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

Development and Experimental Analysis of Wireless High Accuracy Ultra-Wideband Localization Systems for Indoor Medical Applications

This dissertation addresses several interesting and relevant problems in the field of wireless technologies applied to medical applications and specifically problems related to ultra-wideband high accuracy localization for use in the operating room. This research is cross disciplinary in nature and fundamentally builds upon microwave engineering, software engineering, systems engineering, and biomedical engineering. A good portion of this work has been published in peer reviewed microwave engineering and biomedical engineering conferences and journals. Wireless technologies in medicine are discussed with focus on ultra-wideband positioning in orthopedic surgical navigation. Characterization of the operating room as a medium for ultra-wideband signal transmission helps define system design requirements. A discussion of the first generation positioning system provides a context for understanding the overall system architecture of the second generation ultra-wideband positioning system outlined in this dissertation. A system-level simulation framework provides a method for rapid prototyping of ultra-wideband positioning systems which takes into account all facets of the system (analog, digital, channel, experimental setup). This provides a robust framework for optimizing overall system design in realistic propagation environments. A practical approach is taken to outline the development of the second generation ultra-wideband positioning system which includes an integrated tag design and real-time dynamic tracking of multiple tags. The tag and receiver designs are outlined as well as receiver-side digital signal processing, system-level design support for multi-tag tracking, and potential error sources observed in dynamic experiments including phase center error, clock jitter and drift, and geometric position dilution of precision. An experimental analysis of the multi-tag positioning system provides insight into overall system performance including the main sources of error. A five base station experiment shows the potential of redundant base stations in improving overall dynamic accuracy. Finally, the system performance in low signal-to-noise ratio and non-line-of-sight environments is analyzed by focusing on receiver-side digitally-implemented ranging algorithms including leading-edge detection and peak detection. These technologies are aimed at use in next-generation medical systems with many applications including surgical navigation, wireless telemetry, medical asset tracking, and in vivo wireless sensors

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

Proceedings XXIII Congresso SIAMOC 2023

Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica (SIAMOC), giunto quest’anno alla sua ventitreesima edizione, approda nuovamente a Roma. Il congresso SIAMOC, come ogni anno, è l’occasione per tutti i professionisti che operano nell’ambito dell’analisi del movimento di incontrarsi, presentare i risultati delle proprie ricerche e rimanere aggiornati sulle più recenti innovazioni riguardanti le procedure e le tecnologie per l’analisi del movimento nella pratica clinica. Il congresso SIAMOC 2023 di Roma si propone l’obiettivo di fornire ulteriore impulso ad una già eccellente attività di ricerca italiana nel settore dell’analisi del movimento e di conferirle ulteriore respiro ed impatto internazionale. Oltre ai qualificanti temi tradizionali che riguardano la ricerca di base e applicata in ambito clinico e sportivo, il congresso SIAMOC 2023 intende approfondire ulteriori tematiche di particolare interesse scientifico e di impatto sulla società. Tra questi temi anche quello dell’inserimento lavorativo di persone affette da disabilità anche grazie alla diffusione esponenziale in ambito clinico-occupazionale delle tecnologie robotiche collaborative e quello della protesica innovativa a supporto delle persone con amputazione. Verrà infine affrontato il tema dei nuovi algoritmi di intelligenza artificiale per l’ottimizzazione della classificazione in tempo reale dei pattern motori nei vari campi di applicazione

Computational testing of patient-specific gait features and pelvic motion effects on the risk of edge contact in total hip replacements

Although total hip replacement (THR) surgery is considered one of the most successful orthopaedic interventions, failures which require revision still occur. One of the known contributors to the failure of THR is edge contact, where the acetabular cup and the femoral head remain concentric but contact falls partially on the cup's rim. Failures associated with edge contact include rim damage, osteolysis and cup dissociation due to altered in vivo loading and torques. The current structural and tribological pre-clinical testing protocols fail to capture the spread of pelvic movement and joint contact force directions, which can be seen in a patient-specific analysis. Therefore these tests cannot always predict the success of the THR while in vivo. The broad aim of the PhD project presented in this thesis was to bridge the gap between pre-clinical testing and biomechanical THR studies with a focus on risk of edge contact. The effect of pelvic motion exclusion (common in in vitro studies) on the risk of edge contact was assessed from patient-specific perspective. In this work a computational approach was used to achieve the aim. The data for the analysis was gained from previous experimental biomechanical studies, including a conventional force platform and motions marker study, an instrumented implant study and a dual video-fluoroscopy study. The developed computational algorithms identified the relative position of THR bearing components based on the motions of femur and pelvis. The results of two central studies within this PhD showed that the exclusion of pelvic motions substantially affects the risk of edge contact. However, the effect of pelvic motions on the risk of edge contact was shown to be patient-specific. It was found that pelvic sagittal tilt, coronal obliquity and internal-external rotation all contribute to the overall effect of pelvic motions on the risk of edge contact. In addition, the studies within this project revealed that static orientations of the acetabular cup during standing are not representative of the orientation during dynamic activities. The use of dual video-fluoroscopy techniques were shown to have potential to eliminate uncertainty in variability between static acetabular cup orientation and while THR is in motion. The work presented in this thesis, showed the importance of considering the dynamic activity effects on the success of THR device, which potentially applies to other artificial joints. The methods used can be applied to both pre-clinical testing and preoperative planning, as well as postoperative THR success management. Further studies on larger and more diverse patient cohorts are required to estimate, and in some cases predict, the patient-specific characteristics which affect the risk of edge contact in vivo

Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running

Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products

Proceedings XXI Congresso SIAMOC 2021

XXI Congresso Annuale della SIAMOC, modalità telematica il 30 settembre e il 1° ottobre 2021. Come da tradizione, il congresso vuole essere un’occasione di arricchimento e mutuo scambio, dal punto di vista scientifico e umano. Verranno toccati i temi classici dell’analisi del movimento, come lo sviluppo e l’applicazione di metodi per lo studio del movimento nel contesto clinico, e temi invece estremamente attuali, come la teleriabilitazione e il telemonitoraggio

Kinematics-Based Recovery Metrics and Inertial Measurement Units to Monitor Recovery Post-Knee Arthroscopic Surgery: A Case Study

Physiotherapy after lower-limb injury or surgery is essential for recovery of range of motion, functional movement, strength, and return to sport. Clinicians assess patients, prescribe rehabilitation exercises, and monitor progress through recovery phases. Given the bulk of recovery occurs between in-person visits, coupled with regional differences in access to physiotherapy care, remote monitoring of recovery is warranted to improve patient care and recovery. This work follows the recovery of a patient after arthroscopic partial meniscectomy (APM) surgery, a procedure to remove part of the meniscus in the knee joint. The meniscus is a tissue in the knee joint that improves the articulating surface between the femur and tibia, shock absorption, and transmits force. A conservative estimate puts the rate of meniscal tears at 60 per 100,000, making the APM procedure one of the most common orthopaedic procedures performed. Rehabilitation after APM procedure is generally separated into three phases where the continuation to the next phase relies on meeting the goals of the previous phase as determined by clinician assessments. Assessments are often done through visual observation, manual testing, and goniometric measurements. In a remote setting, these assessments and measurements are challenging to conduct. Wearable inertial measurement units (IMUs) can reconstruct 3D human motion in an unconstrained space, making them a potentially useful tool for remote visualization of therapy exercises and for generating recovery metrics that clinicians can use to inform decision making. The first part of this work extracts current and exploratory recovery metrics to examine recovery over time, alignment with clinical decisions, and explores novel metrics quantify recovery remotely. Exploratory recovery metrics were extracted based on literature review, clinical input, and incidental findings. Fifty-one (51) recovery metrics were extracted for 5 of the most common rehabilitation exercises: supine heel slide, leg raise, straight line walking, goblet squats, and single leg Romanian deadlifts. Metrics showed strong evidence of recovery if all of the following conditions were observed: improving trends over the recovery period, trends between affected and unaffected limbs, and significant differences. Metrics showed moderate evidence of recovery if two of three conditions were met and weak evidence of recovery if only one or no conditions were met. Of all the metrics examined, 39.2% (20/51) of metrics provided strong evidence of recovery, determined by trends over recovery, between affected and unaffected limbs, and statistical significance. An additional 45.1% (22) of the metrics showed moderate evidence of tracking recovery over time for this case study. Of the 23 exploratory recovery metrics examined, 13 showed strong evidence of recovery and potential for use in tracking rehabilitation. The second component of this thesis examined the IMU metric error relative to motion capture-based metrics and exercise specific tuning of the IMU algorithm noise parameters. Error between IMU and motion capture metrics being smaller than the effect size, as well as IMU metrics demonstrating similar recovery trends to motion capture metrics, were factors considered when determining the remote monitoring potential using IMU metrics. IMU feasibility was considered strong if both these conditions were met, moderate if only one condition was met, and weak if neither condition was met. Fourteen (14) metrics showed strong feasibility for remote monitoring using the algorithm and another 24 metrics showed moderate feasibility. Tuning the IMU algorithm measurement noise parameters for the heel slide and leg raise showed that increasing gyroscope noise improved heel slide metric error 9.48%, while decreasing gyroscope noise improved metric error for the leg raise exercise by 23.5%. Finally, a clinician survey was conducted to gather clinician feedback on recovery metrics and stakeholder opinion on future use of the data. As the target primary users of the data presented in this work, 19 physiotherapists participated in the survey. For all metrics they currently use, 95.5% of respondents said they would use the data provided to assist in monitoring recovery. Eight-one percent (81.1%) of respondents said they would potentially use data from exploratory recovery metrics to assist in their clinical decision making, if the data was available. Strength of clinician feedback from the survey was based on the percentage of responses that said they would use the data to inform therapeutic decision making. This work presents examination of new and existing recovery metrics and a wearable IMU system to monitor recovery remotely using a case study of a patient recovery from a lower limb surgery. Existing metrics provide good indication of recovery, while a subset of exploratory metrics show potential to add valuable recovery information given further validation. Preliminary results indicate that setting exercise specific tuning parameters might have potential for better algorithm performance. Initial clinician feedback on motion capture metrics and future use was primarily positive. Overall, 10 metrics are rated as strong in all two or three categories. Six (6) other metrics were tracked well using the IMU algorithm, however did not show recovery in this case study. Ten (10) metrics showed trends over the recovery period, but only demonstrated moderate success tracking trends using IMUs. Combined, the information presented in this work shows promise in improving patient care and recovery, potentially increasing access to quality care, and transitioning sensor-based human movement reconstruction tools to a clinical setting