Development and Implementation of a Computational Modeling Tool for Evaluation of THA Component Position

The human body is a complicated structure with muscles, ligaments, bones, and joints. Modeling human body with computational tools are becoming a trend [1]. More importantly, using computational tools to evaluate human body is a non-invasive technique that could help surgeons and researchers evaluate implant products [2]. Therefore, the development of a model which can analyze both implant sizing suggestion and kinematics of subject specific data could prove valuable. For total hip arthroplasty, one common complication is in vivo separation and dislocation of the femoral head within the acetabular cup [3] [4]. Developing a successful computational tool to address this issue includes developing a dynamic model of hip joint, implementing implant sizing suggestion algorithms and computing component alignments. Due to advancement in technology, the current focus has been to develop patient-specific solutions, a combined program of both hip model and implant suggestion model has been developed. In this dissertation, the primary objective is to develop a fully functional hip analysis software that not only can suggestion and template the implant sizing and position, but the software can also utilize the patient specific data to run simulation with different activities. The second objective of this dissertation is to conduct hip analysis studies using hip analysis software. Overall, the results in this dissertation discuss the effect of different stem positions and surgeon preferences on the outcome of the Total Hip Arthroplasty

Patient-specific modelling in orthopedics: from image to surgery

In orthopedic surgery, to decide upon intervention and how it can be optimized, surgeons usually rely on subjective analysis of medical images of the patient, obtained from computed tomography, magnetic resonance imaging, ultrasound or other techniques. Recent advancements in computational performance, image analysis and in silico modeling techniques have started to revolutionize clinical practice through the development of quantitative tools, including patient#specific models aiming at improving clinical diagnosis and surgical treatment. Anatomical and surgical landmarks as well as features extraction can be automated allowing for the creation of general or patient-specific models based on statistical shape models. Preoperative virtual planning and rapid prototyping tools allow the implementation of customized surgical solutions in real clinical environments. In the present chapter we discuss the applications of some of these techniques in orthopedics and present new computer-aided tools that can take us from image analysis to customized surgical treatment

Patient-Specific Implants in Musculoskeletal (Orthopedic) Surgery

Most of the treatments in medicine are patient specific, aren’t they? So why should we bother with individualizing implants if we adapt our therapy to patients anyway? Looking at the neighboring field of oncologic treatment, you would not question the fact that individualization of tumor therapy with personalized antibodies has led to the thriving of this field in terms of success in patient survival and positive responses to alternatives for conventional treatments. Regarding the latest cutting-edge developments in orthopedic surgery and biotechnology, including new imaging techniques and 3D-printing of bone substitutes as well as implants, we do have an armamentarium available to stimulate the race for innovation in medicine. This Special Issue of Journal of Personalized Medicine will gather all relevant new and developed techniques already in clinical practice. Examples include the developments in revision arthroplasty and tumor (pelvic replacement) surgery to recreate individual defects, individualized implants for primary arthroplasty to establish physiological joint kinematics, and personalized implants in fracture treatment, to name but a few

Personalized Hip and Knee Joint Replacement

This open access book describes and illustrates the surgical techniques, implants, and technologies used for the purpose of personalized implantation of hip and knee components. This new and flourishing treatment philosophy offers important benefits over conventional systematic techniques, including component positioning appropriate to individual anatomy, improved surgical reproducibility and prosthetic performance, and a reduction in complications. The techniques described in the book aim to reproduce patients’ native anatomy and physiological joint laxity, thereby improving the prosthetic hip/knee kinematics and functional outcomes in the quest of the forgotten joint. They include kinematically aligned total knee/total hip arthroplasty, partial knee replacement, and hip resurfacing. The relevance of available and emerging technological tools for these personalized approaches is also explained, with coverage of, for example, robotics, computer-assisted surgery, and augmented reality. Contributions from surgeons who are considered world leaders in diverse fields of this novel surgical philosophy make this open access book will invaluable to a wide readership, from trainees at all levels to consultants practicing lower limb surger

An automated optimization pipeline for clinical-grade computer-assisted planning of high tibial osteotomies under consideration of weight-bearing

3D preoperative planning for high tibial osteotomies (HTO) has increasingly replaced 2D planning but is complex, time-consuming and therefore expensive. Several interdependent clinical objectives and constraints have to be considered, which often requires multiple rounds of revisions between surgeons and biomedical engineers. We therefore developed an automated preoperative planning pipeline, which takes imaging data as an input to generate a ready-to-use, patient-specific planning solution. Deep-learning based segmentation and landmark localization was used to enable the fully automated 3D lower limb deformity assessment. A 2D-3D registration algorithm allowed the transformation of the 3D bone models into the weight-bearing state. Finally, an optimization framework was implemented to generate ready-to use preoperative plannings in a fully automated fashion, using a genetic algorithm to solve the multi-objective optimization (MOO) problem based on several clinical requirements and constraints. The entire pipeline was evaluated on a large clinical dataset of 53 patient cases who previously underwent a medial opening-wedge HTO. The pipeline was used to automatically generate preoperative solutions for these patients. Five experts blindly compared the automatically generated solutions to the previously generated manual plannings. The overall mean rating for the algorithm-generated solutions was better than for the manual solutions. In 90% of all comparisons, they were considered to be equally good or better than the manual solution. The combined use of deep learning approaches, registration methods and MOO can reliably produce ready-to-use preoperative solutions that significantly reduce human workload and related health costs

3D Innovations in Personalized Surgery

Current practice involves the use of 3D surgical planning and patient-specific solutions in multiple surgical areas of expertise. Patient-specific solutions have been endorsed for several years in numerous publications due to their associated benefits around accuracy, safety, and predictability of surgical outcome. The basis of 3D surgical planning is the use of high-quality medical images (e.g., CT, MRI, or PET-scans). The translation from 3D digital planning toward surgical applications was developed hand in hand with a rise in 3D printing applications of multiple biocompatible materials. These technical aspects of medical care require engineers’ or technical physicians’ expertise for optimal safe and effective implementation in daily clinical routines.The aim and scope of this Special Issue is high-tech solutions in personalized surgery, based on 3D technology and, more specifically, bone-related surgery. Full-papers or highly innovative technical notes or (systematic) reviews that relate to innovative personalized surgery are invited. This can include optimization of imaging for 3D VSP, optimization of 3D VSP workflow and its translation toward the surgical procedure, or optimization of personalized implants or devices in relation to bone surgery

MECHANICAL METRICS OF THE PROXIMAL FEMUR ARE PRECISE AND ASSOCIATED WITH HIP MUSCLE PROPERTIES: A MAGNETIC RESONANCE BASED FINITE ELEMENT STUDY

Proximal femoral (hip) fractures are a life-threatening injury which affects 30,000 Canadians annually. Improved muscle and bone strength assessment methods may reduce fracture occurrence rates in the future. Magnetic resonance (MR) imaging has potential to assess proximal femoral bone strength in vivo through usage of finite element (FE) modeling. Though, to precisely assess bone strength, knowledge of a technique’s measurement error is needed. Hip muscle properties (e.g., lean muscle and fat area) are intrinsically linked to proximal femoral bone strength; however, it is unclear which muscles and properties are most closely associated with bone strength. This thesis is focused on MR-based FE modeling (MR-FE) of the proximal femur and surrounding muscle properties (e.g., hip abductor fat area, hip extensor muscle area). The specific objectives of this research were 1) to characterize the short-term in vivo measurement precision of MR-FE outcomes (e.g., failure load) of the proximal femur for configurations simulating fall and stance loading, and 2) explore associations between upper thigh muscle and fat properties (e.g., hip abductor fat area, knee extensor muscle area) with MR-FE failure loads of the proximal femur. In vivo precision errors (assessed via root mean square coefficient of variation, CV%RMS from repeated measures) of MR-FE outcomes ranged from 3.3-11.8% for stress and strain outcomes, and 6.0-9.5% for failure loads. Hip adductor muscle area and total muscle area correlated with failure load of the fracture-prone neck and intertrochanteric region under both fall and stance loading (correlation coefficients ranged from 0.416-0.671). This is the first study to report the in vivo short-term precision errors of MR-FE outcomes at the proximal femur. Also, this is the first study to relate upper-thigh muscle and fat properties with MR-FE derived failure loads. Results indicate that MR-FE outcomes have comparable precision to computed tomography (CT) based FE outcomes and are related to hip muscle area

Artificial intelligence in orthopaedic surgery

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction

Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction

Various deep learning models have been proposed for 3D bone shape reconstruction from two orthogonal (biplanar) X-ray images. However, it is unclear how these models compare against each other since they are evaluated on different anatomy, cohort and (often privately held) datasets. Moreover, the impact of the commonly optimized image-based segmentation metrics such as dice score on the estimation of clinical parameters relevant in 2D-3D bone shape reconstruction is not well known. To move closer toward clinical translation, we propose a benchmarking framework that evaluates tasks relevant to real-world clinical scenarios, including reconstruction of fractured bones, bones with implants, robustness to population shift, and error in estimating clinical parameters. Our open-source platform provides reference implementations of 8 models (many of whose implementations were not publicly available), APIs to easily collect and preprocess 6 public datasets, and the implementation of automatic clinical parameter and landmark extraction methods. We present an extensive evaluation of 8 2D-3D models on equal footing using 6 public datasets comprising images for four different anatomies. Our results show that attention-based methods that capture global spatial relationships tend to perform better across all anatomies and datasets; performance on clinically relevant subgroups may be overestimated without disaggregated reporting; ribs are substantially more difficult to reconstruct compared to femur, hip and spine; and the dice score improvement does not always bring a corresponding improvement in the automatic estimation of clinically relevant parameters.Comment: accepted to NeurIPS 202