Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

Individualised Modelling for Preoperative Planning of Total Knee Replacement Surgery

Total knee replacement (TKR) surgery is routinely prescribed for patients with severe knee osteoarthritis to alleviate the pain and restore the kinematics. Although this procedure was proven to be successful in reducing the joint pain, the number of failures and the low patients’ satisfaction suggest that while the number of reoperations is small, the surgery frequently fail to restore the function in full. The main cause are surgical techniques which inadequately address the problem of balancing the knee soft tissues. The preoperative planning technique allows to manufacture subject-specific cutting guides that improves the placement of the prosthesis, however the knee soft tissue is ignored. The objective of this dissertation was to create an optimized preplanning procedure to compute the soft tissue balance along with the placement of the prosthesis to ensure mechanical stability. The dissertation comprises the development of CT based static and quasi-static knee models able to estimate the postoperative length of the collateral lateral ligaments using a dataset of seven TKR patients; In addition, a subject-specific dynamic musculoskeletal model of the lower limb was created using in vivo knee contact forces to perform the same analysis during walking. The models were evaluated by their ability to predict the postoperative elongation using a threshold based on the 10 % of the preoperative length, through which the model detected whether an elongation was acceptable. The results showed that the subject-specific static model is the best solution to be included in the optimized, subject-specific, preoperative planning framework; full order musculoskeletal model allowed to estimate the postoperative length of the ligaments during walking, and at least in principle while performing any other activity. Unlike the current methodology used in clinic this optimized preoperative planning framework might help the surgeon to understand how the position of the TKR affects the knee soft tissue

CAOS & TKA. A critical appraisal on computer navigation in total knee arthroplasty

In mijn proefschrift heb ik onderzocht wat de invloed is van het gebruik van navigatie bij het plaatsen van een knieprothese. Hiervoor zijn drie onderzoeksvragen opgesteld en beantwoord. Allereerst: leidt CAOS tot het nauwkeuriger plaatsen van een TKP? Op basis van de door mij gedane studies en analyse van de huidige literatuur concludeer ik dat juiste registratie tijdens CAOS essentieel is voor het bereiken van een goede stand van de TKP. Zolang hier nog onnauwkeurigheden in zitten leidt CAOS (nog) niet tot het nauwkeuriger plaatsen van de TKP, met name wat betreft de rotatie van de femurcomponent. Daarnaast heb ik onderzocht of CAOS leidt tot een juiste maatvoering van de TKP en patella tracking. Ik kom tot de conclusie dat men uit moet kijken voor het plaatsen van met name een te grote femurcomponent. De data die verkregen zijn middels het gebruik van de patella tracking module worden significant be_nvloed door de snelheid van bewegen van de knie en de zichtbaarheid van een markertree. Tot slot is bekeken wat de klinische en radiologische uitkomst is van een TKP geplaatst met CAOS. Hoewel er aanwijzingen zijn dat het aantal outliers wat betreft het alignment van de TKP met CAOS afneemt, kan er geen relatie aangetoond worden met de klinische uitkomst van de prothese. Momenteel is CAOS een bruikbare techniek voor onderzoeksdoeleinden, zoals de chirurgische techniek en kinematische analyse, en als onderwijsinstrument. Verder onderzoek is nodig om de exacte plaats van CAOS bij het plaatsen van TKP te bepalen. Tot die tijd moet men kritisch blijven wat betreft de toepassing van nieuwe technieken in de Orthopaedische Chirurgie, deze gefaseerd invoeren en de vraag stellen of iets een __tool__ of een __toy__ is.UBL - phd migration 201

A Novel Method for Determining the Inherent Capabilities of Computer and Robotic-Assisted Total Knee Arthroplasty Devices

This thesis presents a method for evaluating and comparing assistive total knee arthroplasty (TKA) devices while controlling surgeon landmarking variability. To achieve consistent landmark selection by surgeons during TKA procedures, the method uses identical 3D-printed knees with indented landmarks. This method was used to compare a robotic and computer-assisted TKA device on three metrics: measurement accuracy, alignment accuracy, and cut-surface uniformity. Although both devices had considerable sagittal plane measurement errors, the robotic device had better measurement and alignment accuracy than the computer-assisted device. Furthermore, the robotic device\u27s measuring error compensated for cutting errors, but the computer-assisted device\u27s compounded them. However, both techniques were equally able to maintain small bone-implant gaps. This thesis demonstrates that this new method can be used to draw conclusions about the inherent capabilities and limitations of robotic and computer-assisted TKA devices

