Three dimensional printing as an aid for pre-operative planning in complex cases of total joint arthroplasty: A case series

Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.

Technology and orthopaedic surgeons

Modern and innovative technologies are rapidly penetrating the clinical practices of Orthopaedic Surgeons. The ones that have proved successful for clinical use are Additive Manufacturing/3D printing, Artificial Intelligence, Robotics, Smart sensors, and Orthobiologics. Industry 5.0 revolution has helped provide personalised treatment by integrating machines and human beings. In this special issue, we present a collection of excellent articles on these technologies.

Imaging in Hip Arthroplasty Management-Part 1: Templating: Past, Present and Future

Hip arthroplasty is a frequently used procedure with high success rates. Its main indications are primary or secondary advanced osteoarthritis, due to acute fracture, osteonecrosis of the femoral head, and hip dysplasia. The goals of HA are to reduce pain and restore normal hip biomechanics, allowing a return to the patient’s normal activities. To reach those goals, the size of implants must suit, and their positioning must meet, quality criteria, which can be determined by preoperative imaging. Moreover, mechanical complications can be influenced by implant size and position, and could be avoided by precise preoperative templating. Templating used to rely on standard radiographs, but recently the use of EOS^® imaging and CT has been growing, given the 3D approach provided by these methods. However, there is no consensus on the optimal imaging work-up, which may have an impact on the outcomes of the procedure. This article reviews the current principles of templating, the various imaging techniques used for it, as well as their advantages and drawbacks, and their expected results.