The development of a novel navigation system for reverse shoulder arthroplasty and its accuracy: a phantom and cadaveric study.

PURPOSE: Reverse shoulder arthroplasty has demonstrated excellent clinical efficacy for patients with shoulder joint diseases and is increasingly in demand. Traditional surgery faces challenges such as limited exposed surfaces and a narrow field of vision, leading to a shorter prosthesis lifespan and a higher risk of complications. In this study, an optical navigation system was proposed to assist surgeons in real-time tracking of the surgical scene. METHODS: Our optical navigation system was developed using the NDI Polaris Spectra device and several open-source platforms. The first step involved using the preoperative medical image to plan screw implantation paths. Real-time tracking of the patient phantom or cadaver and the surgical instrument was achieved through registration and calibration algorithms. Surgeons were guided on drilling through visualization methods. Postoperative results were compared with the planned implantation paths, and an algorithm was introduced to correct errors caused by the incorrect beginning points. RESULTS: Experiments involved three scapula cadavers and their corresponding phantoms with identical anatomy. For each experiment, three holes were completed with drills with diameters of 3.2 mm and 8.0 mm, respectively. Comparisons between the postoperative actual screw implantation paths and the preoperative planned implantation paths revealed an entry error of 1.05 ± 0.15 mm and an angle error of 2.47 ± 0.55° for phantom experiments. For cadaver experiments, the entry error was 1.53 ± 0.22 mm, and the angle error was 4.91 ± 0.78°. CONCLUSION: Our proposed optical navigation system successfully achieved real-time tracking of the surgical site, encompassing the patient phantom or cadaver and surgical instrument, thereby aiding surgeons in achieving precise surgical outcomes. Future study will explore the integration of robots to further enhance surgical efficiency and effectiveness.

A review of advances in image-guided orthopedic surgery.

Orthopedic surgery remains technically demanding due to the complex anatomical structures and cumbersome surgical procedures. The introduction of image-guided orthopedic surgery (IGOS) has significantly decreased the surgical risk and improved the operation results. This review focuses on the application of recent advances in artificial intelligence (AI), deep learning (DL), augmented reality (AR) and robotics in image-guided spine surgery, joint arthroplasty, fracture reduction and bone tumor resection. For the pre-operative stage, key technologies of AI and DL based medical image segmentation, 3D visualization and surgical planning procedures are systematically reviewed. For the intra-operative stage, the development of novel image registration, surgical tool calibration and real-time navigation are reviewed. Furthermore, the combination of the surgical navigation system with AR and robotic technology is also discussed. Finally, the current issues and prospects of the IGOS system are discussed, with the goal of establishing a reference and providing guidance for surgeons, engineers, and researchers involved in the research and development of this area.