Computer-assisted surgery and navigation in foot and ankle: state of the art and fields of application

Computer-assisted orthopaedic surgery (CAOS) is a real-time navigation guidance system that supports surgeons intraoperatively. Its use is reported to increase precision and facilitate less-invasive surgery. Advanced intraoperative imaging helps confirm that the initial aim of surgery has been achieved and allows for immediate adjustment when required. The complex anatomy of the foot and ankle, and the associated wide range of challenging procedures should benefit from the use of CAOS; however, reports on the topic are scarce. This article explores the fields of applications of real-time navigation and CAOS in foot and ankle surgery. Cite this article: EFORT Open Rev 2021;6:531-538. DOI: 10.1302/2058-5241.6.200024

Automatic Bone Surface Restoration for Markerless Computer-assisted Orthopaedic Surgery

An automatic markerless knee tracking and registration algorithm has been proposed in the literature to avoid the marker insertion required by conventional computer-assisted knee surgery, resulting in a shorter and less invasive surgical workflow. However, such an algorithm considers intact femur geometry only. The bone surface modification is inevitable due to intra-operative intervention. The mismatched correspondences will degrade the reliability of registered target pose. To solve this problem, this work proposed a supervised deep neural network to automatically restore the surface of processed bone. The network was trained on a synthetic dataset that consists of real depth captures of a model leg and simulated realistic femur cutting. According to the evaluation on both synthetic data and real-time captures, the registration quality can be effectively improved by surface reconstruction. The improvement in tracking accuracy is only evident over test data, indicating the need for future enhancement of the dataset and network.

Automatic Markerless Tracking for Computer-Assisted Orthopaedic Surgery

In computer assisted orthopaedic surgery, it is important to find the correct spatial lo- cation of the target in a predefined world coordinate, so that the model can be transformed accordingly onto the surgical site for surgeons’ reference. Current tracking systems mainly rely on the detection of optical markers inserted into the anatomy. The invasiveness of fixa- tion pins increases operating time and bone complications. Automatic markerless tracking is therefore preferred in practice. In this paper, we integrate an automatic RGBD-image based segmentation neural network and a fast markerless registration algorithm to achieve the markerless tracking purpose. An experimental test with a metal leg was designed. By forcing the alignment of the measured hip joint centre, the overall tracking was shown to be sub-degree in terms of orientation accuracy, which is clinically acceptable.

Towards Markerless 3D Pose Estimation for Computer Assisted Orthopaedic Surgery: a Comparison Study of Depth Cameras

This abstract addresses the problem of localizing surgical instruments during orthopaedic surgeries. Compared to usual approaches based on surgical navigation with markers, we propose here a novel method that estimates the 6-DoF pose of surgical instruments without specific markers using a depth camera. The goal of this paper is to compare, on real data, the registration precision of an algorithm called Point Pair Features (PPF) according to consumer depth cameras available on the market. Experimental validation using sawbones has been conducted and 8 cameras have been tested in realistic clinical environment. The Kinect Azure reports the best precision with a registration error of 1.13mm ± 1.00mm.

A Novel Computer-Assisted Workflow for Treatment of Osteochondral Lesions in the Knee

Computer-Assisted Orthopaedic Surgery (CAOS) is now becoming more prevalent, especially in knee arthroplasty. CAOS systems have the potential to improve the accuracy and repeatability of surgical procedures by means of digital preoperative planning and intraoperative tracking of the patient and surgical instruments.One area where the accuracy and repeatability of computer-assisted interventions could prove especially beneficial is the treatment of osteochondral defects (OCD). OCDs represent a common problem in the patient population, and are often a cause of pain and discomfort. The use of synthetic implants is a valid option for patients who cannot be treated with regenerative methods, but the outcome can be negatively impacted by incorrect positioning of the implant and lack of congruency with the surrounding anatomy.In this paper, we present a novel computer-assisted surgical workflow for the treatment of osteochondral defects. The software we developed automatically selects the implant that most closely matches the patient’s anatomy and computes the best pose. By combining this software with the existing capabilities of the Navio™ surgical system (Smith & Nephew inc.), we were able to create a complete workflow that incorporates both surgical planning and assisted bone preparation.Our preliminary testing on plastic bone models was successful and demonstrated that the workflow can be used to select and position an appropriate implant for a given defect.

Projector-Based Augmented Reality System for Computer Assisted Orthopaedic Surgery

This paper presents a projector-based augmented reality (AR) system for Computer- Assisted Orthopaedic Surgery (CAOS). After calibration, our AR system allows for projection of not only the virtual model directly on the surface of the target organ to create an augmented reality but also important clinical information such as distance and angular deviations from a surgical plan, which are important for various computer-assisted surgical procedures such as trajectory drilling and fracture reduction. The feasibility and accuracy of the system is experimentally validated on a 3D printed phantom model with pyramid shape, a dry goat bone and an in vitro pig leg. An average projection distance error of 1.03±0.58mm and an average drill alignment error of 1.17±0.43°were found. The results demonstrate the efficacy of the proposed AR system.