Applied AI/ML for automatic customisation of medical implants

Most knee replacement surgeries are performed using ‘off-the-shelf’ implants, supplied with a set number of standardised sizes. X-rays are taken during pre-operative assessment and used by clinicians to estimate the best options for patients. Manual templating and implant size selection have, however, been shown to be inaccurate, and frequently the generically shaped products do not adequately fit patients’ unique anatomies. Furthermore, off-the-shelf implants are typically made from solid metal and do not exhibit mechanical properties like the native bone. Consequently, the combination of these factors often leads to poor outcomes for patients. Various solutions have been outlined in the literature for customising the size, shape, and stiffness of implants for the specific needs of individuals. Such designs can be fabricated via additive manufacturing which enables bespoke and intricate geometries to be produced in biocompatible materials. Despite this, all customisation solutions identified required some level of manual input to segment image files, identify anatomical features, and/or drive design software. These tasks are time consuming, expensive, and require trained resource. Almost all currently available solutions also require CT imaging, which adds further expense, incurs high levels of potentially harmful radiation, and is not as commonly accessible as X-ray imaging. This thesis explores how various levels of knee replacement customisation can be completed automatically by applying artificial intelligence, machine learning and statistical methods. The principal output is a software application, believed to be the first true ‘mass-customisation’ solution. The software is compatible with both 2D X-ray and 3D CT data and enables fully automatic and accurate implant size prediction, shape customisation and stiffness matching. It is therefore seen to address the key limitations associated with current implant customisation solutions and will hopefully enable the benefits of customisation to be more widely accessible.Open Acces

Modelo de sistema de soporte a la diagnosis de trastornos osteoarticulares de miembros inferiores utilizando procesamiento de imágenes de rayos X

Los trastornos osteoarticulares aquejan a personas de todas las regiones del mundo sin distinción, ejemplos de ellas son: la osteoporosis y atrosis. La OMS determina la existencia de un incremento de casos en sociedades socioeconómicas más bajas y la Unión Europea establece una estrategia enfocada a entregar salud personalizada en el momento correcto, y brindar una alternativa de prevención oportuna y especifica denominada (PerMed). En este contexto nuestro país necesita aplicar la Medicina Personalizada para diagnosticar a tiempo enfermedades con alta incidencia. La presente investigación busca alinearse a los objetivos de la Medicina Personalizada proporcionando un modelo de sistema de soporte a la diagnosis de trastornos osteoarticulares de miembros inferiores utilizando procesamiento de imágenes de rayos X, teniendo presente la confidencialidad y protección de los datos. El pre-procesamiento de las imágenes de rayos X, permitió eliminar los desafíos de estas imágenes, y posibilito la generación de un gold-standard que sirvió como guía para la segmentación-registro de las estructuras óseas de miembros inferiores. Se utilizaron los modelos estadísticos como: SSM - Statistical Shape Model, SAM – Statistical Appeareance Model, ASM - Active Shape Model y Gradient Profiling en el refinamiento de la etapa de segmentación-registro como parte del entrenamiento y prueba. Estos modelos han sido validados con artículos de investigación presentados en el Capítulo IV con resultados de precisión en la segmentación entre el 74 % y 83 % y para la clasificación de las estructuras óseas dependiendo del objetivo a resolver sea: a) detectar fracturas en el acetábulo, o b) detectar osteoporosis en el fémur proximal, los resultados obtuvieron una precisión de: 73% y 87% respectivamente; y por ultimo para lograr el objetivo de: c) medir la distancia articular, se obtiene un error promedio equivalente a 2.4 px, este es un error aceptable para respaldar el diagnostico de desgaste articular de cadera llamado "osteoartritis de cadera". Asimismo, hubo una mejora significativa en el tiempo de procesamiento comparado con la literatura analizada

Tibiofemoral contact areas and contact forces in healthy and osteoarthritic subjects

