Orthopaedic surgeon attitudes towards current limitations and the potential for robotic and technological innovation in arthroscopic surgery

The Relative Importance of Factors That Applicants Weigh When Ranking Adult Reconstruction Fellowships as Well as Their Perspectives on Robotic-Assisted Arthroplasty

BACKGROUND

Orthopedic Surgery Fellowship programs offer highly specialized training that varies based on the training environment and surgical experience. Additionally, for Adult Reconstruction programs, robotic-assisted surgery exposure has been a widely discussed topic. The purpose of this study was to determine the relative value of various factors to Adult Hip and Knee Fellowship applicants, and their perceptions of robotic-assisted arthroplasty.

METHODS

We surveyed 780 applicants who applied to our fellowship to matriculate in 2020 to 2024. We received 158 responses (20.3% response rate). We assessed factors concerning people and perceptions, logistics, salary and benefits, program reputation and curriculum, and surgical experience. Additionally, we surveyed fellows' attitudes toward using robotic surgery and its impact on patient outcomes.

RESULTS

The highest-rated factors were Level of Hands-On Operative Experience (4.83), Revision Hip Volume (4.72), Revision Knee Volume (4.71), Multiple Surgical Exposures to the Hip (4.59), and Clinical Case Variety (4.59). Respondents who were postfellowship matriculation placed significantly more value on Exposure to Multiple Attendings with Surgical Diversity (P = .01), and Anterior Hip Volume (P = .04), and less value on Geographic Location (P = .04) and Patient-Specific Instrumentation (P = .02) than prematriculates. Overall, 65% of applicants plan to or currently use robotics, 7.6% do not, and 27.2% said "Maybe". Those who plan to or currently use robotics most cited procedure fidelity, patient-preference, and marketability as reasons to use robotics.

CONCLUSIONS

Hands-on surgical experience and revision volume were the most important factors for fellowship applicants. Applicants placed lower importance on robotics exposure and their perspectives on robotics in their future practice were highly variable. Our results will inform fellowship programs and future applicants what previous applicants have valued in their training to help guide fellowship program structure, resource management, as well as recruitment.

Theory and evidence-base for a digital platform for the delivery of language tests during awake craniotomy and collaborative brain mapping

Objectives

Build the theoretical and evidence-base for a digital platform (map-OR) which delivers intraoperative language tests during awake craniotomy and facilitates collaborative sharing of brain mapping data.

Design

Mixed methodology study including two scoping reviews, international survey, synthesis of development guiding principles and a risk assessment using failure modes and effects analysis.

Setting

The two scoping reviews examined the literature published in the English language. International survey was completed by members of awake craniotomy teams from 14 countries.

Main outcome measures

Scoping review 1: number of technologies described for language mapping during awake craniotomy. Scoping review 2: barriers and facilitators to adopting novel technology in surgery. International survey: degree of language mapping technology penetration into clinical practice.

Results

A total of 12 research articles describing 6 technologies were included. The technologies required a range of hardware components including portable devices, virtual reality headsets and large integrated multiscreen stacks. The facilitators and barriers of technology adoption in surgery were extracted from 11 studies and mapped onto the 4 Unified Theory of Acceptance and Use of Technology constructs. A total of 37 awake craniotomy teams from 14 countries completed the survey. Of the responses, 20 (54.1%) delivered their language tests digitally, 10 (27.0%) delivered tests using cards and 7 (18.9%) used a combination of both. The most commonly used devices were tablet computers (67.7%; n=21) and the most common software used was Microsoft PowerPoint (60.6%; n=20). Four key risks for the proposed digital platform were identified, the highest risk being a software and internet connectivity failure during surgery.

Conclusions

This work represents a rigorous and structured approach to the development of a digital platform for standardized intraoperative language testing during awake craniotomy and for collaborative sharing of brain mapping data.

Trial registration number

Scoping review protocol registrations in OSF registries (scoping review 1: osf.io/su9xm; scoping review 2: osf.io/x4wsc).

Vibro-acoustic sensing of tissue-instrument-interactions allows a differentiation of biological tissue in computerised palpation

BACKGROUND

The shift towards minimally invasive surgery is associated with a significant reduction of tactile information available to the surgeon, with compensation strategies ranging from vision-based techniques to the integration of sensing concepts into surgical instruments. Tactile information is vital for palpation tasks such as the differentiation of tissues or the characterisation of surfaces. This work investigates a new sensing approach to derive palpation-related information from vibration signals originating from instrument-tissue-interactions.

METHODS

We conducted a feasibility study to differentiate three non-animal and three animal tissue specimens based on palpation of the surface. A sensor configuration was mounted at the proximal end of a standard instrument opposite the tissue-interaction point. Vibro-acoustic signals of 1680 palpation events were acquired, and the time-varying spectrum was computed using Continuous-Wavelet-Transformation. For validation, nine spectral energy-related features were calculated for a subsequent classification using linear Support Vector Machine and k-Nearest-Neighbor.

RESULTS

Indicators derived from the vibration signal are highly stable in a set of palpations belonging to the same tissue specimen, regardless of the palpating subject. Differences in the surface texture of the tissue specimens reflect in those indicators and can serve as a basis for differentiation. The classification following a supervised learning approach shows an accuracy of >93.8% for the three-tissue classification tasks and decreases to 78.8% for a combination of all six tissues.

CONCLUSIONS

Simple features derived from the vibro-acoustic signals facilitate the differentiation between biological tissues, showing the potential of the presented approach to provide information related to the interacting tissue. The results encourage further investigation of a yet little-exploited source of information in minimally invasive surgery.

Vibro-Acoustic Sensing of Instrument Interactions as a Potential Source of Texture-Related Information in Robotic Palpation

The direct tactile assessment of surface textures during palpation is an essential component of open surgery that is impeded in minimally invasive and robot-assisted surgery. When indirectly palpating with a surgical instrument, the structural vibrations from this interaction contain tactile information that can be extracted and analysed. This study investigates the influence of the parameters contact angle α and velocity v→ on the vibro-acoustic signals from this indirect palpation. A 7-DOF robotic arm, a standard surgical instrument, and a vibration measurement system were used to palpate three different materials with varying α and v→. The signals were processed based on continuous wavelet transformation. They showed material-specific signatures in the time-frequency domain that retained their general characteristic for varying α and v→. Energy-related and statistical features were extracted, and supervised classification was performed, where the testing data comprised only signals acquired with different palpation parameters than for training data. The classifiers support vector machine and k-nearest neighbours provided 99.67% and 96.00% accuracy for the differentiation of the materials. The results indicate the robustness of the features against variations in the palpation parameters. This is a prerequisite for an application in minimally invasive surgery but needs to be confirmed in realistic experiments with biological tissues.

Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy

Registration of partial view intra-operative ultrasound (US) to pre-operative MRI is an essential step in image-guided minimally invasive surgery. In this paper, we present an automatic, landmark-free 3D multimodal registration of pre-operative MRI to 4D US (high-refresh-rate 3D-US) for enabling guidance in knee arthroscopy. We focus on the problem of initializing registration in the case of partial views. The proposed method utilizes a pre-initialization step of using the automatically segmented structures from both modalities to achieve a global geometric initialization. This is followed by computing distance maps of the procured segmentations for registration in the distance space. Following that, the final local refinement between the MRI-US volumes is achieved using the LC2 (Linear correlation of linear combination) metric. The method is evaluated on 11 cases spanning six subjects, with four levels of knee flexion. A best-case error of 1.41 mm and 2.34∘ and an average registration error of 3.45 mm and 7.76∘ is achieved in translation and rotation, respectively. An inter-observer variability study is performed, and a mean difference of 4.41 mm and 7.77∘ is reported. The errors obtained through the developed registration algorithm and inter-observer difference values are found to be comparable. We have shown that the proposed algorithm is simple, robust and allows for the automatic global registration of 3D US and MRI that can enable US based image guidance in minimally invasive procedures.

3D CAIPIRINHA SPACE versus standard 2D TSE for routine knee MRI: a large-scale interchangeability study

OBJECTIVE

To perform a large-scale interchangeability study comparing 3D controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) sampling perfection with application optimized contrast using different flip angle evolutions (SPACE) TSE with standard 2D TSE for knee MRI.

METHODS

In this prospective study, 250 patients underwent 3 T knee MRI, including a multicontrast 3D CAIPIRINHA SPACE TSE (9:26 min) and a standard 2D TSE protocol (12:14 min). Thirty-three (13%) patients had previous anterior cruciate ligament and/or meniscus surgery. Two radiologists assessed MRIs for image quality and identified pathologies of menisci, ligaments, and cartilage by using a 4-point Likert scale according to the level of diagnostic confidence. Interchangeability of the protocols was tested under the same-reader scenario using a bootstrap percentile confidence interval. Interreader reliability and intermethod concordance were also evaluated.

RESULTS

Despite higher image quality and diagnostic confidence for standard 2D TSE compared to 3D CAIPIRINHA SPACE TSE, the protocols were found interchangeable for diagnosing knee abnormalities, except for patellar (6.8% difference; 95% CI: 4.0, 9.6) and trochlear (3.6% difference; 95% CI: 0.8, 6.6) cartilage defects. The interreader reliability was substantial to almost perfect for 2D and 3D MRI (range κ, 0.785-1 and κ, 0.725-0.964, respectively). Intermethod concordance was almost perfect for all diagnoses (range κ, 0.817-0.986).

CONCLUSION

Multicontrast 3D CAIPIRINHA SPACE TSE and standard 2D TSE protocols perform interchangeably for diagnosing knee abnormalities, except for patellofemoral cartilage defects. Despite the radiologist's preference for 2D TSE imaging, a pursuit towards time-saving 3D TSE knee MRI is justified for routine practice.

KEY POINTS

• Multicontrast 3D CAIPIRINHA SPACE and standard 2D TSE protocols perform interchangeably for diagnosing knee abnormalities, except for patellofemoral cartilage defects. • Radiologists are more confident in diagnosing knee abnormalities on 2D TSE than on 3D CAIPIRINHA SPACE TSE MRI. • Despite the radiologist's preference for 2D TSE, a pursuit towards accelerated 3D TSE knee MRI is justified for routine practice.

The effects of gender, age, and videogame experience on performance and experiences with a surgical robotic arm: an exploratory study with general public

Robotic surgery is increasing in prevalence, thanks to its potential benefits for patients (e.g., reduced blood loss) and surgeons (e.g., ergonomics). It is important to know what inherent characteristics of potential surgeons may facilitate robotic surgery training and performance. Findings from previous studies indicate videogames can be inexpensive tools that help improve hand-eye coordination, coordination of 3-D movements with 2-D images, and spatial orientation. In the context of robotic-assisted knee arthroscopy using a MAKO robotic arm, this study explored performance and subjective experiences of novices (N = 104) with a fake bone shaving task at a public event. Participants' performance was measured based on how much of the bone they successfully shaved. Findings showed that duration of videogame play per week was negatively related to performance with the robotic arm. Male and female participants performed similarly on the bone shaving task, and reported similar difficulty with and enjoyment of the task. However, female participants who played videogames performed better than those who did not play videogames. Participants who were younger than 11 had the worst performance and the most difficulty with the robotic arm. Overall, the findings indicate that the effect of videogame experience on the performance with the robotic arm may differ based on gender and age. This has implications on the length of training for surgeons of different gender using videogames and other emerging technologies.

Visual Localisation for Knee Arthroscopy

PURPOSE  : Navigation in visually complex endoscopic environments requires an accurate and robust localisation system. This paper presents the single image deep learning based camera localisation method for orthopedic surgery. METHODS  : The approach combines image information, deep learning techniques and bone-tracking data to estimate camera poses relative to the bone-markers. We have collected one arthroscopic video sequence for four knee flexion angles, per synthetic phantom knee model and a cadaveric knee-joint. RESULTS  : Experimental results are shown for both a synthetic knee model and a cadaveric knee-joint with mean localisation errors of 9.66mm/0.85[Formula: see text] and 9.94mm/1.13[Formula: see text] achieved respectively. We have found no correlation between localisation errors achieved on synthetic and cadaveric images, and hence we predict that arthroscopic image artifacts play a minor role in camera pose estimation compared to constraints introduced by the presented setup. We have discovered that the images acquired for 90°and 0°knee flexion angles are respectively most and least informative for visual localisation. CONCLUSION  : The performed study shows deep learning performs well in visually challenging, feature-poor, knee arthroscopy environments, which suggests such techniques can bring further improvements to localisation in Minimally Invasive Surgery.

What Promotes Surgeon Practice Change? A Scoping Review of Innovation Adoption in Surgical Practice

OBJECTIVE

The goal of this scoping review was to summarize the literature on facilitators and barriers to surgical practice change. This information can inform research to implement best practices and evaluate new surgical innovations.

BACKGROUND

In an era of accelerated innovations, surgeons face the difficult decision to either acknowledge and implement or forgo new advances. Although changing surgical practice to align with evidence is an imperative of health systems, evidence-based guidelines have not translated into consistent change. The literature on practice change is limited and has largely focused on synthesizing information on methods and trials to evaluate innovative surgical interventions. No reviews to date have grounded their analysis within an implementation science framework.

METHODS

A systematic review of the literature on surgical practice change was performed. Abstracts and full-text articles were reviewed for relevance using inclusion and exclusion criteria and data were extracted from each article. Cited facilitators and barriers were then mapped across domains within the implementation science Theoretical Domains Framework and expanded to the Capability, Opportunity, Motivation, and Behavior model.

