Aims

The Chopart joint complex is a joint between the midfoot and hindfoot. The static and dynamic support system of the joint is critical for maintaining the medial longitudinal arch of the foot. Any dysfunction leads to progressive collapsing flatfoot deformity (PCFD). Often, the tibialis posterior is the primary cause; however, contrary views have also been expressed. The present investigation intends to explore the comprehensive anatomy of the support system of the Chopart joint complex to gain insight into the cause of PCFD.

Recognized anatomic variations that lead to patella instability include patella alta and trochlea dysplasia. Lateralization of the extensor mechanism relative to the trochlea is often considered to be a contributing factor; however, controversy remains as to the degree this contributes to instability and how this should be measured. As the tibial tuberosity-trochlear groove (TT-TG) is one of most common imaging measurements to assess lateralization of the extensor mechanism, it is important to understand its strengths and weaknesses. Care needs to be taken while interpreting the TT-TG value as it is affected by many factors. Medializing tibial tubercle osteotomy is sometimes used to correct the TT-TG, but may not truly address the underlying anatomical problem. This review set out to determine whether the TT-TG distance sufficiently summarizes the pathoanatomy, and if this assists with planning of surgery in patellar instability. Cite this article: Bone Jt Open 2022;3(3):268–274

Aims

The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs?