Diffuse reflectance spectroscopy-enhanced drill for bone boundary detection

A handheld fiber-optic tissue sensing device for spine surgery

The demographic shift has increased the demand for surgical interventions to address age-related degenerative diseases, such as spinal fusion. Accurate placement of pedicle screws, crucial for successful spinal fusion, varies widely with physician experience. Integrating tissue sensing into spine surgical instruments allows intraoperative examination of tissue properties, providing surgeons with additional information to prevent screw misplacement. This paper introduces a handheld fiber-optic tissue sensing device for real-time bone tissue differentiation during spine surgery using Diffuse Reflectance Spectroscopy (DRS). Our prototype employs laser diodes at two distinct wavelengths for tissue illumination, eliminating the need for a spectrometer and enabling direct light collection with a photodiode. The device includes a printed circuit board (PCB) with driver circuits that are adjustable for varying laser diode output power, and signal amplification to convert the photodiode current to a measurable voltage signal. Controlled by a microcontroller, the device computes a reflectance ratio from both laser diode signals to provide real-time audio feedback to surgeons across various healthcare settings. Despite challenges in coupling efficiencies from manual fiber-coupling of the diodes, our prototype is able to emit and collect light to distinguish bone tissues with DRS, demonstrating feasibility. It is compact, made of low-cost and readily available components, and offers fast, real-time feedback, thus serving as a successful proof-of-concept for enhancing surgical accuracy during spinal fusion procedures.

Extended-wavelength diffuse reflectance spectroscopy dataset of animal tissues for bone-related biomedical applications

Diffuse reflectance spectroscopy (DRS) has been extensively studied in both preclinical and clinical settings for multiple applications, notably as a minimally invasive diagnostic tool for tissue identification and disease delineation. In this study, extended-wavelength DRS (EWDRS) measurements of ex vivo tissues ranging from ultraviolet through visible to the short-wave infrared region (355-1919 nm) are presented in two datasets. The first dataset contains labelled EWDRS measurements collected from bone cement samples and ovine specimens including 10 tissue types commonly encountered in orthopedic surgeries for data curation purposes. The other dataset includes labelled EWDRS measurements of primarily bone structures at different depths during stepwise drilling into intact porcine skulls until plunging into the cranial cavity. The raw data with code for pre-processing and calibration is publicly available for reuse on figshare. The datasets can be utilized not only for exploratory purposes in machine learning model construction, but also for knowledge discovery in the orthopedic domain to identify important features for surgical guidance, extract physiological parameters and provide diagnostic insights.

Frameworks of wavelength selection in diffuse reflectance spectroscopy for tissue differentiation in orthopedic surgery

Significance

Wavelength selection from a large diffuse reflectance spectroscopy (DRS) dataset enables removal of spectral multicollinearity and thus leads to improved understanding of the feature domain. Feature selection (FS) frameworks are essential to discover the optimal wavelengths for tissue differentiation in DRS-based measurements, which can facilitate the development of compact multispectral optical systems with suitable illumination wavelengths for clinical translation.

Aim

The aim was to develop an FS methodology to determine wavelengths with optimal discriminative power for orthopedic applications, while providing the frameworks for adaptation to other clinical scenarios.

Approach

An ensemble framework for FS was developed, validated, and compared with frameworks incorporating conventional algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), and backward interval partial least squares (biPLS).

Results

Via the one-versus-rest binary classification approach, a feature subset of 10 wavelengths was selected from each framework yielding comparable balanced accuracy scores (PCA: 94.8 ± 3.47 % , LDA: 98.2 ± 2.02 % , biPLS: 95.8 ± 3.04 % , and ensemble: 95.8 ± 3.16 % ) to those of using all features (100%) for cortical bone versus the rest class labels. One hundred percent balanced accuracy scores were generated for bone cement versus the rest. Different feature subsets achieving similar outcomes could be identified due to spectral multicollinearity.

Conclusions

Wavelength selection frameworks provide a means to explore domain knowledge and discover important contributors to classification in spectroscopy. The ensemble framework generated a model with improved interpretability and preserved physical interpretation, which serves as the basis to determine illumination wavelengths in optical instrumentation design.

Diffuse reflectance spectroscopy of the spine: improved breach detection with angulated fibers

Accuracy in spinal fusion varies greatly depending on the experience of the physician. Real-time tissue feedback with diffuse reflectance spectroscopy has been shown to provide cortical breach detection using a conventional probe with two parallel fibers. In this study, Monte Carlo simulations and optical phantom experiments were conducted to investigate how angulation of the emitting fiber affects the probed volume to allow for the detection of acute breaches. Difference in intensity magnitude between cancellous and cortical spectra increased with the fiber angle, suggesting that outward angulated fibers are beneficial in acute breach scenarios. Proximity to the cortical bone could be detected best with fibers angulated at θ f = 45 ∘ for impending breaches between θ p = 0 ∘ and θ p = 45 ∘ . An orthopedic surgical device comprising a third fiber perpendicular to the device axis could thus cover the full impending breach range from θ p = 0 ∘ to θ p = 90 ∘ .

Perspective on the integration of optical sensing into orthopedic surgical devices

SIGNIFICANCE

Orthopedic surgery currently comprises over 1.5 million cases annually in the United States alone and is growing rapidly with aging populations. Emerging optical sensing techniques promise fewer side effects with new, more effective approaches aimed at improving patient outcomes following orthopedic surgery.

AIM

The aim of this perspective paper is to outline potential applications where fiberoptic-based approaches can complement ongoing development of minimally invasive surgical procedures for use in orthopedic applications.

APPROACH

Several procedures involving orthopedic and spinal surgery, along with the clinical challenge associated with each, are considered. The current and potential applications of optical sensing within these procedures are discussed and future opportunities, challenges, and competing technologies are presented for each surgical application.

RESULTS

Strong research efforts involving sensor miniaturization and integration of optics into existing surgical devices, including K-wires and cranial perforators, provided the impetus for this perspective analysis. These advances have made it possible to envision a next-generation set of devices that can be rigorously evaluated in controlled clinical trials to become routine tools for orthopedic surgery.

CONCLUSIONS

Integration of optical devices into surgical drills and burrs to discern bone/tissue interfaces could be used to reduce complication rates across a spectrum of orthopedic surgery procedures or to aid less-experienced surgeons in complex techniques, such as laminoplasty or osteotomy. These developments present both opportunities and challenges for the biomedical optics community.

Power-Tool Use in Orthopaedic Surgery: Iatrogenic Injury, Its Detection, and Technological Advances: A Systematic Review

Power tools are an integral part of orthopaedic surgery but have the capacity to cause iatrogenic injury. With this systematic review, we aimed to investigate the prevalence of iatrogenic injury due to the use of power tools in orthopaedic surgery and to discuss the current methods that can be used to reduce injury.

Wavelength weightings in machine learning for ovine joint tissue differentiation using diffuse reflectance spectroscopy (DRS)

Objective: To investigate the DRS of ovine joint tissue to determine the optimal optical wavelengths for tissue differentiation and relate these wavelengths to the biomolecular composition of tissues. In this study, we combine machine learning with DRS for tissue classification and then look further at the weighting matrix of the classifier to further understand the key differentiating features. Methods: Supervised machine learning was used to analyse DRS data. After normalising the data, dimension reduction was achieved through multiclass Fisher's linear discriminant analysis (Multiclass FLDA) and classified with linear discriminant analysis (LDA). The classifier was first run with all the tissue types and the wavelength range 190 nm - 1081 nm. We analysed the weighting matrix of the classifier and then ran the classifier again, the first time using the ten highest weighted wavelengths and the second using only the single highest. Our method was applied to a dataset containing ovine joint tissue including cartilage, cortical and subchondral bone, fat, ligament, meniscus, and muscle. Results: It achieved a classification accuracy of 100% using the wavelength 190 nm - 1081 nm (2048 attributes) with an accuracy of 90% being present for 10 attributes with the exception of those with comparable compositions such as ligament and meniscus. An accuracy greater than 70% was achieved using a single wavelength, with the same exceptions. Conclusion: Multiclass FLDA combined with LDA is a viable technique for tissue identification from DRS data. The majority of differentiating features existed within the wavelength ranges 370-470 and 800-1010 nm. Focusing on key spectral regions means that a spectrometer with a narrower range can potentially be used, with less computational power needed for subsequent analysis.

Cranial Perforation Using an Optically-Enhanced Surgical Drill

The design of mechanically clutched cranial perforators, used in craniotomy procedures, limits their performance under certain clinical conditions and can, in some cases, impose the risk of severe brain injury on patients undergoing the procedure. An additional safety mechanism could help in mitigating these risks. In this work, we examine the use of diffuse reflectance spectroscopy as a potential fallback mechanism for near real-time detection of the bone-brain boundary. Monte Carlo simulation of a two layer model with optical properties of bone and brain at 530 and 850 nm resulted in a detectable change in diffuse reflectance signal when approaching the boundary. The simulated results were used to guide the development of an experimental drill control system, which was tested on 10 sheep craniums and yielded 88.1 % success rate in the detection of the approaching bone-brain boundary.