Augmented reality for computer assisted orthopaedic surgery

In recent years, computer-assistance and robotics have established their presence in operating theatres and found success in orthopaedic procedures. Benefits of computer assisted orthopaedic surgery (CAOS) have been thoroughly explored in research, finding improvements in clinical outcomes, through increased control and precision over surgical actions. However, human-computer interaction in CAOS remains an evolving field, through emerging display technologies including augmented reality (AR) – a fused view of the real environment with virtual, computer-generated holograms. Interactions between clinicians and patient-specific data generated during CAOS are limited to basic 2D interactions on touchscreen monitors, potentially creating clutter and cognitive challenges in surgery. Work described in this thesis sought to explore the benefits of AR in CAOS through: an integration between commercially available AR and CAOS systems, creating a novel AR-centric surgical workflow to support various tasks of computer-assisted knee arthroplasty, and three pre–clinical studies exploring the impact of the new AR workflow on both existing and newly proposed quantitative and qualitative performance metrics. Early research focused on cloning the (2D) user-interface of an existing CAOS system onto a virtual AR screen and investigating any resulting impacts on usability and performance. An infrared-based registration system is also presented, describing a protocol for calibrating commercial AR headsets with optical trackers, calculating a spatial transformation between surgical and holographic coordinate frames. The main contribution of this thesis is a novel AR workflow designed to support computer-assisted patellofemoral arthroplasty. The reported workflow provided 3D in-situ holographic guidance for CAOS tasks including patient registration, pre-operative planning, and assisted-cutting. Pre-clinical experimental validation on a commercial system (NAVIO®, Smith & Nephew) for these contributions demonstrates encouraging early-stage results showing successful deployment of AR to CAOS systems, and promising indications that AR can enhance the clinician’s interactions in the future. The thesis concludes with a summary of achievements, corresponding limitations and future research opportunities.Open Acces

Automatic 3D Postoperative Evaluation of Complex Orthopaedic Interventions

In clinical practice, image-based postoperative evaluation is still performed without state-of-the-art computer methods, as these are not sufficiently automated. In this study we propose a fully automatic 3D postoperative outcome quantification method for the relevant steps of orthopaedic interventions on the example of Periacetabular Osteotomy of Ganz (PAO). A typical orthopaedic intervention involves cutting bone, anatomy manipulation and repositioning as well as implant placement. Our method includes a segmentation based deep learning approach for detection and quantification of the cuts. Furthermore, anatomy repositioning was quantified through a multi-step registration method, which entailed a coarse alignment of the pre- and postoperative CT images followed by a fine fragment alignment of the repositioned anatomy. Implant (i.e., screw) position was identified by 3D Hough transform for line detection combined with fast voxel traversal based on ray tracing. The feasibility of our approach was investigated on 27 interventions and compared against manually performed 3D outcome evaluations. The results show that our method can accurately assess the quality and accuracy of the surgery. Our evaluation of the fragment repositioning showed a cumulative error for the coarse and fine alignment of 2.1 mm. Our evaluation of screw placement accuracy resulted in a distance error of 1.32 mm for screw head location and an angular deviation of 1.1° for screw axis. As a next step we will explore generalisation capabilities by applying the method to different interventions

Augmented Reality and Artificial Intelligence in Image-Guided and Robot-Assisted Interventions

In minimally invasive orthopedic procedures, the surgeon places wires, screws, and surgical implants through the muscles and bony structures under image guidance. These interventions require alignment of the pre- and intra-operative patient data, the intra-operative scanner, surgical instruments, and the patient. Suboptimal interaction with patient data and challenges in mastering 3D anatomy based on ill-posed 2D interventional images are essential concerns in image-guided therapies. State of the art approaches often support the surgeon by using external navigation systems or ill-conditioned image-based registration methods that both have certain drawbacks. Augmented reality (AR) has been introduced in the operating rooms in the last decade; however, in image-guided interventions, it has often only been considered as a visualization device improving traditional workflows. Consequently, the technology is gaining minimum maturity that it requires to redefine new procedures, user interfaces, and interactions. This dissertation investigates the applications of AR, artificial intelligence, and robotics in interventional medicine. Our solutions were applied in a broad spectrum of problems for various tasks, namely improving imaging and acquisition, image computing and analytics for registration and image understanding, and enhancing the interventional visualization. The benefits of these approaches were also discovered in robot-assisted interventions. We revealed how exemplary workflows are redefined via AR by taking full advantage of head-mounted displays when entirely co-registered with the imaging systems and the environment at all times. The proposed AR landscape is enabled by co-localizing the users and the imaging devices via the operating room environment and exploiting all involved frustums to move spatial information between different bodies. The system's awareness of the geometric and physical characteristics of X-ray imaging allows the exploration of different human-machine interfaces. We also leveraged the principles governing image formation and combined it with deep learning and RGBD sensing to fuse images and reconstruct interventional data. We hope that our holistic approaches towards improving the interface of surgery and enhancing the usability of interventional imaging, not only augments the surgeon's capabilities but also augments the surgical team's experience in carrying out an effective intervention with reduced complications