Knee osteoarthritis (OA) is a common type of musculoskeletal disability, particularly among the elderly population. Excessive contact forces on the joint, or on specific parts of it (e.g. medial compartment), or shifting the contact forces to the regions that are not adapted to loading are the mechanical factors which can trigger OA. Therefore, it is crucial to understand the differences of these mechanical parameters in OA subjects with respect to the healthy ones. The aim of this study was to the compare the tibiofemoral contact point locations and the contact forces in OA and healthy subjects and examine if the contact point locations influence the contact force sharing in both groups. The tibiofemoral contact point locations in 10 healthy and 9 osteoarthritic (OA) subjects during a weight-bearing squat was measured using stand-alone biplane X-ray images. A manual multiple view 3D reconstruction/registration method was used to reconstruct the bones in different squat postures from the biplane radiographs and a weighted center of bone-to-bone proximity was applied to estimate the contact point locations. Results showed that the contact point locations of the OA subjects on the medial and lateral compartments were shifted medially compared to the healthy group. In both groups, contact points showed a posterior excursion on the medial compartment and posterior and lateral excursions on the lateral compartment, where the excursion on the lateral compartment was smaller in OA subjects. To estimate the tibiofemoral contact forces, a custom musculoskeletal model of the lower limb with the integration of personalized contact points was provided to estimate contact forces at subject-specific contact points during gait. The tibiofemoral joint model was reformulated so that the constraints of the joint were formed by the superimposition of the personalized tibial and femoral contact points. The suggested constraints are adaptable to the contact points derived from the classical joint models or those experimentally measured from the 3D imaging techniques. The estimated contact forces estimated using the personalized contact points were compared to those estimated from the classical knee joint models in 10 healthy subjects. Results showed that the impact of personalization of contact points on the contact forces is very variable among the subjects and the shifts of the contact points alone cannot predict the distribution of contact forces in the medial and lateral compartments. To evaluate the contribution of contact point locations to the contact force distribution the musculoskeletal model of the lower limb with the personalized contact point trajectories were used to estimate the medial and lateral contact forces of 10 healthy and 12 OA subjects. The contact forces in healthy subjects were slightly higher compared to the OA subjects. However, no statistically significant difference was noted in the peaks of medial, lateral or total contact forces. The regression analysis results showed that the knee adduction moment and knee flexion moment were the main contributors to the medial-to-total contact force ratio (MR) in both groups. From the components of the contact point location, the medial contact point location in medial/lateral direction had a significant contribution to the MR in OA subjects. This study showed that the mechanism of load distribution was different in OA joints where contrary to the healthy ones the contact point location was a significant contributor to MR. In addition, the knee flexion moment had a higher contribution to MR than the knee adduction moment whereas in healthy subjects the knee adduction moment was the most significant contributor to the MR

3D Shape Reconstruction of Knee Bones from Low Radiation X-ray Images Using Deep Learning

Understanding the bone kinematics of the human knee during dynamic motions is necessary to evaluate the pathological conditions, design knee prosthesis, orthosis and surgical treatments such as knee arthroplasty. Also, knee bone kinematics is essential to assess the biofidelity of the computational models. Kinematics of the human knee has been reported in the literature either using in vitro or in vivo methodologies. In vivo methodology is widely preferred due to biomechanical accuracies. However, it is challenging to obtain the kinematic data in vivo due to limitations in existing methods. One of the several existing methods used in such application is using X-ray fluoroscopy imaging, which allows for the non-invasive quantification of bone kinematics. In the fluoroscopy imaging method, due to procedural simplicity and low radiation exposure, single-plane fluoroscopy (SF) is the preferred tool to study the in vivo kinematics of the knee joint. Evaluation of the three-dimensional (3D) kinematics from the SF imagery is possible only if prior knowledge of the shape of the knee bones is available. The standard technique for acquiring the knee shape is to either segment Magnetic Resonance (MR) images, which is expensive to procure, or Computed Tomography (CT) images, which exposes the subjects to a heavy dose of ionizing radiation. Additionally, both the segmentation procedures are time-consuming and labour-intensive. An alternative technique that is rarely used is to reconstruct the knee shape from the SF images. It is less expensive than MR imaging, exposes the subjects to relatively lower radiation than CT imaging, and since the kinematic study and the shape reconstruction could be carried out using the same device, it could save a considerable amount of time for the researchers and the subjects. However, due to low exposure levels, SF images are often characterized by a low signal-to-noise ratio, making it difficult to extract the required information to reconstruct the shape accurately. In comparison to conventional X-ray images, SF images are of lower quality and have less detail. Additionally, existing methods for reconstructing the shape of the knee remain generally inconvenient since they need a highly controlled system: images must be captured from a calibrated device, care must be taken while positioning the subject's knee in the X-ray field to ensure image consistency, and user intervention and expert knowledge is required for 3D reconstruction. In an attempt to simplify the existing process, this thesis proposes a new methodology to reconstruct the 3D shape of the knee bones from multiple uncalibrated SF images using deep learning. During the image acquisition using the SF, the subjects in this approach can freely rotate their leg (in a fully extended, knee-locked position), resulting in several images captured in arbitrary poses. Relevant features are extracted from these images using a novel feature extraction technique before feeding it to a custom-built Convolutional Neural Network (CNN). The network, without further optimization, directly outputs a meshed 3D surface model of the subject's knee joint. The whole procedure could be completed in a few minutes. The robust feature extraction technique can effectively extract relevant information from a range of image qualities. When tested on eight unseen sets of SF images with known true geometry, the network reconstructed knee shape models with a shape error (RMSE) of 1.91± 0.30 mm for the femur, 2.3± 0.36 mm for the tibia and 3.3± 0.53 mm for the patella. The error was calculated after rigidly aligning (scale, rotation, and translation) each of the reconstructed shape models with the corresponding known true geometry (obtained through MRI segmentation). Based on a previous study that examined the influence of reconstructed shape accuracy on the precision of the evaluation of tibiofemoral kinematics, the shape accuracy of the proposed methodology might be adequate to precisely track the bone kinematics, although further investigation is required