RESULTS

Components of the Capability, Opportunity, Motivation, and Behavior model were represented across the Theoretical Domains Framework domains and acted as both facilitators and barriers to practice change depending on the circumstances. Domains that most affected surgical practice change, in order, were: opportunity (environmental context and resources and social influences), capability (knowledge and skills), and motivation (beliefs about consequences and reinforcement).

CONCLUSIONS

Practice change is predicated on a conducive environment with adequate resources, but once that is established, the surgeon's individual characteristics, including skills, motivation, and reinforcement determine the likelihood of successful change. Deficiencies in the literature underscore the need for further study of resource interventions and the role of surgical team dynamics in the adoption of innovation. A better understanding of these areas is needed to optimize our ability to disseminate and implement best practices in surgery.

Eyes in Ears: A Miniature Steerable Digital Endoscope for Trans-Nasal Diagnosis of Middle Ear Disease

The aim of this work is to design, fabricate and experimentally validate a miniature steerable digital endoscope that can provide comprehensive, high-resolution imaging of the middle ear using a trans-nasal approach. The motivation for this work comes from the high incidence of middle ear diseases, and the current reliance on invasive surgery to diagnose and survey these diseases which typically consists of the eardrum being lifted surgically to directly visualize the middle ear using a trans-canal approach. To enable less-invasive diagnosis and surveillance of middle ear disease, we propose an endoscope that is small enough to pass into the middle ear through the Eustachian tube, with a steerable tip that carries a 1 Megapixel image sensor and fiber-optic illumination to provide high-resolution visualization of critical middle ear structures. The proposed endoscope would enable physicians to diagnose middle ear disease using a non-surgical trans-nasal approach instead, enabling such procedures to be performed in an office setting and greatly reducing invasiveness for the patient. In this work, the computational design of the steerable tip based on computed tomography models of real human middle ear anatomy is presented, and these results informed the fabrication of a clinical-scale steerable endoscope prototype. The prototype was used in a pilot study in three cadaveric temporal bone specimens, where high-quality middle ear visualization was achieved as determined by an unbiased cohort of otolaryngologists. This is the first paper to demonstrate cadaveric validation of a digital, steerable, clinical-scale endoscope for middle ear disease diagnosis, and the experimental results illustrate that the endoscope enables the visualization of critical middle ear structures (such as the epitympanum or sinus tympani) that were seldom or never visualized in prior published trans-Eustachian tube endoscopy feasibility studies.

Deep Learning for US Image Quality Assessment Based on Femoral Cartilage Boundary Detection in Autonomous Knee Arthroscopy

Knee arthroscopy is a complex minimally invasive surgery that can cause unintended injuries to femoral cartilage or postoperative complications, or both. Autonomous robotic systems using real-time volumetric ultrasound (US) imaging guidance hold potential for reducing significantly these issues and for improving patient outcomes. To enable the robotic system to navigate autonomously in the knee joint, the imaging system should provide the robot with a real-time comprehensive map of the surgical site. To this end, the first step is automatic image quality assessment, to ensure that the boundaries of the relevant knee structures are defined well enough to be detected, outlined, and then tracked. In this article, a recently developed one-class classifier deep learning algorithm was used to discriminate among the US images acquired in a simulated surgical scenario on which the femoral cartilage either could or could not be outlined. A total of 38 656 2-D US images were extracted from 151 3-D US volumes, collected from six volunteers, and were labeled as "1" or as "0" when an expert was or was not able to outline the cartilage on the image, respectively. The algorithm was evaluated using the expert labels as ground truth with a fivefold cross validation, where each fold was trained and tested on average with 15 640 and 6246 labeled images, respectively. The algorithm reached a mean accuracy of 78.4% ± 5.0, mean specificity of 72.5% ± 9.4, mean sensitivity of 82.8% ± 5.8, and mean area under the curve of 85% ± 4.4. In addition, interobserver and intraobserver tests involving two experts were performed on an image subset of 1536 2-D US images. Percent agreement values of 0.89 and 0.93 were achieved between two experts (i.e., interobserver) and by each expert (i.e., intraobserver), respectively. These results show the feasibility of the first essential step in the development of automatic US image acquisition and interpretation systems for autonomous robotic knee arthroscopy.

Damage control articular surgery: Maintaining chondrocyte health and minimising iatrogenic injury

Articular cartilage has limited intrinsic regenerative potential. The maintenance of healthy articular cartilage is essential to prevent joint degeneration and the morbidity associated with arthritis. In this review, we outline the structure and function of healthy articular cartilage. We summarise some of the recent literature outlining the influence of surgical factors on chondrocyte health. These factors include mechanical injury from instrumentation and drilling, drying, and the influence of irrigation fluids, antimicrobial solutions and local anaesthetics. We demonstrate that there is scope for improving cartilage viability at the time of surgery if simple chondroprotective measures are routinely adopted.

