The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article:
Appropriate acetabular component placement has been proposed for prevention of postoperative dislocation in total hip arthroplasty (THA). Manual placements often cause outliers in spite of attempts to insert the component within the intended safe zone; therefore, some surgeons routinely evaluate intraoperative pelvic radiographs to exclude excessive acetabular component malposition. However, their evaluation is often ambiguous in case of the tilted or rotated pelvic position. The purpose of this study was to develop the computational analysis to digitalize the acetabular component orientation regardless of the pelvic tilt or rotation. Intraoperative pelvic radiographs of 50 patients who underwent THA were collected retrospectively. The 3D pelvic bone model and the acetabular component were image-matched to the intraoperative pelvic radiograph. The radiological anteversion (RA) and radiological inclination (RI) of the acetabular component were calculated and those measurement errors from the postoperative CT data were compared relative to those of the 2D measurements. In addition, the intra- and interobserver differences of the image-matching analysis were evaluated.Aims
Methods
We evaluated the accuracy of augmented reality (AR)-based navigation assistance through simulation of bone tumours in a pig femur model. We developed an AR-based navigation system for bone tumour resection, which could be used on a tablet PC. To simulate a bone tumour in the pig femur, a cortical window was made in the diaphysis and bone cement was inserted. A total of 133 pig femurs were used and tumour resection was simulated with AR-assisted resection (164 resection in 82 femurs, half by an orthropaedic oncology expert and half by an orthopaedic resident) and resection with the conventional method (82 resection in 41 femurs). In the conventional group, resection was performed after measuring the distance from the edge of the condyle to the expected resection margin with a ruler as per routine clinical practice.Objectives
Methods
We are entering a new era with governmental bodies
taking an increasingly guiding role, gaining control of registries,
demanding direct access with release of open public information
for quality comparisons between hospitals. This review is written
by physicians and scientists who have worked with the Swedish Knee
Arthroplasty Register (SKAR) periodically since it began. It reviews
the history of the register and describes the methods used and lessons
learned. Cite this article:
We performed A total of 12 cadaveric lower limbs were tested with a commercial
image-free navigation system using trackers secured by bone screws.
We then tested a non-invasive fabric-strap system. The lower limb
was secured at 10° intervals from 0° to 60° of knee flexion and
100 N of force was applied perpendicular to the tibia. Acceptable
coefficient of repeatability (CR) and limits of agreement (LOA)
of 3 mm were set based on diagnostic criteria for anterior cruciate
ligament (ACL) insufficiency.Objectives
Methods