Automatic image analysis of C-arm Computed Tomography images for ankle joint surgeries

Open reduction and internal fixation is a standard procedure in ankle surgery for treating a fractured fibula. Since fibula fractures are often accompanied by an injury of the syndesmosis complex, it is essential to restore the correct relative pose of the fibula relative to the adjoining tibia for the ligaments to heal. Otherwise, the patient might experience instability of the ankle leading to arthritis and ankle pain and ultimately revision surgery. Incorrect positioning referred to as malreduction of the fibula is assumed to be one of the major causes of unsuccessful ankle surgery. 3D C-arm imaging is the current standard procedure for revealing malreduction of fractures in the operating room. However, intra-operative visual inspection of the reduction result is complicated due to high inter-individual variation of the ankle anatomy and rather based on the subjective experience of the surgeon. A contralateral side comparison with the patient’s uninjured ankle is recommended but has not been integrated into clinical routine due to the high level of radiation exposure it incurs. This thesis presents the first approach towards a computer-assisted intra-operative contralateral side comparison of the ankle joint. The focus of this thesis was the design, development and validation of a software-based prototype for a fully automatic intra-operative assistance system for orthopedic surgeons. The implementation does not require an additional 3D C-arm scan of the uninjured ankle, thus reducing time consumption and cumulative radiation dose. A 3D statistical shape model (SSM) is used to reconstruct a 3D surface model from three 2D fluoroscopic projections representing the uninjured ankle. To this end, a 3D SSM segmentation is performed on the 3D image of the injured ankle to gain prior knowledge of the ankle. A 3D convolutional neural network (CNN) based initialization method was developed and its outcome was incorporated into the SSM adaption step. Segmentation quality was shown to be improved in terms of accuracy and robustness compared to the pure intensity-based SSM. This allows us to overcome the limitations of the previously proposed methods, namely inaccuracy due to metal artifacts and the lack of device-to-patient orientation of the C-arm. A 2D-CNN is employed to extract semantic knowledge from all fluoroscopic projection images. This step of the pipeline both creates features for the subsequent reconstruction and also helps to pre-initialize the 3D-SSM without user interaction. A 2D-3D multi-bone reconstruction method has been developed which uses distance maps of the 2D features for fast and accurate correspondence optimization and SSM adaption. This is the central and most crucial component of the workflow. This is the first time that a bone reconstruction method has been applied to the complex ankle joint and the first reconstruction method using CNN based segmentations as features. The reconstructed 3D-SSM of the uninjured ankle can be back-projected and visualized in a workflow-oriented manner to procure clear visualization of the region of interest, which is essential for the evaluation of the reduction result. The surgeon can thus directly compare an overlay of the contralateral ankle with the injured ankle. The developed methods were evaluated individually using data sets acquired during a cadaver study and representative clinical data acquired during fibular reduction. A hierarchical evaluation was designed to assess the inaccuracies of the system on different levels and to identify major sources of error. The overall evaluation performed on eleven challenging clinical datasets acquired for manual contralateral side comparison showed that the system is capable of accurately reconstructing 3D surface models of the uninjured ankle solely using three projection images. A mean Hausdorff distance of 1.72 mm was measured when comparing the reconstruction result to the ground truth segmentation and almost achieved the high required clinical accuracy of 1-2 mm. The overall error of the pipeline was mainly attributed to inaccuracies in the 2D-CNN segmentation. The consistency of these results requires further validation on a larger dataset. The workflow proposed in this thesis establishes the first approach to enable automatic computer-assisted contralateral side comparison in ankle surgery. The feasibility of the proposed approach was proven on a limited amount of clinical cases and has already yielded good results. The next important step is to alleviate the identified bottlenecks in the approach by providing more training data in order to further improve the accuracy. In conclusion, the new approach presented gives the chance to guide the surgeon during the reduction process, improve the surgical outcome while avoiding additional radiation exposure and reduce the number of revision surgeries in the long term

A shape analysis approach to prediction of bone stiffness using FEXI

The preferred method of assessing the risk of an osteoporosis related fracture is currently a measure of bone mineral density (BMD) by dual energy X-ray absorptiometry (DXA). However, other factors contribute to the overall risk of fracture, including anatomical geometry and the spatial distribution of bone. Finite element analysis can be performed in both two and three dimensions, and predicts the deformation or induced stress when a load is applied to a structure (such as a bone) of defined material composition and shape. The simulation of a mechanical compression test provides a measure of whole bone stiffness (N mm−1). A simulation system was developed to study the sensitivity of BMD, 3D and 2D finite element analysis to variations in geometric parameters of a virtual proximal femur model. This study demonstrated that 3D FE and 2D FE (FEXI) were significantly more sensitive to the anatomical shape and composition of the proximal femur than conventional BMD. The simulation approach helped to analyse and understand how variations in geometric parameters affect the stiffness and hence strength of a bone susceptible to osteoporotic fracture. Originally, the FEXI technique modelled the femur as a thin plate model of an assumed constant depth for finite element analysis (FEA). A better prediction of tissue depth across the bone, based on its geometry, was required to provide a more accurate model for FEA. A shape template was developed for the proximal femur to provide this information for the 3D FE analysis. Geometric morphometric techniques were used to procure and analyse shape information from a set of CT scans of excised human femora. Generalized Procrustes Analysis and Thin Plate Splines were employed to analyse the data and generate a shape template for the proximal femur. 2D Offset and Depth maps generated from the training set data were then combined to model the three-dimensional shape of the bone. The template was used to predict the three-dimensional bone shape from a 2D image of the proximal femur procured through a DXA scan. The error in the predicted 3D shape was measured as the difference in predicted and actual depths at each pixel. The mean error in predicted depths was found to be 1.7mm compared to an average bone depth of 34mm. 3D FEXI analysis on the predicted 3D bone along with 2D FEXI for a stance loading condition and BMD measurement were performed based on 2D radiographic projections of the CT scans and compared to bone stiffness results obtained from finite element analysis of the original 3D CT scans. 3D FEXI provided a significantly higher correlation (R2 = 0.85) with conventional CT derived 3D finite element analysis than achieved with both BMD (R2 = 0.52) and 2D FEXI (R2 = 0.44)

Évaluation tridimensionnelle de la reconstruction du ligament croisé antérieur

Le ligament croisé antérieur (LCA) demeure un des ligaments du genou le plus souvent blessé. Un mauvais positionnement des tunnels osseux est souvent mis en cause dans les échecs de reconstructions du LCA. Une meilleure compréhension biomécanique du phénomène devient essentielle. Par l’utilisation de l’imagerie biplanaire stéréoradiographique à faible irradiation EOS , notre groupe a développé une méthode de reconstruction 3D permettant une description morphologique osseuse remarquable. Par l’entremise de ce système, un référentiel permet d’évaluer, de manière automatisée, précise et reproductible, le positionnement tridimensionnel des tunnels osseux. Notre groupe souhaite partager ce référentiel afin d’assister les chirurgiens orthopédistes à restaurer une biomécanique optimale dans les reconstructions du LCA.The anterior cruciate ligament (ACL) remains one of the most injured ligament of the knee. Mispositioning the tunnels remains a common cause of ACL reconstruction failure. A better biomechanical description of this phenomenon is therefore essential. Using the low irradiation biplanar stereoradiographic EOStm imaging system, our group developed a 3D reconstruction method allowing a precise morphologic description of the knee. With this system, the tridimensional positioning of the femoral tunnel can be evaluated in a novel, computerized, precise and reproducible coordinate system. With this referential, our group wish to assist orthopedic surgeons in the restoration of optimal biomechanics in ACL reconstructions

A Novel Free Form Femoral Cutting Guide

Knee arthoplasty is a common procedure that requires the removal of damaged bone and cartilage from the distal femur so that a reconstructive implant may be installed. Traditionally, a five planar resection has been accomplished with a universal cutting box and navigated with either metal jigs or optically tracked computer navigation systems. Free form, or curved, resections have been made possible with surgical robots which control the resection pathway and serve as the navigation system. The free form femoral cutting guide serves as a non powered framework to guide a standard surgical drill along an anatomically defined pathway, resulting in the removal of distal femoral cartilage. It is fixed via attachment to a bone mounted base component, which is positioned with a patient specific jig. To operate, the surgeon slides the surgical drill along a pair of interlocked tracks. One track controls motion in the anteroposterior (AP) direction and one track controls motion in the mediolateral (ML) direction. Combining both motions results in the removal of cartilage from the area of the distal femur for unilateral or total knee arthoplasty