand, using the Xenon light source yielded decent differentiation results providing sufficient intensity around the 410 nm peak - a wavelength which turned out to be of value for the differentiation of the tissue types investigated in this study. The results might be different considering the influence of blood. Hence, further research is necessary to investigate if the chosen set up is sufficient for the differentiation of biological tissue under
in vivo conditions.
The results of this study were obtained using separated tissue samples containing only one type of tissue. One major challenge will be transferring the set up to a compound tissue sample which contains nerve tissue as well as other tissue types. Associated with this challenge is the penetration depth of the applied light, inheriting the possibility of optical spectra derived from several types of tissues situated together in the interrogation depth. Further research is necessary to establish a model simulation of the optical pathways to analyze nerve identification in biological tissue compounds.
In this study, we investigated tissue samples from domestic pigs. Extrapolating the results to human tissue, interspecies differences of optical tissue properties have to be considered. Additionally, there may be an alteration of optical tissue properties due to post mortem changes. It is known that de-oxygenation and a loss of hemoglobin are the main factors for a continuing decrease of absorption in the visible wavelength range during the first 24 hours post mortem [
42]. Even if this process slows down after the first 24 hours, a further decrease of absorption may occur. To take the continuing post mortem changes of absorption decrease at the hemoglobin peaks into account, we kept the
ex vivo time for tissue preparation and measurements as short as possible and, with 6 hours, equal for all the tissue types investigated in this study. However, post mortem changes may have altered the optical properties of the tissue samples, influencing the diffuse reflectance spectra.
A remote set-up was utilized for the measurements, to take two factors into account. Light delivery or measurement tools that are in direct contact with biological tissue may cause an alteration of optical properties due to a mechanical manipulation. The applied pressure on the tissue causes increased tissue absorption and scattering coefficients [
43], which may alter the results of optical tissue differentiation. In addition, considering the clinical application of optical tissue differentiation, it has to be kept in mind that mechanical manipulation of the tissue may cause the spreading of germs or tumor cells during surgery [
44,
45]. Hence, focusing on a non-contact set-up, the environmental light has to be excluded as it may interfere with the optical spectra derived from the tissue. Executing the optical measurements in absolute darkness is a well known possibility [
29,
46], but does not meet the requirements for an uncomplicated clinical application. Hence, the diffuse reflectance spectra were mathematically corrected for environmental stray light to increase the signal to noise ratio during the measurements [
47].
For a sufficient implementation of this tissue differentiation technique in a closed loop system to control laser ablation in surgery, the computational time required for analyzing the reflectance spectra is essential. A first basic feedback system was established with an acoustic sensor by our workgroup showing the general feasibility of a real-time sensor based control of laser surgery. The system was limited to the differentiation of two bone qualities [
13,
48]. Considering an expansion of the system towards a general applicability, it will be necessary to analyze several tissue types in a very short period of time which may be a mathematical and computational challenge. However, the time required for tissue differentiation was not the objective of our study. Before transferring the results of this study to a control system, this issue has to be investigated on further research.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
FS, AZ and KTG carried out the tissue preparation as well as the optical measurements. AZ, AD and KTG installed and adapted the optical set-up. WA participated in the design of the study and performed the statistical analysis. FS, AD, EN and MS performed the data analysis and assessment. FS and MS conceived of the study, participated in design and coordination and drafted the manuscript. All authors read and approved the final manuscript.