Morphometric Evaluation of Detailed Asymmetry for the Proximal Humerus in Korean Population

Computer-assisted orthopedic surgery and patient-specific instruments are widely used in orthopedic fields that utilize contralateral side bone data as a template to restore the affected side bone. The essential precondition for these techniques is that the left and right bone features are similar. Although proximal humerus fracture accounts for 4% to 8% of all fractures, the bilateral asymmetry of the proximal humerus is not fully understood. The aim of this study is to investigate anthropometric differences of the bilateral proximal humerus. One hundred one pairs of Korean humerus CT data from 51 females and 50 males were selected for this research. To investigate bilateral shape differences, we divided the proximal humerus into three regions and the proximal humerus further into five sections in each region. The distance from the centroid to the cortical outline at every 10 degrees was measured in each section. Differences were detected in all regions of the left and right proximal humerus; however, males had a larger number of significant differences than females. Large bilateral differences were measured in the greater tubercle. Nevertheless, using contralateral data as a template for repairing an affected proximal humerus might be possible.

Application-Specific Learning Curve With a Modern Computer-Assisted Orthopedic Surgery System for Joint Arthroplasty

A commonly acknowledged barrier for the adoption of new computer-assisted orthopedic surgery (CAOS) technologies relates to a perceived long and steep learning curve. However, this perception has not been objectively tested with the consideration of surgeon-specific learning approaches. This study employed the cumulative sum control chart (CUSUM) to investigate individual surgeon's learning of CAOS technology by monitoring the stability of the surgical process regarding surgical time. Two applications for total knee arthroplasty (TKA) and two applications for total shoulder arthroplasty (TSA) provided by a modern CAOS system were assessed with a total of 21 surgeons with different levels of previous CAOS experience. The surgeon-specific learning durations identified by CUSUM method revealed that CAOS applications with “full guidance” (i.e., those that offer comprehensive guidance, full customization, and utilize CAOS-specific instrumentation) required on average less than ten cases to learn, while the streamlined application designed as a CAOS augmentation of existing mechanical instrumentation demonstrated a minimal learning curve (less than three cases). During the learning phase, the increase in surgical time was found to be moderate (approximately 15 min or less) for the “full guidance” applications, while the streamlined CAOS application only saw a clinically negligible time increase (under 5 min). The CUSUM method provided an objective and consistent measurement on learning, and demonstrated, contrary to common perception, a minimal to modest learning curve required by the modern CAOS system studied.

Patient-Specific Implants for Focal Cartilage Lesions in The Knee: Implant Survivorship Analysis up to Seven Years Post-Implantation

In the quest for increased surgical precision and improved joint kinematics, Computer-Assisted Orthopedic Surgery (CAOS) shows promising results for both total and partial joint replacement. In the knee, computer-assisted joint design can now be applied to the treatment of younger patients suffering pain and restriction of activity due to focal defects in their femoral articular cartilage. By taking MRI scans of the affected knee and digitally segmenting these scans, we can identify and map focal defects in cartilage and bone. Metallic implants matched to the defect can be fabricated, and guide instrumentation to ensure proper implant alignment and depth of recession in the surrounding cartilage can be designed from segmented MRI scans. Beginning in 2012, a series of 682 patient-specific implants were designed based on MRI analysis of femoral cartilage focal defects, and implanted in 612 knees. A Kaplan-Meier analysis found a cumulative survivorship of 96% at 7-year follow-up from the first implantation. Fourteen (2.3%) of these implants required revision due to disease progression, incorrect implant positioning, and inadequate lesion coverage at the time of surgery. These survivorship data compare favorably with all other modes of treatment for femoral focal cartilage lesions and support the use of patient-specific implants designed from segmented MRI scans in these cases.

Hierarchical 3-D Registration of Computed Tomography to Ultrasound Using Reinforcement Learning

In ultrasound (US)-based computer-assisted orthopedic surgery (CAOS), accurate and robust intra-operative registration in real-time is vital in securing the reliable outcomes for surgical image guidance. For this purpose, we focus on developing a hierarchical registration method, using reinforcement learning (RL), for 3-D registration of pre-operative computed tomography (CT) data to intra-operative US. In the RL-based registration procedure, we proposed a supervised Q-learning framework for learning the sequence of motion action to achieve the optimal alignment. Within the approach, the agent was modeled using PointNet++ with the mis-aligned point set from US and CT as the input, and the next optimal action as the output. Evaluation studies achieved average target registration error (TRE) of 3.82 mm with success rate of 92.7% and an average time of 8.36 seconds. We achieve 57.1% improvement in success rate over state of the art.

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.

Spine Surface Segmentation from Ultrasound Using Multi-feature Guided CNN

Accurate, robust, and real-time segmentation of bone surfaces is an essential objective for ultrasound (US) guided computer assisted orthopedic surgery (CAOS) procedures. In this work, we present a convolutional neural network (CNN)-based technique for segmenting spine surfaces from in vivo US scans. Proposed design utilizes fusion of feature maps extracted from multimodal images to abate sensitivity to variations caused by imaging artifacts and low intensity bone boundaries. In particular, our multimodal inputs consist of B-mode US images and their corresponding local phase filtered counterparts. Validation studies performed on 261 in vivo US scans obtained from 10 subjects achieved a mean localization accuracy of 0.1 mm with an F-score of 97%. Comparison against state-of-the-art CNN networks show an improvement of 89% in bone surface localization accuracy.