Anterior cruciate ligament (ACL) graft failure from rupture, attenuation, or malposition may cause recurrent subjective instability and objective laxity, and occurs in 3% to 22% of ACL reconstruction (ACLr) procedures. Revision ACLr is often indicated to restore knee stability, improve knee function, and facilitate return to cutting and pivoting activities. Prior to reconstruction, a thorough clinical and diagnostic evaluation is required to identify factors that may have predisposed an individual to recurrent ACL injury, appreciate concurrent intra-articular pathology, and select the optimal graft for revision reconstruction. Single-stage revision can be successful, although a staged approach may be used when optimal tunnel placement is not possible due to the position and/or widening of previous tunnels. Revision ACLr often involves concomitant procedures such as meniscal/chondral treatment, lateral extra-articular augmentation, and/or osteotomy. Although revision ACLr reliably restores knee stability and function, clinical outcomes and reoperation rates are worse than for primary ACLr.

Cite this article: Bone Joint J 2023;105-B(5):474–480.

Telehealth has the potential to change the way we approach patient care. From virtual consenting to reducing carbon emissions, costs, and waiting times, it is a powerful tool in our clinical armamentarium. There is mounting evidence that remote diagnostic evaluation and decision-making have reached an acceptable level of accuracy and can safely be adopted in orthopaedic surgery. Furthermore, patients’ and surgeons’ satisfaction with virtual appointments are comparable to in-person consultations. Challenges to the widespread use of telehealth should, however, be acknowledged and include the cost of installation, training, maintenance, and accessibility. It is also vital that clinicians are conscious of the medicolegal and ethical considerations surrounding the medium and adhere strictly to the relevant data protection legislation and storage framework. It remains to be seen how organizations harness the full spectrum of the technology to facilitate effective patient care.

Cite this article: Bone Joint J 2023;105-B(8):843–849.

We aim to explore the potential technologies for monitoring and assessment of patients undergoing arthroplasty by examining selected literature focusing on the technology currently available and reflecting on possible future development and application. The reviewed literature indicates a large variety of different hardware and software, widely available and used in a limited manner, to assess patients’ performance. There are extensive opportunities to enhance and integrate the systems which are already in existence to develop patient-specific pathways for rehabilitation.

Cite this article: Bone Joint J 2022;104-B(10):1104–1109.

Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.

In this review, we discuss the evidence for patients returning to sport after hip arthroplasty. This includes the choices regarding level of sporting activity and revision or complications, the type of implant, fixation and techniques of implantation, and how these choices relate to health economics. It is apparent that despite its success over six decades, hip arthroplasty has now evolved to accommodate and support ever-increasing patient demands and may therefore face new challenges.

Cite this article: Bone Joint J 2020;102-B(6):661–663.

This annotation briefly reviews the history of artificial intelligence and machine learning in health care and orthopaedics, and considers the role it will have in the future, particularly with reference to statistical analyses involving large datasets.

Cite this article: Bone Joint J 2019;101-B:1476–1478

The Jaipur foot was developed for barefoot amputees by Professor P. K. Sethi. He used local artisans and readily available materials. The prosthesis was cheap and could be made in one hour. It enabled amputees to work in rural conditions, muddy and wet fields and to climb trees. It has been widely used in India, South East Asia and Africa, where local variations to the design have now been made.