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

3D computerized model for measuring strain and displacement of the brachial plexus following placement of reverse shoulder prosthesis

The aim of the present study was to develop a method for three-dimensional (3D) reconstruction of the brachial plexus to study its morphology and to calculate strain and displacement in relation to changed nerve position. The brachial plexus was finely dissected and injected with contrast medium and leaden markers were implanted into the nerves at predefined places. A reverse shoulder prosthesis was inserted in a cadaveric specimen what induced positional change in the upper limb nerves. Computed tomography (CT) was performed before and after this surgical intervention. The computer assisted image processing package Mimics (R) was used to reconstruct the pre- and postoperative brachial plexus in 3D. The results show that the current interactive model is a realistic and detailed representation of the specimen used, which allows 3D study of the brachial plexus in different configurations. The model estimated strains up to 15.3% and 19.3% for the lateral and the medial root of the median nerve as a consequence of placing a reverse shoulder prosthesis. Furthermore, the model succeeded in calculating the displacement of the brachial plexus by tracking each implanted lead marker. The presented brachial plexus 3D model currently can be used in vitro for cadaver biomechanical analyses of nerve movement to improve diagnosis and treatment of peripheral neuropathies. The model can also be applied to study the exact location of the plexus in unusual upper limb positions like during axillary radiation therapy and it is a potential tool to optimize the approaches of brachial plexus anesthetic blocks

Soft tissue structure modelling for use in orthopaedic applications and musculoskeletal biomechanics

We present our methodology for the three-dimensional anatomical and geometrical description of soft tissues, relevant for orthopaedic surgical applications and musculoskeletal biomechanics. The technique involves the segmentation and geometrical description of muscles and neurovascular structures from high-resolution computer tomography scanning for the reconstruction of generic anatomical models. These models can be used for quantitative interpretation of anatomical and biomechanical aspects of different soft tissue structures. This approach should allow the use of these data in other application fields, such as musculoskeletal modelling, simulations for radiation therapy, and databases for use in minimally invasive, navigated and robotic surgery

Dynamic three-dimensional shoulder MRI during active motion for investigation of rotator cuff diseases

Background MRI is the standard methodology in diagnosis of rotator cuff diseases. However, many patients continue to have pain despite treatment, and MRI of a static unloaded shoulder seems insufficient for best diagnosis and treatment. This study evaluated if Dynamic MRI provides novel kinematic data that can be used to improve the understanding, diagnosis and best treatment of rotator cuff diseases. Methods Dynamic MRI provided real-time 3D image series and was used to measure changes in the width of subacromial space, superior-inferior translation and anterior-posterior translation of the humeral head relative to the glenoid during active abduction. These measures were investigated for consistency with the rotator cuff diseases classifications from standard MRI. Results The study included: 4 shoulders with massive rotator cuff tears, 5 shoulders with an isolated full-thickness supraspinatus tear, 5 shoulders with tendinopathy and 6 normal shoulders. A change in the width of subacromial space greater than 4mm differentiated between rotator cuff diseases with tendon tears (massive cuff tears and supraspinatus tear) and without tears (tendinopathy) (p = 0.012). The range of the superior-inferior translation was higher in the massive cuff tears group (6.4mm) than in normals (3.4mm) (p = 0.02). The range of the anterior-posterior translation was higher in the massive cuff tears (9.2 mm) and supraspinatus tear (9.3 mm) shoulders compared to normals (3.5mm) and tendinopathy (4.8mm) shoulders (p = 0.05). Conclusion The Dynamic MRI enabled a novel measure; ‘Looseness’, i.e. the translation of the humeral head on the glenoid during an abduction cycle. Looseness was better able at differentiating different forms of rotator cuff disease than a simple static measure of relative glenohumeral position

Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches

Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A variety of investigational deep learning algorithms have been developed to detect anterior cruciate ligament tears, meniscus tears, and rotator cuff disorders. Additional deep learning-based MRI algorithms have been investigated to detect Achilles tendon tears, recurrence prediction of musculoskeletal neoplasms, and complex segmentation of nerves, bones, and muscles. Proof-of-concept studies suggest that deep learning algorithms may achieve similar diagnostic performances when compared to human readers in meta-analyses; however, musculoskeletal radiologists outperformed most deep learning algorithms in studies including a direct comparison. Earlier investigations and developments of deep learning algorithms focused on the binary classification of the presence or absence of an abnormality, whereas more advanced deep learning algorithms start to include features for characterization and severity grading. While many studies have focused on comparing deep learning algorithms against human readers, there is a paucity of data on the performance differences of radiologists interpreting musculoskeletal MRI studies without and with artificial intelligence support. Similarly, studies demonstrating the generalizability and clinical applicability of deep learning algorithms using realistic clinical settings with workflow-integrated deep learning algorithms are sparse. Contingent upon future studies showing the clinical utility of deep learning algorithms, artificial intelligence may eventually translate into clinical practice to assist detection and characterization of various conditions on musculoskeletal MRI exams

Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction

Three-dimensional (3D)-image-based anatomical analysis of rotator cuff tear patients has been proposed as a way to improve repair prognosis analysis to reduce the incidence of postoperative retear. However, for application in clinics, an efficient and robust method for the segmentation of anatomy from MRI is required. We present the use of a deep learning network for automatic segmentation of the humerus, scapula, and rotator cuff muscles with integrated automatic result verification. Trained on N = 111 and tested on N = 60 diagnostic T1-weighted MRI of 76 rotator cuff tear patients acquired from 19 centers, a nnU-Net segmented the anatomy with an average Dice coefficient of 0.91 ± 0.06. For the automatic identification of inaccurate segmentations during the inference procedure, the nnU-Net framework was adapted to allow for the estimation of label-specific network uncertainty directly from its subnetworks. The average Dice coefficient of segmentation results from the subnetworks identified labels requiring segmentation correction with an average sensitivity of 1.0 and a specificity of 0.94. The presented automatic methods facilitate the use of 3D diagnosis in clinical routine by eliminating the need for time-consuming manual segmentation and slice-by-slice segmentation verification

Advancing imaging technologies for patients with spinal pain : with a focus on whiplash injury

Background: Radiological observations of soft-tissue changes that may relate to clinical symptoms in patients with traumatic and non-traumatic spinal disorders are highly controversial. Studies are often of poor quality and findings are inconsistent. A plethora of evidence suggests some pathoanatomical findings from traditional imaging applications are common in asymptomatic participants across the life span, which further questions the diagnostic, prognostic, and theranostic value of traditional imaging. Although we do not dispute the limited evidence for the clinical importance of most imaging findings, we contend that the disparate findings across studies may in part be due to limitations in the approaches used in assessment and analysis of imaging findings. Purpose: This clinical commentary aimed to (1) briefly detail available imaging guidelines, (2) detail research-based evidence around the clinical use of findings from advanced, but available, imaging applications (eg, fat and water magnetic resonance imaging and magnetization transfer imaging), and (3) introduce how evolving imaging technologies may improve our mechanistic understanding of pain and disability, leading to improved treatments and outcomes. Study Design/Setting: A non-systematic review of the literature is carried out. Methods: A narrative summary (including studies from the authors' own work in whiplash injuries) of the available literature is provided. Results: An emerging body of evidence suggests that the combination of existing imaging sequences or the use of developing imaging technologies in tandem with a good clinical assessment of modifiable risk factors may provide important diagnostic information toward the exploration and development of more informed and effective treatment options for some patients with traumatic neck pain. Conclusions: Advancing imaging technologies may help to explain the seemingly disconnected spectrum of biopsychosocial signs and symptoms of traumatic neck pain

A Review of Virtual Reality Based Training Simulators for Orthopaedic Surgery

This review presents current virtual reality based training simulators for hip, knee and other orthopaedic surgery, including elective and trauma surgical procedures. There have not been any reviews focussing on hip and knee orthopaedic simulators. A comparison of existing simulator features is provided to identify what is missing and what is required to improve upon current simulators. In total 11 total hip replacement pre-operative planning tools were analysed, plus 9 hip trauma fracture training simulators. Additionally 9 knee arthroscopy simulators and 8 other orthopaedic simulators were included for comparison. The findings are that for orthopaedic surgery simulators in general, there is increasing use of patient-specific virtual models which reduce the learning curve. Modelling is also being used for patient-specific implant design and manufacture. Simulators are being increasingly validated for assessment as well as training. There are very few training simulators available for hip replacement, yet more advanced virtual reality is being used for other procedures such as hip trauma and drilling. Training simulators for hip replacement and orthopaedic surgery in general lag behind other surgical procedures for which virtual reality has become more common. Further developments are required to bring hip replacement training simulation up to date with other procedures. This suggests there is a gap in the market for a new high fidelity hip replacement and resurfacing training simulator

Objective analysis of neck muscle boundaries for cervical dystonia using ultrasound imaging and deep learning

Objective: To provide objective visualization and pattern analysis of neck muscle boundaries to inform and monitor treatment of cervical dystonia. Methods: We recorded transverse cervical ultrasound (US) images and whole-body motion analysis of sixty-one standing participants (35 cervical dystonia, 26 age matched controls). We manually annotated 3,272 US images sampling posture and the functional range of pitch, yaw, and roll head movements. Using previously validated methods, we used 60-fold cross validation to train, validate and test a deep neural network (U-net) to classify pixels to 13 categories (five paired neck muscles, skin, ligamentum nuchae, vertebra). For all participants for their normal standing posture, we segmented US images and classified condition (Dystonia/Control), sex and age (higher/lower) from segment boundaries. We performed an explanatory, visualization analysis of dystonia muscle-boundaries. Results: For all segments, agreement with manual labels was Dice Coefficient (64±21%) and Hausdorff Distance (5.7±4 mm). For deep muscle layers, boundaries predicted central injection sites with average precision 94±3%. Using leave-one-out cross-validation, a support-vector-machine classified condition, sex, and age from predicted muscle boundaries at accuracy 70.5%, 67.2%, 52.4% respectively, exceeding classification by manual labels. From muscle boundaries, Dystonia clustered optimally into three sub-groups. These sub-groups are visualized and explained by three eigen-patterns which correlate significantly with truncal and head posture. Conclusion: Using US, neck muscle shape alone discriminates dystonia from healthy controls. Significance: Using deep learning, US imaging allows online, automated visualization, and diagnostic analysis of cervical dystonia and segmentation of individual muscles for targeted injection. The dataset is available (DOI: 10.23634/MMUDR.00624643)