The diagnostic sub-categorization of cauda equina syndrome (CES) is used to aid communication between doctors and other healthcare professionals. It is also used to determine the need for, and urgency of, MRI and surgery in these patients. A recent paper by Hoeritzauer et al (2023) in this journal examined the interobserver reliability of the widely accepted subcategories in 100 patients with cauda equina syndrome. They found that there is no useful interobserver agreement for the subcategories, even for experienced spinal surgeons. This observation is supported by the largest prospective study of the treatment of cauda equina syndrome in the UK by Woodfield et al (2023). If the accepted subcategories are unreliable, they cannot be used in the way that they are currently, and they should be revised or abandoned. This paper presents a reassessment of the diagnostic and prognostic subcategories of cauda equina syndrome in the light of this evidence, with a suggested cure based on a more inclusive synthesis of symptoms, signs, bladder ultrasound scan results, and pre-intervention urinary catheterization. Cite this article: Bone Joint J 2024;106-B(3):227–231

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: Bone Joint J 2022;104-B(8):911–914.

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.

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines.

Cite this article: Bone Joint J 2021;103-B(12):1754–1758.

The posterior malleolus component of a fracture of the ankle is important, yet often overlooked. Pre-operative CT scans to identify and classify the pattern of the fracture are not used enough. Posterior malleolus fractures are not difficult to fix. After reduction and fixation of the posterior malleolus, the articular surface of the tibia is restored; the fibula is out to length; the syndesmosis is more stable and the patient can rehabilitate faster. There is therefore considerable merit in fixing most posterior malleolus fractures. An early post-operative CT scan to ensure that accurate reduction has been achieved should also be considered.

Cite this article: Bone Joint J 2017;99-B:1413–19.