Deep Learning-Based Femoral Cartilage Automatic Segmentation in Ultrasound Imaging for Guidance in Robotic Knee Arthroscopy

Knee arthroscopy is a minimally invasive surgery used in the treatment of intra-articular knee pathology which may cause unintended damage to femoral cartilage. An ultrasound (US)-guided autonomous robotic platform for knee arthroscopy can be envisioned to minimise these risks and possibly to improve surgical outcomes. The first necessary tool for reliable guidance during robotic surgeries was an automatic segmentation algorithm to outline the regions at risk. In this work, we studied the feasibility of using a state-of-the-art deep neural network (UNet) to automatically segment femoral cartilage imaged with dynamic volumetric US (at the refresh rate of 1 Hz), under simulated surgical conditions. Six volunteers were scanned which resulted in the extraction of 18278 2-D US images from 35 dynamic 3-D US scans, and these were manually labelled. The UNet was evaluated using a five-fold cross-validation with an average of 15531 training and 3124 testing labelled images per fold. An intra-observer study was performed to assess intra-observer variability due to inherent US physical properties. To account for this variability, a novel metric concept named Dice coefficient with boundary uncertainty (DSC_UB) was proposed and used to test the algorithm. The algorithm performed comparably to an experienced orthopaedic surgeon, with DSC_UB of 0.87. The proposed UNet has the potential to localise femoral cartilage in robotic knee arthroscopy with clinical accuracy.

Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images

The tracking of the knee femoral condyle cartilage during ultrasound-guided minimally invasive procedures is important to avoid damaging this structure during such interventions. In this study, we propose a new deep learning method to track, accurately and efficiently, the femoral condyle cartilage in ultrasound sequences, which were acquired under several clinical conditions, mimicking realistic surgical setups. Our solution, that we name Siam-U-Net, requires minimal user initialization and combines a deep learning segmentation method with a siamese framework for tracking the cartilage in temporal and spatio-temporal sequences of 2D ultrasound images. Through extensive performance validation given by the Dice Similarity Coefficient, we demonstrate that our algorithm is able to track the femoral condyle cartilage with an accuracy which is comparable to experienced surgeons. It is additionally shown that the proposed method outperforms state-of-the-art segmentation models and trackers in the localization of the cartilage. We claim that the proposed solution has the potential for ultrasound guidance in minimally invasive knee procedures.

Iatrogenic cartilage injury associated with the use of stainless-steel cannulas and silicone-guarded cannulas for canine stifle arthroscopy

OBJECTIVE

To determine the ability to reduce iatrogenic cartilage injury (IACI) during canine stifle arthroscopy by using a silicone arthroscope cannula guard.

STUDY DESIGN

Ex vivo canine cadaver experimental study.

ANIMALS

Paired canine stifles from 14 cadavers (≥20 kg).

METHODS

Stifles (N = 28) were assigned to unguarded traditional or silicone-guarded arthroscopy. Stifle arthroscopy and full joint exploration with meniscal probing was performed by a second-year surgery resident (I.C.) in fourteen canine cadavers, alternating between left and right stifles for guarded vs unguarded arthroscopy. After arthroscopy, stifles were disarticulated, and india ink assay was performed to identify IACI. Total IACI number, lesion length and area, duration of procedure, and procedure difficulty score were recorded for each stifle.

RESULTS

Unguarded arthroscopy resulted in more total IACI per joint (unguarded 5.2 ± 3.0, guarded 2.4 ± 1.4; P = .02), larger IACI area (unguarded 5.2 ± 4.2 mm² , guarded 2.3 ± 1.5 mm² ; P = .02), and IACI length (unguarded 13.6 ± 6.9 mm, guarded 8.6 ± 5.9 mm; P = .03). No difference was identified in duration of procedure (unguarded 11.8 ± 5.2 minutes, guarded 13.8 ± 4.3 minutes; P = .79) or procedure difficulty score (unguarded 1.7 ± 0.6, guarded 1.6 ± 0.6 P = .73).

CONCLUSION

Silicone-guarded arthroscope cannulas decreased IACI number and size during canine cadaveric stifle arthroscopy without increasing duration of procedure or surgical difficulty.

CLINICAL SIGNIFICANCE

Silicone-guarded arthroscope cannulas may be safer than traditional cannulas for novice veterinary surgeons performing stifle arthroscopy.