Intraoperative Quantification of Bone Perfusion in Lower Extremity Injury Surgery

Orthopaedic surgery is one of the most common surgical categories. In particular, lower extremity injuries sustained from trauma can be complex and life-threatening injuries that are addressed through orthopaedic trauma surgery. Timely evaluation and surgical debridement following lower extremity injury is essential, because devitalized bones and tissues will result in high surgical site infection rates. However, the current clinical judgment of what constitutes “devitalized tissue” is subjective and dependent on surgeon experience, so it is necessary to develop imaging techniques for guiding surgical debridement, in order to control infection rates and to improve patient outcome. In this thesis work, computational models of fluorescence-guided debridement in lower extremity injury surgery will be developed, by quantifying bone perfusion intraoperatively using Dynamic contrast-enhanced fluorescence imaging (DCE-FI) system. Perfusion is an important factor of tissue viability, and therefore quantifying perfusion is essential for fluorescence-guided debridement. In Chapters 3-7 of this thesis, we explore the performance of DCE-FI in quantifying perfusion from benchtop to translation: We proposed a modified fluorescent microsphere quantification technique using cryomacrotome in animal model. This technique can measure bone perfusion in periosteal and endosteal separately, and therefore to validate bone perfusion measurements obtained by DCE-FI; We developed pre-clinical rodent contaminated fracture model to correlate DCE-FI with infection risk, and compare with multi-modality scanning; Furthermore in clinical studies, we investigated first-pass kinetic parameters of DCE-FI and arterial input functions for characterization of perfusion changes during lower limb amputation surgery; We conducted the first in-human use of dynamic contrast-enhanced texture analysis for orthopaedic trauma classification, suggesting that spatiotemporal features from DCE-FI can classify bone perfusion intraoperatively with high accuracy and sensitivity; We established clinical machine learning infection risk predictive model on open fracture surgery, where pixel-scaled prediction on infection risk will be accomplished. In conclusion, pharmacokinetic and spatiotemporal patterns of dynamic contrast-enhanced imaging show great potential for quantifying bone perfusion and prognosing bone infection. The thesis work will decrease surgical site infection risk and improve successful rates of lower extremity injury surgery

Non-invasive quantification of knee kinematics: a cadaver study

The ability to quantify kinematic parameters of the knee is crucial in understanding normal biomechanics, recognising the presence of pathology and its severity, planning treatment and evaluation of outcomes. Current methods of quantifying lower limb kinematics remain limited in allowing accurate dynamic assessment. Computer assisted surgery systems have been validated in quantifying kinematic parameters, but remain limited to the operative setting. Recently, image-free computer assisted surgery technology has been adapted for non-invasive use and validated in terms of repeatability in measuring coronal and sagittal femorotibial mechanical alignment in extension. The aim of this thesis was to develop and implement a set of validation protocols to quantify the reliability, precision and accuracy of this non-invasive technology in quantifying lower limb coronal and sagittal femorotibial mechanical alignment, anteroposterior and rotatory laxity of the knee by comparison with a validated, commercially available image-free computer assisted surgery system. Pilot study confirmed feasibility of further experimental work and revealed that the noninvasive method measured with satisfactory precision and accuracy: coronal mechanical femorotibial alignment (MFTA) from extension to 30° knee flexion, anteroposterior translation in extension and tibial rotatory laxity during flexion. Further experiments using 12 fresh cadaveric limbs revealed that the non-invasive method gave satisfactory precision and agreement with the invasive system measuring MFTA without stress from extension to 40° knee flexion, and with 15Nm coronal stress from extension to 30° knee flexion. Using 100N of anterior force on the tibia, the non-invasive system was acceptably precise and accurate in measuring sagittal tibial displacement from extension to 40° flexion. End of range apprehension, such as has been proven repeatable in measuring tibial rotatory laxity was used and the non-invasive method gave superior 3 precision and accuracy to most reported non-invasive devices in quantifying tibial rotatory range of motion. Non-invasive optical tracking systems provide a means to quantify important kinematic parameters in health and disease, and could allow standardisation of knee examination increasing communicability and translation of findings from the out-patient to operative setting. This technology therefore could allow restoration of individual specific kinematics in knee arthroplasty and soft-tissue reconstruction