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:
Paediatric bone sarcomas are a dual challenge for orthopaedic surgeons in terms of tumour resection and reconstruction, as it is important to minimize functional and growth problems without compromising survival rates. Cañadell’s technique consists of a Type I epiphysiolysis performed using continuous distraction by an external fixator prior to resection. It was designed to achieve a safe margin due to the ability of the physeal cartilage to be a barrier to tumour spread in some situations, avoiding the need for articular reconstruction, and preserving the growth capacity most of the times. Despite initial doubts raised in the scientific community, this technique is now widely used in many countries for the treatment of metaphyseal paediatric bone sarcomas. This annotation highlights the importance of Cañadell’s work and reviews the experience of applying it to bone sarcoma patients over the last 40 years. Cite this article:
Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’). Cite this article:
